Dr Fabien Heliot

A correction to a key figure in a previous paper by the authors, in the above paper [1] , is contained in this comment. Subsequent to the error, some further analysis is made of the tangential magnetic field close to the surface of the sphere model representing human tissue. Results explain how, counterintuitively, phase inversion between the antenna elements causes a more substantial specific absorption rate (SAR) in between them. On the other hand, co- phasing between the antenna elements causes a more substantial SAR at the antenna elements.

A rigorous analysis of the concept of coupling manipulation utilizing two antennas suited to modern smartphone devices in talk position for voice calls is presented. By using the optimum relative phase between the elements, they can substantially reduce the specific absorption rate (SAR) but still maintain efficiency due to the splitting of power between them and by exploiting a suitable level of interelement coupling. The same antenna elements can still be used for multiple-input-multiple-output (MIMO) when not in talk position without heavily degrading their fundamental capacity limit, but this is of secondary importance. The concept could be applied to frequency ranges used in mobile communications from 1.8 to 6 GHz where the ground plane has sufficient form factor. Extensive simulations using two planar inverted-F antennas (PIFAs) operating at 2.4 GHz are carried out to demonstrate conceptually how two antennas can be optimized to reduce SAR by over 50% compared to a single antenna element. SAR reduction is maintained regardless of the user's head composition and how they are handling the device in a talk position. Antenna prototypes are measured and compared to verify the capacity when the handset is used away from the body with two MIMO terminal antennas.

The dual objective of this paper is to first characterize the probability distribution of the random variable (RV), U , equivalent to the sum of multiple products of two independent circularly symmetric complex normal (CN) RVs, in order to then evaluate the fundamental limit of multi-input multi-output (MIMO)-reconfigurable intelligent surface (RIS) communication systems in terms of channel/signal-to-noise ratio (SNR) gain. First, we derive simple closed-form expressions of the joint moment generating function and probability density function (pdf) of the real and imaginary parts of U , as well as the pdf and mean value of |U |, and validate their accuracy via numerical simulations. Second, we provide a rigorous analysis on how MIMO-RIS improves channel propagation by relying on our probabilistic characterization of U. More specifically, we obtain a simple upper bound of the MIMO-RIS capacity, expressed as a function of its channel gain, for a large number of RIS elements. We then derive closed-form expressions of this channel gain, as well as the MIMO-RIS to MIMO SNR gain, and validate their accuracy via numerical simulations. Our analysis leads to the following insights: 1) RIS improvement in channel propagation accounts for a " baseline " /intrinsic improvement due to the amplitude response of each RIS phase-shifter element and a directionality improvement due to the optimization of the RIS phase-shifters used for steering the incident radiation towards its intended direction. 2) The intrinsic improvement is larger than the directionality improvement, and the latter is only significant when the number of RIS elements is very large in comparison with the square root of the product between the number of transmit and receive antennas.

Reconfigurable Intelligent Surface (RIS) technology is designed to improve channel propagation and, in turn, the performance of wireless communication systems. The extent of the improvement heavily relies on the phase shift optimization, a.k.a. passive beamforming design, at the RIS. This paper proposes novel approaches for simplifying this process in large RIS-aided multiple-input multiple-output (MIMO), a.k.a. MIMO-RIS, systems. More specifically, we derive two novel simplified closed-form approximations (CFAs) of the MIMO-RIS capacity expression in the large number of elements/antennas asymptotic regime and identify various scenarios for which they tightly approximate the classic MIMO-RIS capacity expression. We then design two novel simplified RIS phase optimization algorithms for lossless and practical metasurfaces with reduced computational complexity. We also demonstrate that, in certain practical scenarios, very simple guidelines can be used for setting the phases of MIMO-RIS systems. Numerical evaluations validate the accuracy of our CFAs and simplified RIS phase-shift optimization algorithms. They also show that our simplified approaches can deliver either similar or even better capacity performance than existing methods, especially for practical metasurfaces, in various scenarios.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for enhancing the performance of wireless communication systems. However, the extent of this enhancement has yet to be defined in a simple and insightful manner, especially when RIS amplitude and phase responses are coupled. In this paper, we characterize the fundamental ergodic capacity limits of RIS-aided multiple-input multiple-output (MIMO), a.k.a. MIMO-RIS, when considering a practical amplitude response for the RIS, which is coupled to its phase shift response. By studying these fundamental limits, we provide insights into the performance of MIMO-RIS systems and inform the design and optimization of future wireless communications. Accordingly, we first derive a novel expression of MIMO-RIS ergodic capacity from a closed-form expression of the probability density function (pdf) of the cascaded channel eigenvalues. We then provide upper and lower bounds, alongside low SNR, high SNR, and large number of RIS element approximations to illustrate the dependence of the MIMO-RIS ergodic capacity on the amplitude and phase of RIS elements. These expressions helped us to define the maximum SNR gain of MIMO-RIS over MIMO systems. Next, simulations are used to validate the accuracy and correctness of our various capacity expressions. Furthermore, we investigate the impact of environmental factors, such as near-field or far-field path loss, on the MIMO-RIS ergodic capacity. Numerical results confirm the accuracy of our MIMO-RIS SNR gain expression and provide valuable insights into the performance of RIS-based systems in realistic scenarios. Consequently, this can contribute to the design of future wireless communications based on MIMO-RIS.

Reconfigurable Intelligent Surfaces (RIS) can enhance system performance at the cost of increased complexity in multiuser MIMO systems. The beamforming options scale with the number of antennas at the base station/RIS. Existing methods for solving this problem tend to use computationally intensive iterative methods that are non-scalable for large RIS-aided MIMO systems. We propose here a novel self-supervised contrastive learning neural network (NN) architecture to optimize the sum spectral efficiency through joint active and passive beamforming design in multiuser RIS-aided MIMO systems. Our scheme utilizes contrastive learning to capture the channel features from augmented channel data and then can be trained to perform beamforming with only 1% of labeled data. The labels are derived through a closed-form optimization algorithm, lever-aging a sequential fractional programming approach. Leveraging the proposed self-supervised design helps to greatly reduce the computational complexity during the training phase. Moreover, our proposed model can operate under various noise levels by using data augmentation methods while maintaining a robust out-of-distribution performance under various propagation environments and different signal-to-noise ratios (SNR)s. During training, our proposed network only needs 10% of labeled data to converge when compared to supervised learning. Our trained NN can then achieve performance which is only 7% and 2.5% away from mathematical upper bound and fully supervised learning, respectively, with far less computational complexity.

In this paper, the mutual coupling from a multiple-input-multiple-output (MIMO) rim antenna has been utilized to control the level of specific absorption rate (SAR), when the mobile handset comes in close contact to the human body. The proposed antenna is capable of operating at 2.1 GHz and 4.3 GHz, respectively. A periodic defective ground structure (DGS) in conjunction with diodes and capacitors are used to manipulate the coupling between antenna elements. The working of the proposed dual band antenna design is validated using the characteristic mode analysis (CMA), and the current distribution. The MIMO performance is studied by using envelope correlation coefficient (ECC) and loss in capacity analysis. The effect of hand and LCD on the antenna performance is shown. The SAR analysis shows up to 30% reduction, in comparison to the baseline value of the SAR of the proposed antenna design.

Due to the rise of the energy efficiency (EE) as a system performance evaluation criterion, the EE-spectral efficiency (SE) trade-off is becoming a key tool for getting insight on how to efficiently design future communication system. As far as the single-input single-output (SISO) Rayleigh fading channel is concerned, the EE-SE trade-off has been accurately approximated in the past but only at low-SE. In this paper, we propose a novel and more generic closed-form approximation (CFA) of this EE-SE trade-off which is very accurate for any SE values. We compare our CFA with two existing CFAs and show the great accuracy of the former for a wider range of SE in comparison with the latter. As an application, we use our CFA to study the variation of EE-SE trade-off when a realistic power model is assumed and to compare the energy consumption of SISO against a 2x2 multi-input multi-output (MIMO) system over the Rayleigh fading channel.

In the 5G era, the densification of wireless infrastructure to fulfill ever‐increasing quality of service (QoS) needs, as well as the ever‐increasing number of wireless devices, may result in increased levels of electromagnetic field (EMF) exposure in the environment. The potential long‐term health impacts of EMF radiation are currently being debated and deserve consideration. As a result, we propose in this chapter a novel EMF‐aware resource allocation strategy based on power domain non‐orthogonal multiple access (PD‐NOMA) and machine learning (ML) technologies for lowering EMF exposure in cellular system uplinks. We employ the K‐means strategy (an unsupervised ML approach) to construct clusters of users to be allocated together, and then strategically organize and assign them on the subcarriers depending on their related channel attributes. Finding the optimal number of clusters in the PD‐NOMA environment is a critical challenge, and we utilized the elbow approach in conjunction with the F ‐test method in this chapter to successfully manage the maximum number of users to be given at the same time per subcarrier. We have also derived an EMF‐aware power allocation by formulating and solving a convex optimization problem. Based on the simulation findings, our suggested ML‐based solution successfully minimizes EMF exposure when compared with current techniques.

On the one hand, the number of wireless personal devices (WPDs) owned by individuals has soared in the last five years. On the other hand, WPDs exposed their users to electromagnetic field (EMF) radiation that has been linked with possible adverse physiological effects. In this paper, we first provide a generic analytical framework for modelling the exposure generated by WPDs having two transmit antennas. Our model is based on reliable exposure data for different types of human dielectric properties; its accuracy is showcased via simulations. We then integrate this model in an optimization framework for minimizing the exposure, while meeting spectral efficiency (SE) requirements, by means of beamforming. Results show the existence of a trade-off between the specific absorption rate (SAR) and SE, such that our exposure minimization beamforming approach can reduce the exposure by at least 27% by trading-off only 1% of the SE when compared to the optimal SE-based beamforming approach. In addition, they also indicate that increasing the number of antennas at the receiver side can help to further reduce the exposure generated by WPDs.

Mutual coupling between multiple antennas is used in this chapter to lower the amount of specific absorption rate (SAR) when a mobile device is in close proximity to the human body. The recommended antenna works at 4.3 and 2.1 GHz. A periodic version of defective ground structure (DGS) is used in conjunction with capacitors and diodes to change the coupling between antenna components. The functioning of the presented antenna design is confirmed using current distribution and characteristic mode analysis (CMA). The performance of multiple‐input, multiple‐output (MIMO) is investigated using the capacity loss and envelope correlation coefficient (ECC) analysis. Antenna performance is shown to be influenced by the LCD and the hand. When compared with the standard value of the proposed antenna arrangement, the SAR investigation showed a 30% reduction in SAR.

In this paper, we propose a novel closed-form approximation of the Energy Efficiency vs. Spectral Efficiency (EE-SE) trade-off for the uplink/downlink of distributed multipleinput multiple-output (DMIMO) system with two cooperating base stations. Our closed-form expression can be utilized for evaluating the idealistic and realistic EE-SE performances of various antenna configurations as well as assessing how DMIMO compares against MIMO system in terms of EE. Results show a tight match between our closed-form approximation and the Monte-Carlo simulation for both idealistic and realistic EESE trade-off. Our results also show that given a target SE requirement, there exists an optimal antenna setting that maximizes the EE. In addition, DMIMO scheme can offer significant improvement in terms of EE over the MIMO scheme

In this paper, a novel low-complexity and spectrally efficient modulation scheme for visible light communication (VLC) is proposed. Our new spatial quadrature modulation (SQM) is designed to efficiently adapt traditional complex modulation schemes to VLC; i.e. converting multi-level quadrature amplitude modulation (M-QAM), to real-unipolar symbols, making it suitable for transmission over light intensity. The proposed SQM relies on the spatial domain to convey the orthogonality and polarity of the complex signals, rather than mapping bits to symbol as in existing spatial modulation (SM) schemes. The detailed symbol error analysis of SQM is derived and the derivation is validated with link level simulation results. Using simulation and derived results, we also provide a performance comparison between the proposed SQM and SM. Simulation results demonstrate that SQM could achieve a better symbol error rate (SER) and/or data rate performance compared to the state of the art in SM; for instance a Eb/No gain of at least 5 dB at a SER of 10 4.

Energy savings are becoming a global trend, hence the importance of energy efficiency (EE) as an alternative performance evaluation metric. This paper proposes an EE based resource allocation method for the broadcast channel (BC), where a linear power model is used to characterize the power consumed at the base station (BS). Having formulated our EE based optimization problem and objective function, we utilize standard convex optimization techniques to show the concavity of the latter, and thus, the existence of a unique globally optimal energy-efficient rate and power allocation. Our EE based resource allocation framework is also extended to incorporate fairness, and provide a minimum user satisfaction in terms of spectral efficiency (SE). We then derive the generic equation of the EE contours and use them to get insights about the EE-SE trade-off over the BC. The performances of the aforementioned resource allocation schemes are compared for different metrics against the number of users and cell radius. Results indicate that the highest EE improvement is achieved by using the unconstrained optimization scheme, which is obtained by significantly reducing the total transmit power. Moreover, the network EE is shown to increase with the number of users and decrease as the cell radius increases.

It is well-established that transmitting at full power is the most spectral-efficient power allocation strategy for pointto- point (P2P) multi-input multi-output (MIMO) systems, however, can this strategy be energy efficient as well? In this letter, we address the most energy-efficient power allocation policy for symmetric P2P MIMO systems by accurately approximating in closed-form their optimal transmit power when a realistic MIMO power consumption model is considered. In most cases, being energy efficient implies a reduction in transmit and overall consumed powers at the expense of a lower spectral efficiency.

Drone networks offer rapid network deployment to areas that can pose access difficulty. This paper investigates the deployment of multi-hop drone-based unmanned aerial vehicles networks with a focus on the self-organization aspect. When rescue drones carry out their rescue operations which may fly faraway from the gateway, relay drones are autonomously deployed to maintain connectivity. We study the multiple dedicated connections where each rescue drone is connected to the gateway via dedicated relay drones. We show that this approach lacks sharing of relay drones and thus requires more relay drones. We then propose a centralized greedy algorithm and a distributed solution to significantly reduce the number of relay drones. We show that while the distributed self-organized drones (DSOD) solution requires a slightly higher number of relay drones than the greedy algorithm, it eliminates the need for global message exchange which makes it attractive for practical use.

The mutual information (MI) of multiple-input multiple-output (MIMO) system over Rayleigh fading channel is known to asymptotically follow a normal probability distribution. In this paper, we first prove that the MI of distributed MIMO (DMIMO) system is also asymptotically equivalent to a Gaussian random variable (RV) by deriving its moment generating function (MGF) and by showing its equivalence with the MGF of a Gaussian RV. We then derive an accurate closed-form approximation of the outage probability for DMIMO system by using the mean and variance of the MI and show the uniqueness of its formulation. Finally, several applications for our analysis are presented.

Future communication networks promise to provide ubiquitous high-speed services for numerous users via densely deployed small cells. They should offer good user experiences to all the users while incurring a low operational cost to the operators. User scheduling is a well-known approach to deliver good user experience, and recent works further demonstrate that it is also beneficial to improve energy efficiency (EE). However, existing EE-based scheduling schemes tend to favor users with good channel condition which lead to unfair user experiences. In this paper, we introduce a new concept of resource allocation boundary where EE and user fairness can be addressed simultaneously. We derive the boundary that partition in an effective manner the users into different groups. By applying an appropriate scheduling strategy to each group of users, not only users with poorer channel conditions can be served fairly, but the EE of the system can be further improved. We also provide a low-complexity energy-efficient power allocation algorithm that is designed to fully exploit the transmit power reduction capability of small cells. Simulation results show that our new scheduling scheme can improve the EE and user fairness when compared to existing approaches, i.e. by up to 63% and 56%, respectively.

In the next generation of communication networks (i.e. 6G), metamaterial-based antenna designs, such as Reconfigurable Intelligent Surface (RIS), will be critical for improving wireless communication systems. This paper investigates the ergodic capacity of RIS-aided multiple input multiple outputs (MIMO) systems in the presence of a direct link between the transmitter and receiver. We obtain an exact expression for the ergodic capacity of the cooperative MIMO-RIS systems (along with the corresponding probability density function of the cooperative MIMO-RIS channel) assuming that the receiver is capable of treating the RIS and direct link contributions in the received signal separately. Furthermore, we demonstrate that in the absence of this capability, the resulting formula is a tight upper bound that becomes increasingly tighter with greater numbers of RIS elements. In addition, we pose a simplified capacity expression for large numbers of RIS elements, which provides further insights into the behaviour of the cooperative MIMO-RIS capacity. To gain more insights, we also include a high SNR approximation. Our simulation results confirm the correctness of our expressions and illustrate how the SNR and the number of RIS elements impact the ergodic capacity.

This paper proposes a novel unipolar transceiver for visible light communication (VLC) by using orthogonal waveforms. The main advantage of our proposed scheme over most of the existing unipolar schemes in the literature is that the polarity of the real-valued orthogonal frequency division multiplexing (OFDM) sample determines the pulse shape of the continuous-time signal and thus, the unipolar conversion is performed directly in the analog instead of the digital domain. Therefore, our proposed scheme does not require any direct current (DC) biasing or clipping as it is the case with existing schemes in the literature. The bit error rate (BER) performance of our proposed scheme is analytically derived and its accuracy is verified by using Matlab simulations. Simulation results also substantiate the potential performance gains of our proposed scheme against the state-of-the-art OFDM-based systems in VLC; it indicates that the absence of DC shift and clipping in our scheme supports more reliable communication and outperforms the asymmetrically clipped optical-OFDM (ACO-OFDM), DC optical-OFDM (DCOOFDM) and unipolar-OFDM (U-OFDM) schemes. For instance, our scheme outperforms ACO-OFDM by at least 3 dB (in terms of signal to noise ratio) at a target BER of 10

Along with spectral efficiency (SE), energy efficiency (EE) is becoming one of the key performance evaluation criteria for communication system. These two criteria, which are conflicting, can be linked through their trade-off. The EE-SE trade-off for the multi-input multi-output (MIMO) Rayleigh fading channel has been accurately approximated in the past but only in the low-SE regime. In this paper, we propose a novel and more generic closed-form approximation of this trade-off which exhibits a greater accuracy for a wider range of SE values and antenna configurations. Our expression has been here utilized for assessing analytically the EE gain of MIMO over single-input single-output (SISO) system for two different types of power consumption models (PCMs): the theoretical PCM, where only the transmit power is considered as consumed power; and a more realistic PCM accounting for the fixed consumed power and amplifier inefficiency. Our analysis unfolds the large mismatch between theoretical and practical MIMO vs. SISO EE gains; the EE gain increases both with the SE and the number of antennas in theory, which indicates that MIMO is a promising EE enabler; whereas it remains small and decreases with the number of transmit antennas when a realistic PCM is considered. © 2012 IEEE.

In this paper, we derive a generic closed-form approximation (CFA) of the energy efficiency-spectral efficiency (EE-SE) trade-off for the uplink of coordinated multi-point (CoMP) system and demonstrate its accuracy for both idealistic and realistic power consumption models (PCMs). We utilize our CFA to compare CoMP against conventional non-cooperative system with orthogonal multiple access. In the idealistic PCM, CoMP is more energy efficient than non-cooperative system due to a reduction in power consumption; whereas in the realistic PCM, CoMP can also be more energy efficient but due to an improvement in SE and mainly for cell-edge communication and small cell deployment. © 2012 IEEE.

In this paper, the trade-off between energy efficiency (EE) and spectral efficiency (SE) is analyzed for both the uplink and downlink of the distributed multiple-input multiple-output (DMIMO) system over the Rayleigh fading channel while considering different types of power consumption models (PCMs). A novel tight closed-form approximation of the DMIMO EE-SE trade-off is presented and a detailed analysis is provided for the scenario with practical antenna configurations. Furthermore, generic and accurate low and high-SE approximations of this trade-off are derived for any number of radio access units (RAUs) in both the uplink and downlink channels. Our expressions have been utilized for assessing both the EE gain of DMIMO over co-located MIMO (CMIMO) and the incremental EE gain of DMIMO in the downlink channel. Our results reveal that DMIMO is more energy efficient than CMIMO for cell edge users in both the idealistic and realistic PCMs; whereas in terms of the incremental EE gain, connecting the user terminal to only one RAU is the most energy efficient approach when a realistic PCM is considered. © 1972-2012 IEEE.

Amplify-and-forward (AF) is one of the most popular and simple approaches for transmitting information over a cooperative multi-input multi-output (MIMO) relay channel. In cooperative communication, relays are employed for improving the coverage or enhancing the spectral efficiency, especially of cell-edge users. However, in a multi-cell context, the use of relays will also lead to an increase in the level of interference that is experienced by cell-edge users of neighboring cells. In this paper, two novel precoding schemes are proposed for mitigating this adverse effect of cooperative communication. They are designed by taking into account the effect of interference coming from neighboring cells, i.e. other cell-interference (OCI), in order to maximize the sum-rate of cell-edge users. Our novel OCI aware precoding schemes are compared against non OCI-aware techniques and results show the large performance gain in terms of sum-rate that our schemes can achieved, especially for large numbers of users and/or antennas in the multi-cell system.

Wireless communication technologies have transformed the way civilizations communicate information. To handle the ever‐increasing number of wireless users, the capacity of wireless communication networks is expected to increase 1,000 times. A portion of this capacity expansion will be made feasible by increasing the number of access points (APs), which will increase the number and kind of electromagnetic field (EMF) exposure sources in the environment. This chapter includes a thorough examination of the potential health risks associated with EMF exposure as well as the impact of this sort of exposure on the general public. This chapter also examines the potential effects of new wireless technologies on EMF exposure and suggests some unique research approaches for mitigating these effects in future wireless communication systems. The influence of mmWave or massive MIMO/beamforming on EMF exposure, for example, has yet to be thoroughly explored and included into the exposure evaluation framework.

This introductory chapter provides an overview of Electromagnetic Field (EMF) exposure from mobile systems. Electromagnetic (EM) radiation in the Radio-Frequency (RF) spectrum range is described, with established EMF exposure metrics as well as the recent EMF Exposure Index (EI), and public perceptions of EMF exposure are discussed. Finally, international EMF exposure guidelines and limits are presented.

The reflective elements on a reconfigurable intelligent surface (RIS) can be tuned to improve the propagation environment and, in turn, the system performance. However, RIS also brings challenges in terms of joint active and passive beamforming optimization. First, transmit beamforming has to be jointly designed with RIS to fully reap beamforming gain and signal-to-interference plus noise ratio (SINR) performance. Also, given that the number of beamforming options grows with the number of antennas/elements on the base station(BS)/RIS, it makes exhaustive search unpractical for multi-user (MU) multiple-input-multiple-output (MIMO) systems. In order to reduce the search overhead for joint BS and RIS beamforming optimization, we propose a novel joint active BS and passive RIS beamforming scheme based on a bespoke convolutional neural network (CNN) architecture. More specifically, we develop a fast converge and lightweight CNN with loss function designed based on the probability density function (PDF) of beams. By using this CNN, exhaustive search is only needed in data collection part to derive labels and no longer needed in prediction part. We also adopt a realistic blockage scenario to simulate non-stationary channels. Our proposed solution exhibits fast convergence speed, low neural network (NN) complexity and high prediction accuracy. Simulation results, based on benchmark dataset, show that our approach can significantly outperform comparable existing machine learning algorithms. It can achieve 70% shorter convergence time with around 91% beam prediction accuracy.

Energy efficiency (EE) is gradually becoming one of the key criteria, along with the spectral efficiency (SE), for evaluating communication system performances. However, minimizing the energy-per-bit consumption while maximizing the SE are conflicting objectives and, thus, the main criterion for designing efficient communication systems will become the trade-off between SE and EE. The EE-SE trade-off for the multi-input multi-output (MIMO) Rayleigh fading channel has been accurately approximated in the past but only in the low-SE regime. In this paper, we propose a novel and more generic closed-form approximation of this EE-SE trade-off which exhibits a greater accuracy for a wider range of SE values and antenna configurations. Our expression, which can easily be used for evaluating and comparing the EE-SE trade-off of MIMO communication systems, has been utilized in this paper for analyzing the impact of using multiple antennas on the EE and the EE gain of MIMO in comparison with single-input single-output (SISO) system. Our results indicate that EE can be improved predominantly through receive diversity in the very low-SE regime and that MIMO is far more energy efficient than SISO at high SE over the Rayleigh fading channel.

Energy efficiency (EE) is becoming an important performance indicator for ensuring both the economical and environmental sustainability of the next generation of communication networks. Equally, cooperative communication is an effective way of improving communication system performances. In this paper, we propose a near-optimal energy-efficient joint resource allocation algorithm for multi-hop multiple-input-multiple-output (MIMO) amplify-and-forward (AF) systems. We first show how to simplify the multivariate unconstrained EE-based problem, based on the fact that this problem has a unique optimal solution, and then solve it by means of a low-complexity algorithm. We compare our approach with classic optimization tools in terms of energy efficiency as well as complexity, and results indicate the near-optimality and low-complexity of our approach. As an application, we use our approach to compare the EE of multihop MIMO-AF with MIMO systems and our results show that the former outperforms the latter mainly when the direct link quality is poor.

The increasing demand for data and multimedia services, as well as the ubiquitous nature of the current generation of mobile devices have resulted in continuous network upgrades to support an ever-increasing number of users. Given that wireless communication systems rely on radiofrequency waves, the electromagnetic (EM) emissions from these systems are increasingly becoming a concern, especially in terms of adverse health effects. In order to address these concerns, we propose a novel resource allocation scheme for minimizing the EM emission of users in the uplink of multicell OFDM systems, while ensuring quality of service. Our scheme is based on the assumption that long-term channel state information of all the users in the network is available. A new multicell user grouping that uses the received interference powers of the users of different sectors is proposed. Furthermore, we propose two power allocation algorithms to minimize EM emission. The first power allocation algorithm performs multicell iterative optimization to obtain the transmit powers of each user in the system. On the other hand, our second power allocation algorithm uses the average channel gains of the users of different sectors to obtain an approximation of the transmit power of each user without multicell iterative optimization. As a result, this approach has a reduced complexity when compared to our first power allocation algorithm. Simulation results show that our scheme reduces EM emission by up to 70% when compared to a single cell EM emission aware scheme and by over 3 to 4 orders of magnitude when compared to spectral efficiency maximization schemes.

The densification of wireless infrastructure to meet ever-increasing quality of service (QoS) demands, and the ever-growing number of wireless devices may lead to higher levels of electromagnetic field (EMF) exposure in the environment, in the 5G era. The possible long term health effects related to the EMF radiation are still an open debate and requires attention. Therefore, in this paper, we propose a novel EMF-aware resource allocation scheme based on the power domain non-orthogonal multiple access (PD-NOMA) and machine learning (ML) technologies for reducing the EMF exposure in the uplink of cellular systems. More specifically, we use the K-means approach (an unsupervised ML approach) to create clusters of users to be allocated together and to then strategically group and assign them on the subcarriers, based on their associated channel properties. Finding the best number of clusters in the PD-NOMA environment is a key challenge, and in this paper, we have used the elbow method in conjunction with the F-test method to effectively control the maximum number of users to be allocated at the same time per subcarrier. We have also derived an EMF-aware power allocation by formulating and solving a convex optimization problem. Based on the simulation results, our proposed ML-based strategy effectively reduces the EMF exposure, in comparison with the state-of-the-art techniques.

In this paper we propose a tight closed-form approximation of the Energy Efficiency vs. Spectral Efficiency (EE-SE) trade-off for the uplink of a cellular communication system. We model the uplink of the cellular system by considering the Wyner model with Raleigh fading. We first demonstrate the accuracy of our expression by comparing it with Monte-Carlo simulation and the EE-SE trade-off expression based on lowpower approximation. Results show the great tightness of our expression with Monte-Carlo simulation. We utilize our closed-form for assessing the EE performance of base station (BS) cooperation against non-cooperative system for both a theoretical power model and a realistic power model. The theoretical power model includes only the transmit power, whereas the realistic power model incorporates the backhaul and signal processing powers in addition of the transmit power. Results indicate that BS cooperation is more energy efficient than non cooperative system and the former always outperforms the latter in terms of EE-SE trade-off. This is however no more the case with the realistic power model: the EE performance is then highly dependent on the number of cooperating BSs.

Meta-material-based antenna designs, such as large intelligent surface (LIS), are expected to be a game changer in future wireless cellular systems, since they provide a simple yet effective mean of drastically improving the wireless propagation environment. This paper investigates the ergodic capacity of LIS-aided multiple input multiple output (MIMO), a.k.a. MIMO-LIS, systems. To this end, the derivation of the probability density function (pdf) of the cascaded channel, i.e. the transmitter-to-LIS-to-receiver channel, is studied. Moreover, both high signal-to-noise ratio (SNR) asymptotic expression and closed-form approximations of this ergodic capacity are provided. Monte-Carlo simulations graphically validate the correctness and accuracy of our various expressions, for different antenna configurations. Furthermore, our performance analysis shows that the MIMO-LIS system outperforms both MIMO-AF and MIMO systems (by more than 60% and 15% respectively, at a 30 dB SNR) from an ergodic capacity point of view, which confirms that LIS can be beneficial for improving the propagation environment.

In this paper, we consider the precoding problem in the nonregenerative dual hop multi-input multi-output (MIMO) relay system. We propose a joint beamforming and power allocation method for a limited feedback system where a codebookbased beamformer is considered for each hop. The destination node selects the optimal beamforming codeword by relying on full channel knowledge of both source-relay and relay-destination channels for each hop from the code book and also compute the optimal power allocation coefficients for each substream. The index of the codewords and a quantized version of the power allocation coefficients are conveyed back to the source and relay nodes. The source and relay use the selected beamforming matrices and power allocation values to precode the data stream. In order to demonstrate the effectiveness of the proposed method, the performance of our joint beamforming and power allocation method is compared with previous codebook-based algorithms.

Energy efficiency (EE) is considered as a key enabler for the next generation of communication system. Equally, scheduling is an important aspect for efficient and reliable communication in multi-user system. In this paper, we propose a low-complexity green scheduling algorithm for the downlink of orthogonal frequency division multiple access (OFDMA) cellular system when considering that base station (BS) can coordinate their transmission. More specifically, our aim here is to design a practical, low-complexity and low-power consumption solution based on a realistic EE scheduling criterion, which takes into account the time dependence of the scheduling process. Numerical results indicate that our scheme reduces both the computational complexity (by a factor of at least 25) and transmit power (by at least 30%) while achieving similar EE performance than existing schemes, in a typical cellular environment. Moreover, they confirm the benefit of BS coordination for power and energy consumption reduction.

Along with spectral efficiency (SE), Energy efficiency (EE) is becoming one of the main performance evaluation criteria in communication. These two criteria, which are conflicting, can be linked through their trade-off. As far as MIMO is concerned, a closed-form approximation of the EE-SE trade-off has recently been proposed and it proved useful for analyzing the impact of using multiple antennas on the EE. In this paper, we use this closed-form approximation for assessing and comparing the EE gain of MIMO over SISO system when different power consumption models (PCMs) are considered at the transmitter. The EE of a communication system is closely related to its power consumption. In theory only the transmit power is considered as consumed power, whereas in a practical setting, the consumed power is the addition of two terms; the fixed consumed power, which accounts for cooling, processing, etc., and the variable consumed power, which varies as a function of the transmit power. Our analysis unveils the large mismatch between theoretical and practical EE gain of MIMO over SISO system; In theory, the EE gain increases both with the SE and the number of antennas, and, hence the potential of MIMO for EE improvement is very large in comparison with SISO; On the contrary, the EE gain is small and decreases as the number of transmit antennas increases when realistic PCMs are considered.

The spatially-incoherent radiators in visible light communication (VLC) constrain the optical carrier to be only driven by a real electrical sub-carrier, which cannot be quadrature modulated as in classic RF-based systems. This restriction, in turn, severely limits the transmission throughput of VLC systems. To overcome this technical challenge, we propose a novel coherent transmission scheme for VLC, in which the optical carrier is only treated as a purely amplitude-modulated carrier capable of transmitting two-dimensional (2D) symbols (e.g. quadrature modulated symbols). The ability of our new coherent transmission scheme to transmit 2D symbols is validated through analytical symbol error rate derivation and Matlab simulations. Results show that our scheme can improve both the spectral and energy efficiency of VLC systems, i.e. by either doubling the spectral efficiency or achieving more than 45% energy efficiency improvement, when compared to its existing counterparts.

With the anticipation of sixth-generation (6G) networks escalating, the integrated sensing and communication (ISAC) and millimeter-wave (mmWave) unmanned aerial vehicle (UAV) communications emerge as the key focus areas. This paper presents an innovative approach to ISAC waveform design tailored for mmWave UAV communications. The orthogonal chirp division multiplexing (OCDM), characterized by a brunch of orthogonal chirp signals, is firstly introduced in UAV scenarios to offer dual sensing and communication functionalities. We propose an holistic waveform design which incorporates OCDM with the state-of-the-art mmWave frequency-modulated continuous wave (FMCW) radar. Specifically, one subcarrier in OCDM is chosen as the dedicated sensing signal to facilitate the FMCW processing at the UAV receiver, while the rest of OCDM subcarriers can be used to enhance communication data rate. Such OCDM-FMCW scheme significantly reduces the required hardware complexity, particularly in analog-to-digital converter, which provides an energy-efficient ISAC solution for the resource-constraint UAVs. Simulation results demonstrate the effectiveness and superiority of the proposed scheme. It can surpass traditional methods like OFDM and OTFS, by trading off the sensing performance, communication performance, and hardware complexity.

The signaling scheme that is possible to be readily utilized in visible light communication (VLC) systems is unipolar incoherent signaling; i.e., intensity modulation/direct detection, whereas the conventional radio frequency transceivers (e.g., carrierless amplitude and phase modulation) require modifications that reduce their classical efficiency. Therefore, research efforts has been focused on adapting radio frequency methods to VLC. To this end, P-orthogonal transmission (OT) scheme, which could reduce the direct current element of the two-dimensional signal by relying on multi-waveform method at the transmitter side, was proposed. However, this method increases the computational complexity of the classical joint detector. Thus, the focus of this paper is to minimize the decoding complexity of the classical joint detector utilized in P-OT by proposing a new successive maximum likelihood detector in conjunction with P-OT that is less computationally demanding than the optimal joint detector by 62.5%.

Mobile communication systems rely on radiofrequency waves to operate. Given the popularity and ubiquity of mobile communication devices as well as network densification, the level of Electromagnetic Field (EMF) exposure to the public is expected to rise significantly over the next few years. Although there is no clear evidence linking short-term exposure to EMF emission from wireless communication systems with adverse health effects, the International Agency for Research on Cancer (IARC) has concluded that EMF radiation is possibly carcinogenic. To cope with the concerns of the general public, the European Environmental Agency has recommended non-technical precautionary approaches to minimize exposure to EMF emissions. Rather than relying on these non-technical approaches, EMF, latency, network resilience and connection density, alongside traditional criteria such as spectral efficiency and energy efficiency are expected to take centre stage in the development of 5G systems.This book focuses on innovative EMF exposure research for future generations of mobile and wireless communications. This timely publication highlights the novel work done on reducing EMF emissions in future mobile communication systems and how to develop smart integrated technical solutions.

Cooperative communication is an effective approach for increasing the spectral efficiency and/or the coverage of cellular networks as well as reducing the cost of network deployment. However, it remains to be seen how energy efficient it is. In this paper, we assess the energy efficiency of the conventional Amplify-and- forward (AF) scheme in an in-building relaying scenario. This scenario simplifies the mutual information formulation of the AF system and allows us to express its channel capacity with a simple and accurate closed-form approximation. In addition, a framework for the energy efficiency analysis of AF system is introduced, which includes a power consumption model and an energy efficiency metric, i.e. the bit-per-joule capacity. This framework along with our closed-form approximation are utilized for assessing both the channel and bit-per-joule capacities of the AF system in an in-building scenario. Our results indicate that transmitting with maximum power is not energy efficient and that AF system is more energy efficient than point-to-point communication at low transmit powers and signal-to-noise ratios.

Until recently, link adaptation and resource allocation for communication system relied extensively on the spectral efficiency as an optimization criterion. With the emergence of the energy efficiency (EE) as a key system design criterion, resource allocation based on EE is becoming of great interest. In this paper, we propose an optimal EE-based resource allocation method for the scalar broadcast channel (BC-S). We introduce our EE framework, which includes an EE metric as well as a realistic power consumption model for the base station, and utilize this framework for formulating our EE-based optimization problem subject to a power as well as fairness constraints. We then prove the convexity of this problem and compare our EE-based resource allocation method against two other methods, i.e. one based on sum-rate and one based on fairness optimization. Results indicate that our method provides large EE improvement in comparison with the two other methods by significantly reducing the total consumed power. Moreover, they show that near-optimal EE and average fairness can be simultaneously achieved over the BC-S channel. © 2012 IEEE.

Energy efficiency (EE) is growing in importance as a system design crite- rion for power-unlimited system such as cellular systems. Equally, resource allocation is a well-known method for improving the performance of the latter. In this paper, we propose two novel coordinated resource allocation strategies for jointly optimizing the resources of three sectors/cells in an energy-efficient manner in the downlink of multi-cell/sector systems. Given that this optimization problem is non-convex, it can only be optimally solved using high complexity exhaustive search. Here, we propose two practical approaches for allocating resources in a low complexity manner. We then compare our novel approaches against other existing non-coordinated and co- ordinated ones in order to highlight their benefit. Our results indicate that our first approach performs the best in terms of EE but with a low level of fairness in the user rate allocation; whereas our second approach provides a good trade-off between EE and fairness. Overall, base station selection, i.e. allowing only one sector to transmit at a time, is a very energy-efficient approach when the sleeping power is considered in the base station power model.

Base stations (BSs) rely on the massive multiple-input multiple-output (mMIMO) technology in the fifth generation of mobile networks (5G). A technology having a major impact on the nature of the electromagnetic field (EMF) exposure in such systems. This work has used a fully reconfigurable mMIMO testbed (operating at 2.63 GHz), capable of mimicking realistic 5G new radio (NR) BS beamforming performance, to first gather experimental-based evidence of 5G BS EMF exposure within a real-world outdoor environment, to then analyze its stochastic behaviour, and to finally understand its impact on the definition of exclusion boundaries for 5G BSs. The exposure data of our testbed have been complemented by exposure data collected from a typical commercial 5G BS (operating at 3.65 GHz) to confirm the result trends and findings of our analysis. A robust metrology has been followed to obtain all the EMF exposure data. Our data and analysis indicate that significant exposure variations can be noticed according to the beam directions, i.e. the relative position of the exposure measurement location to the beam directions as well as the environment, confirming the stochastic nature of 5G BS exposure. The variance of the exposure tends to decrease as the number of users increase for a constant traffic load. Whereas the exposure grows sub-linearly with the traffic load, regardless of the number of users. As far as the exclusion boundary of 5G BS is concerned, its revised definition based on 95-th percentile seems still not flexible enough to accommodate the deployment of 5G BS in countries/places with stringent EMF exposure limits, as for instance in Belgium.

Energy efficiency (EE) is becoming an important system design criterion to ensure that the next generation of communication networks is sustainable. Equally, cooperative communication and resource allocation are well-known techniques for improving the performance of communication systems. In this paper, we propose a low-complexity energy-efficient joint resource allocation method for the two-hop multiple-input-multiple-output (MIMO) amplify-and-forward (AF) system. We derive explicit formulations of the near-optimal energy-per-bit consumption, subchannels' power and rate for the unconstrained, total transmit power and sum-rate constrained EE optimization problems as well as detail how to solve these problems in a low-complexity manner. We then use our novel method for comparing the performances of two-hop MIMO-AF and MIMO systems in terms of EE. Our results indicate that the usage of a relay is only energy efficient when the quality of the direct link is very poor. We also show that the extra fixed power consumption induced by transmitting over two hops can seriously disadvantage MIMO-AF in terms of EE, but on the other hand, the usage of relay can be useful for downsizing the donor cell, which in turn provides EE gain. © 2002-2012 IEEE.

In this chapter, a detailed examination of the notion of coupling modification using two antennas appropriate to contemporary smartphone devices in talk position for voice conversations is presented. They can significantly lower the specific absorption rate (SAR) while maintaining efficiency owing to power splitting and a reasonable amount of inter element coupling by adopting the optimal relative phase between the components. When the antenna components are not in the talk position, they may still be utilized for multiple‐input, multiple‐output (MIMO) without significantly decreasing their basic capacity limit, but this is secondary. Where the ground plane has an appropriate form factor, the idea might be used to frequency ranges utilized in mobile communications ranging from 1.8 to 6 GHz. Extensive simulations are performed utilizing two planar inverted‐F antennas (PIFAs) operating at 2.4 GHz to illustrate conceptually how two antennas may be adjusted to lower SAR by more than 50% when compared with a single antenna element. No matter how the user holds the device in the talk position or the user's head structure, the SAR reduction is maintained. Antenna prototypes are measured and compared with validate capacity while utilizing two MIMO terminal antennas away from the body.

Energy efficiency (EE) is one of the main design criterion for the current and next generation of communication systems. Whereas, reflective intelligent surface (RIS) is foreseen to be a key enabler of the next generation of communication systems by facilitating the propagation of radio frequency signals, and in turn, possibly improving its spectral efficiency (SE) and/or EE. This paper investigates both the EE and SE of a multi-hop multi-antenna RIS-aided communication system through its fundamental trade-off. To this end, we first provide a generic and accurate closed-form approximation (CFA) of the SE (ergodic capacity) for multi-hop multi-antenna RIS-aided systems and verify its accuracy through simulations for various numbers of antennas/phase shifters and hops. Based on this expression, we then derive a novel and accurate CFA of the fundamental EE-SE trade-off for multi-hop multi-antenna RIS-aided systems. We subsequently use our CFA to analyse the variations of the EE as a function of the number of antennas/phase shifters and hops when considering a realistic power consumption model. It turns out that increasing the number of hops is more energy efficient than increasing the number of antennas/phase shifters and that multi-hop communication with RIS is not necessarily always more energy efficient than classic multi-antenna communication, as it is for instance the case in a simple device-to-device communication scenario.

Energy efficiency (EE) is a key design criterion for the next generation of communication systems. Equally, cooperative communication is known to be very effective for enhancing the performance of such systems. This paper proposes a breakthrough approach for maximizing the EE of multiple-inputmultiple- output (MIMO) relay-based nonregenerative cooperative communication systems by optimizing both the source and relay precoders when both relay and direct links are considered. We prove that the corresponding optimization problem is at least strictly pseudo-convex, i.e. having a unique solution, when the relay precoding matrix is known, and that its Lagrangian can be lower and upper bounded by strictly pseudo-convex functions when the source precoding matrix is known. Accordingly, we then derive EE-optimal source and relay precoding matrices that are jointly optimize through alternating optimization. We also provide a low-complexity alternative to the EE-optimal relay precoding matrix that exhibits close to optimal performance, but with a significantly reduced complexity. Simulations results show that our joint source and relay precoding optimization can improve the EE of MIMO-AF systems by up to 50% when compared to direct/relay link only precoding optimization.

The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.

Given the explosion in both the number of wireless devices and equipment radiating electromagnetic fields ( EMF ) and the growing public concern about it, accurate measurement of electromagnetic exposure and its application are expected to become increasingly important in future wireless communication systems. Indeed, the next generation of wireless networks seeks to provide customers with faster data rates, better quality of service ( QoS ), and reduced latency by increasing the number of access point s ( AP s), i.e. densification, which will increase EMF exposure. Similarly, the proliferation of future linked gadgets, such as the Internet of things ( IoT ) devices, may increase EMF exposure. This chapter provides a detailed assessment of existing methods for measuring EMF exposure in various circumstances, such as during data transmission uplink/downlink, and provides details on the metrics that are most typically used for evaluating EMF exposure in wireless communication. It also determines which metrics are most suited for reducing exposure.

Because of its simplicity, amplify-and-forward (AF) is one of the most popular cooperative relaying technique. Relays are used in cooperative communication to improve reliability, coverage or spectral efficiency of cell-edge users. However, relays tend to increase the interferences seen by users of adjacent cells, particularly by the cell-edge users, when used in multi-cell systems. In this paper, we propose a low-complexity precoding scheme to mitigate the effect of other-cell interference (OCI) in cooperative communication. The scheme is designed by taking into account the interference plus noise covariance matrix of each user for mitigating the interference at each receiver by means of precoding at the relay node. Simulation results show the effectiveness of the proposed scheme, both in terms of sum-rate and computational complexity, when compared to other existing OCI-aware precoding algorithms for AF. © 2012 IEEE.

Energy efficiency (EE) is fast becoming a key performance indicator for designing future wireless communication systems. Equally, precoding/power allocation has proved to be very effective for improving the spectral fficiency (SE) of multiuser (MU) multi-input multi-output (MIMO) communication systems. In a multi-cell environment, other-cell interference (OCI) degrades both the SE and EE performances of the system. We design here an energy efficient OCI-aware precoding/power allocation algorithm for the downlink of MU-MIMO systems by relying on regularized channel inversion and considering a realistic multi-antenna power consumption model. The performances of our proposed scheme are assessed both in presence and absence of OCI and results demonstrate the effectiveness of our approach for mitigating OCI. In addition, results show that our approach improves the EE of the system by saving transmit power in comparison with a traditional SE-based precoding/power allocation approach.

In this paper, the mutual coupling from a multiple-input-multiple-output (MIMO) rim antenna has been utilized to control the level of specific absorption rate (SAR), when the mobile handset comes in close contact to the human body. The proposed antenna is capable of operating at 2.1 GHz and 4.3 GHz, respectively. A periodic defective ground structure (DGS) in conjunction with diodes and capacitors are used to manipulate the coupling between antenna elements. The working of the proposed dual band antenna design is validated using the characteristic mode analysis (CMA), and the current distribution. The MIMO performance is studied by using envelope correlation coefficient (ECC) and loss in capacity analysis. The effect of hand and LCD on the antenna performance is shown. The SAR analysis shows up to 30% reduction, in comparison to the baseline value of the SAR of the proposed antenna design.

Metamaterial-based antenna designs, such as Reconfigurable Intelligent Surface (RIS), are expected to play a significant role in next generation communication networks (i.e. 6G) because of their ability to improve wireless communication environments. This letter investigates the ergodic capacity of RIS-aided multiple input multiple output (MIMO), a.k.a. MIMO-RIS, systems over Rayleigh-Rician fading channels. We consider that the transmitter-RIS and receiver-RIS links experience Rayleigh and Rician fading, respectively. An exact analytical expression of the ergodic capacity is derived based on closed-form expressions of the probability density function (pdf) of the cascaded channel. Moreover, a high SNR expression and a large RIS approximation are provided to unveil further system insights. Simulations result validate the correctness of our expressions and show the impact of the Rician fading and the number of RIS elements on the capacity.

Energy efficiency (EE) is undoubtedly an important criterion for designing power-limited systems, and yet in a context of global energy saving, its relevance for power-unlimited systems is steadily growing. Equally, resource allocation is a well-known method for improving the performance of cellular systems. In this paper, we propose an EE optimization framework for the downlink of planar cellular systems over frequency-selective channels. Relying on this framework, we design two novel low-complexity resource allocation algorithms for the single-cell and coordinated multi-cell scenarios, which are EE-optimal and EE-suboptimal, respectively. We then utilize our algorithms for comparing the EE performance of the classic non-coordinated, orthogonal and coordinated multi-cell approaches in realistic power and system settings. Our results show that coordination can be a simple and effective method for improving the EE of cellular systems, especially for medium to large cell sizes. Indeed, by using a coordinated rather than a non-coordinated resource allocation approach, the per-sector energy consumption and transmit power can be reduced by up to 15% and more than 90%, respectively.

It is well-established that transmitting at full power is the most spectral-efficient power allocation strategy for point-to-point (P2P) multi-input multi-output (MIMO) systems, however, can this strategy be energy efficient as well? In this letter, we address the most energy-efficient power allocation policy for symmetric P2P MIMO systems by accurately approximating in closed-form their optimal transmit power when a realistic MIMO power consumption model is considered. In most cases, being energy efficient implies a reduction in transmit and overall consumed powers at the expense of a lower spectral efficiency.

The impact of multi-antenna wireless personal devices (WPDs) on the electromagnetic field (EMF) exposure of users has yet to be properly understood at the system level. In this paper, we first explain how to model the exposure dose of multi-antenna WPD users in a multi-user multi-carrier communication system. This model is then used for minimising the exposure dose, when considering the quality of service (QoS) as well as transmit power requirements. In the process, we identify a new criterion, i.e. the ratio between the normalised exposure dose and the channel to noise ratio, as the main optimisation criterion for reducing the exposure dose of WPD users. Simulation results show that the usage of multi-antenna transmission can significantly reduce the exposure dose of WPD users in a multi-carrier system. An exposure reduction of at least 55% is achieved when a two-transmit WPD is used instead of a single antenna WPD, while ensuring QoS.

Energy efficiency (EE) is emerging as a key design criterion for both power limited applications, i.e. mobile devices, and power-unlimited applications, i.e. cellular network. Whereas, resource allocation is a well-known technique for improving the performance of communication system. In this paper, we design a simple and optimal EE-based resource allocation method for the orthogonal multi-user channel by adapting the transmit power and rate to the channel condition such that the energy-per-bit consumption is minimized. We present our EE framework, i.e. EE metric and node power consumption model, and utilizes it for formulating our EE-based optimization problem with or without constraint. In both cases, we derive explicit formulations of the optimal energy-per-bit consumption as well as optimal power and rate for each user. Our results indicate that EE-based allocation can substantially reduce the consumed power and increase the EE in comparison with spectral efficiency-based allocation.

© 2014 Springer International Publishing Switzerland. All rights are reserved. The increasing popularity of rich multimedia services has resulted in tremendous growth in demand for higher data rates in wireless communication systems. With the spectral performance of the wireless link is fast approaching the theoretical limit due to advances in cellular technologies, researchers have focused on innovative spectral and to support future wireless networks.

The accurate measurement of electromagnetic exposure and its application is expected to become more and more important in future wireless communication systems, given the explosion in both the number of wireless devices and equipments radiating electromagnetic-fields(EMF)and the growing concerns in the general public linked to it. Indeed, the next generation of wireless systems aims at providing a higher data rate,better quality of service(QoS), and lower latency to users by increasing the number of access points,i.e.densification, which in turn will increase EMF exposure. Similarly, the multiplication of future connected devices,e.g. internet of things(IoT)devices, will also contribute to an increase in EMF exposure. This paper provides a detailed survey relating to the potential health hazards linked with EMF exposure and the different metrics that are currently used for evaluating,limiting and mitigating the effects of this type of exposure on the general public. This paper also reviews the possible impacts of new wireless technologies on EMF exposure and proposes some novel research directions for updating the EMF exposure evaluation framework and addressing these impacts in future wireless communication systems. For instance, the impact of mmWave or massive-MIMO/beamforming on EMF exposure has yet to be fully understood and included in the exposure evaluation framework.

Beamforming and massive multiple-input-multiple-output (mMIMO) technologies are key features of base stations (BSs) in the fifth-generation (5G) of mobile networks. This technology is used to focus more radio frequency (RF) energy towards actively connected users to improve their connec-tion/performance, resulting in high variations in the radio frequency electromagnetic fields (RF-EMFs). This paper proposes a new methodology for modelling the RF-EMF exposure for 5G new radio (NR) mMIMO BS by means of a physics-informed machine learning (ML) approach using empirical measurement data. More precisely, the main focus of our work is to develop a suitable traceable RF-EMF exposure prediction tool in the context of 5G mMIMO BSs that can serve multiple mobile users (i.e. multiple-user MIMO (MU-MIMO)) within realistic real-world environments and scenarios. Our RF-EMF prediction tool relies on empirical measurement data acquired via a user-controllable mMIMO beamforming testbed and traceable RF-EMF measurement capability, where both indoor and outdoor RF-EMF measurement campaigns have been carried out. During the measurement campaigns various factors such as number of users, position of users and data duty cycles were considered. Using an ensemble of gradient boosted decision trees, we show that a physics-informed approach can improve predictive performance of RF-EMF compared with a purely data-driven approach, with the ability to extrapolate values of RF-EMF exposure to larger distances. Results show a coefficient of determination value of 0.86 on a 10-fold cross-validated experimental dataset. We also compare the sensitivity of RF-EMF exposure to various factors in the model, and show that model predictions become isotropic for large numbers of beam configurations, simplifying the exposure measurement methodology of 5G systems. INDEX TERMS 5G new radio (NR), beamforming, electromagnetic field (EMF) exposure, experimental measurements, machine learning, massive multiple-input-multiple-output (mMIMO).

Energy efficiency (EE) is a key enabler for the next generation of communication systems. Equally, resource allocation and cooperative communication are effective tech-niques for improving communication system performance. In this paper, we propose an optimal energy-efficient joint resource allocation method for the multi-hop multiple-input-multiple-output (MIMO) amplify-and-forward (AF) system. We define the joint source and multiple relays optimization problem and prove that its objective function, which is not generally quasiconvex, can be lower-bounded by a convex function. Moreover, all the minima of this objective function are strict minima. Based on these two properties, we then simplify the original multivariate optimization problem into a single variable problem and design a novel approach for optimally solving it in both the unconstraint and power constraint cases. In addition, we provide a sub-optimal approach with reduced complexity; the latter reduces the computational complexity by a factor of up to 40 with near-optimal performance. We finally utilize our novel approach for comparing the optimal energy-per-bit consumption of multi-hop MIMO-AF and MIMO systems; results indicate that MIMO-AF can help to save energy when the direct link quality is poor.

This paper proposes a low-complexity joint source and relay energy-efficient resource allocation scheme for the two-hop multiple-input- multiple-output (MIMO) amplify-and-forward (AF) system when channel state information is available. We first simplify the multivariate unconstrained energy efficiency (EE)-based problem and derive a convex closed-form approximation of its objective function as well as closed-form expressions of subchannel rates in both the unconstrained and power constraint cases. We then rely on these expressions for designing a low-complexity energy-efficient joint resource allocation algorithm. Our approach has been compared with a generic nonlinear constrained optimization solver and results have indicated the low-complexity and accuracy of our approach. As an application, we have also compared our EE-based approach against the optimal spectral efficiency (SE)-based joint resource allocation approach and results have shown that our EE-based approach provides a good trade-off between power consumption and SE. © 2014 IEEE.

Beside the well-established spectral-efficiency (SE), energy-efficiency (EE) is currently becoming an important performance evaluation metric, which in turn makes the EE-SE trade-off as a prominent criterion for efficiently designing future communication systems. In this letter, we propose a very tight closed-form approximation (CFA) of this trade-off over the single-input single-output (SISO) Rayleigh flat fading channel. We first derive an improved approximation of the SISO ergodic capacity by means of a parametric function and then utilize it for obtaining our novel EE-SE trade-off CFA, which is also generalized for the symmetric multi-input multi-output channel. We compare our CFA with existing CFAs and show its improved accuracy in comparison with the latter.

In this paper, we aim to obtain optimal space-time trellis codes and propose novel methods for reducing the highly growing full code search. We show that by exploiting the symmetry in the QAM and PSK constellations, the number combinations in the generator matrix of the encoder can be halved. We also show that for the same set of columns of the generator matrix, interchanging the columns give identical results hence reducing the full code search by the factorial of the number of transmit antennas. Using the suggested methods, we obtain novel space-time codes for slow Rayleigh fading environments and evaluate their performance by simulation, described by frame error probabilities. Furthermore, the performance of the obtained novel codes is evaluated in a multi-user code division multiple access (CDMA) system.

Energy efficiency is becoming an important feature for designing the next generation of communication networks, as are the multiplication of access points and the reduction of their coverage area. In this article we survey the latest development in energy-efficient scheduling, a.k.a. green scheduling, for both classic and heterogeneous cellular networks. We first introduce the main system model and framework that are considered in most of the existing green scheduling works. We then describe the main contributions on green scheduling as well as summarize their key findings. For instance, green scheduling schemes have demonstrated that they can significantly reduce transmit power and improve the energy efficiency of cellular systems. We also provide a performance analysis of some of the existing schemes in order to highlight some of the challenges that need to be addressed to make green scheduling more effective in heterogeneous networks. Indeed, the coordination between tiers and the rate fairness between the users of different tiers are important issues that have not yet been addressed. In addition, most existing designs exhibit a computational complexity that is too high for being deployed in a real system.

This chapter proposes a novel scheduling scheme for minimizing electromagnetic (EM) emission in the uplink of a multicell (MC) multiuser orthogonal frequency division multiple access (OFDMA) wireless communication system, while maintaining a specified quality of service (QoS) constraint.

Two schemes - offline and online - for minimizing the total EMF emission in the uplink of OFDMA systems have been proposed in this chapter. The offline EMF emission reduction scheme is based on the assumption that the network can predict the long-term CSI of all the users for allocating them on subcarriers. Then an optimal rate -based water -filling is performed to obtain the rate and power allocations of each allocated user on each subcarrier allocated to the user. On the other hand, the online EMF emission reduction scheme, which is based on short-term CSI knowledge, allocates power to users by minimizing the transmit energy per bit of each user. Simulation results show that the proposed offline scheme performs close to the optimal solution and that it significantly outperforms existing SE- and EE -based schemes, by up to 3 and 2 orders of magnitude, respectively. Accordingly, the proposed online scheme outperforms the SE- and EE -based schemes by up to 2.5 and 2 orders of magnitude, respectively. It has also shown that EMF emission of the offline scheme is inversely related to the transmission window, which makes it suitable for delay tolerant transmissions. Additionally, the offline scheme proves to be very robust against the effects of imperfect channel prediction.

Energy efficiency (EE) is growing in importance as a key performance indicator for designing the next generation of communication systems. Equally, resource allocation is an effective approach for improving the performance of communication systems. In this paper, we propose a low-complexity energyefficient resource allocation method for the orthogonal multiantenna multi-carrier channel. We derive explicit formulations of the optimal rate and energy-per-bit consumption for the per-antenna transmit power constrained and per-antenna rate constrained EE optimization problems as well as provide a lowcomplexity algorithm for optimally allocating resources over the orthogonal multi-antenna multi-carrier channel. We then compare our approach against a classic optimization tool in terms of energy efficiency as well as complexity, and results indicate the optimality and low-complexity of our approach. Comparing EE-optimal with spectral efficiency and power optimal allocation approaches over the orthogonal multi-antenna multi-carrier channel indicates that the former provides a good trade-off between power consumption and sum-rate performances.

In this paper, a massive multiple-input-multiple-output (mMIMO) testbed that is capable of mimicking realistic 5G new radio (NR) base station (BS) beamforming performance has been utilised to gather experimental-based evidence of 5G BS RF-EMF exposure within a real-world indoor environment. The mMIMO testbed has up to 128 RF channels with user-programmable software defined radio (SDR) capability. The stochastic nature of the 5G NR mMIMO system has been statistically assessed by evaluating the spatial variation of the RF-EMF exposure surrounding the mMIMO testbed when taking into account different beam profiles and data rates. Several other factors that influence the RF-EMF of mMIMO system have also being considered.

The reflective elements on an intelligent reconfigurable surface (IRS) can be tuned to improve the propagation environment and, in turn, the system performance, provided reliable channel information. However, IRS also brings challenges in terms of channel estimation by creating a very large dimensional channel between the IRS and the transmitter/receiver. A channel size being proportional to the number of users, user equipments (UE) antennas, IRS phase shifters, base station (BS) antennas, in an IRS-aided multi-user (MU) multiple-input-multiple-output (MIMO) system. Estimating such a large channel implies a huge amount of training overheads when all IRS phase shifters are passive, or requires extra power consumption and prohibitive hardware complexity, when IRS phase shifters are active. To tackle these challenges, this paper proposes a practical channel estimation process, based on a realistic codebook design constraint, for optimizing the IRS reflective elements in a MU-MIMO scenario. More specifically, we propose two novel machine-learning based algorithms (i.e. a deep supervised and a deep reinforcement) for optimizing the reflective elements of a typical passive IRS as well as a reliable channel estimation technique for IRS. Deep Supervised network uses exhaustive search to try every reflection pattern to train the network while the reinforcement network uses Q-learning to get the best reward. Our two algorithms can use the imperfect estimated channel knowledge to optimize the IRS in terms of sum-rate or minimum rate among all users. Simulation results show that our practical algorithms can achieve sum-rate and minimum rate performances close to the theoretical ones.

Coordinated multi-point (CoMP) architecture has proved to be very effective for improving the user fairness and spectral efficiency of cellular communication system, however, its energy efficiency remains to be evaluated. In this paper, CoMP system is idealized as a distributed antenna system by assuming perfect backhauling and cooperative processing. This simplified model allows us to express the capacity of the idealized CoMP system with a simple and accurate closed-form approximation. In addition, a framework for the energy efficiency analysis of CoMP system is introduced, which includes a power consumption model and an energy efficiency metric, i.e. bit-per-joule capacity. This framework along with our closed-form approximation are utilized for assessing both the channel and bit-per-joule capacities of the idealized CoMP system. Results indicate that multi-base-station cooperation can be energy efficient for cell-edge communication and that the backhauling and cooperative processing power should be kept low. Overall, it has been shown that the potential of improvement of CoMP in terms of bit-per-joule capacity is not as high as in terms of channel capacity due to associated energy cost for cooperative processing and backhauling.

Abstract—This article provides a survey and tutorial of electromagnetic (EM) radiation exposure and reduction in mobile communication systems. EM radiation exposure has received a fair share of interest in literature; however, this work is one of the first to compile the most interesting results and ideas related to EM exposure in mobile communication systems and present possible ways of reducing it. We provide a comprehensive survey of existing literature and also offer a tutorial on the dosimetry, metrics, international projects as well as guidelines and limits on the exposure from EM radiation in mobile communication systems. Based on this survey and given that EM radiation exposure is closely linked with specific absorption rate (SAR) and transmit power usage, we propose possible techniques for reducing EM radiation exposure in mobile communication systems by exploring known concepts related to SAR and transmit power reduction of mobile systems. Thus, this paper serves as an introductory guide for EM radiation exposure in mobile communication systems and provides insights towards the design of future low EM exposure mobile communication networks.

Energy efficiency (EE) is a key figure of merit for designing the next generation of communication systems. Meanwhile, relay-based cooperative communication, through machine-to-machine and other related technologies, is also playing an important part in the development of these systems. This paper designs an energy efficient precoding method for optimizing the EE/energy consumption of two-way multi-input multi-output (MIMO)-amplify-and-forward (AF) relay systems by using pseudo-convexity analysis to design EE-optimal precoding matrices. More precisely, we derive an EE-optimal source precoding matrix in closed-form, design a numerical approach for obtaining an optimal relay precoding matrix, prove the optimality of these matrices, when treated separately, and provide lowcomplexity bespoke algorithms to generate them. These matrices are then jointly optimized through an alternating optimization process that is proved to be systematically convergent. Performance evaluation indicates that our method can be globally optimal in some scenarios and that it is significantly more energy efficient (i.e. up to 60% more energy efficient) than existing EEbased one-way or two-way MIMO-AF precoding methods.

The increasing demand for data and multimedia services, as well as the ubiquitous nature of the current generation of mobile devices have resulted in continuous network upgrades to support an ever-increasing number of users. However, given that wireless communication systems operate on radiofrequency waves, the health effects of electromagnetic (EM) emission from these systems are increasingly becoming a concern. In order to address these concerns, we propose in this paper, an EM emission reduction scheme for the uplink of OFDM wireless systems with base station coordination. We formulate the EM reduction scheme as a convex optimization problem and solve it by iteratively allocating bits to users on their respective subcarriers in each sector. This is based on the assumption that the scheduler can predict the channel state information of all the users for a given transmission window. Simulation results show that, with coordination, our proposed scheme reduces EM emission by over 85% and 99% when compared with a no frequency reuse scheme and an energy efficiency based scheme, respectively.

The popularity and convergence of wireless communications have resulted in continuous network upgrades in order to support the increasing demand for bandwidth. However, given that wireless communication systems operate on radiofrequency waves, the health effects of electromagnetic emission from these systems are increasingly becoming a concern due to the ubiquity of mobile communication devices. In order to address these concerns, we propose two schemes (offline and online) for minimizing the EM emission of users in the uplink of OFDM systems, while maintaining an acceptable quality of service. We formulate our offline EM reduction scheme as a convex optimization problem and solve it through water-filling. This is based on the assumption that the long-term channel state information of all the users is known. Given that, in practice, long-term channel state information of all the users cannot always be available, we propose our online EM emission reduction scheme, which is based on minimizing the instantaneous transmit energy per bit of each user. Simulation results show that both our proposed schemes significantly minimize the EM emission when compared to the benchmark classic greedy spectral efficiency based scheme and an energy efficiency based scheme. Furthermore, our offline scheme proves to be very robust against channel prediction errors.

Current radiofrequency electromagnetic field (RF-EMF) exposure limits have become a critical concern for fifth-generation (5G) mobile network deployment. Regulation is not harmonized and in certain countries and regions it goes beyond the guidelines set out by the International Commission on Non-Ionizing Radiation Protection (ICNIRP). Using a massive multiple-input-multiple-output (mMIMO) testbed with beamforming capabilities that is capable of mimicking realistic 5G base station (BS) performance, this paper presents an experimental and statistical assessment of its associated RFEMF exposure within a real-world indoor environment. The mMIMO testbed has up to 128 channels with userprogrammable software defined radio (SDR) capability. It could perform zero-forcing precoding after channel state information (CSI) acquisition for different beamforming scenarios with respect to the associated user terminal antenna setups and positions. With 64 active mMIMO transmit antennas, 8 beamforming scenarios have been considered for single-user (SU) and multi-user (MU) downlink communications at different locations. Using a calibrated triaxial isotropic field-probe, the received channel power heat map for each beamforming scenario was acquired and then converted into an RF-EMF heat map. The relevant RF-EMF statistics was evaluated based on the variations of beam profiles and number of users.

Energy efficiency (EE) is gradually becoming one of the key criteria, along with the spectral efficiency (SE), for evaluating communication system performances. However, minimizing the EE while maximizing the SE are conflicting objectives and, thus, the main criterion for designing efficient communication systems will become the trade-off between SE and EE. The EE-SE trade-off for the multi-input multi-output (MIMO) Rayleigh fading channel has been accurately approximated in the past but only in the low-SE regime. In this paper, we propose a novel and more generic closed-form approximation of this EE-SE trade-off which exhibits a greater accuracy for a wider range of SE values and antenna configurations. Our expression, which can easily be used for evaluating and comparing the EE-SE trade-off of MIMO communication system, has been utilized in this paper for analyzing the impact of using multiple antennas on the EE and the EE gain of MIMO in comparison with single-input single-output (SISO) system. Our results indicate that EE can be improved predominantly through receive diversity in the low-SE regime and that MIMO is far more energy efficient than SISO at high SE over the Rayleigh fading channel.

Energy efficiency (EE) has become a critical metric for the next generation mobile networks. Scheduling plays a key role for offering not only spectral efficient but also energy efficient operation in mobile networks. In this paper, we address the problem of EE scheduling for the downlink of a two-tier Heterogeneous Network (HetNet) with orthogonal frequency division multiple access (OFDMA). Contrary to the existing contributions on this research topic, we propose a coordinated green scheduling scheme that maximizes EE for the entire HetNet rather than a particular tier. Moreover, our novel scheduling scheme uses a more realistic EE criterion where the time dependence of the scheduling process is taken into account. Numerical results are presented showing the competitive EE performance of our proposed scheduling scheme with improved user fairness and reduced complexity compared with existing non-HetNet schemes. In dense small cell situation, our scheme reduces the scheduling processing time as much as 25 times.

Chapter Contents: 21.1 Motivation for 5G RF-exposure new metrology and guidelines 21.2 Measuring 5G RF-exposure 21.3 Experimental assessment of the RF-exposure of massive MIMO base station via a reconfigurable testbed 21.3.1 mMIMO testbed set-up 21.3.2 Calibration of the mMIMO testbed equipment 21.3.3 Experimental set-up and test scenarios 21.3.4 First experiment 21.3.5 Second experiment 21.4 Summary References

—From the fourth generation (4G) of cellular systems onward, wireless personal devices (WPDs) support multi-input multi-output (MIMO) communication. However, the impact of MIMO communication on the electromagnetic field (EMF) of WPD users has yet to be fully understood and analyzed at the system level. In this paper, we first provide a generic model for assessing the individual exposure dose of multi-antenna WPD users in a multiuser multi-carrier communication system. An optimization framework for minimizing this exposure dose is then developed based on our exposure model. This framework helps us to identify a new criterion, i.e., the ratio between the normalized exposure dose and the channel to noise ratio (CNR), as the main system level criterion for minimizing the individual exposure dose of multi-antenna WPD users. This criterion is further integrated in the design of two novel centralized resource allocation schemes that take advantage of the multiple antennas at the WPD to minimize the per-user exposure dose, when full or limited knowledge of each user channel is available. Our new schemes can significantly reduce the individual exposure dose of WPD users (by approximately 80%) in comparison with the most relevant existing resource allocation schemes. Our results also provide insights into the logarithmic relationship between the per-user exposure dose and the number of receive antennas (or the number of time slots), and how such a parameter can be exploited to further reduce the exposure and/or provide a higher SE while maintaining a low exposure dose. Index Terms—EMF exposure dose, MIMO, Multi-carrier system , SAR, optimization.

Energy efficiency is becoming an important feature for designing the next generation of communication networks, as are the multiplication of access points and the reduction of their coverage area. In this article we survey the latest development in energy-efficient scheduling, a.k.a. green scheduling, for both classic and heterogeneous cellular networks. We first introduce the main system model and framework that are considered in most of the existing green scheduling works. We then describe the main contributions on green scheduling as well as summarize their key findings. For instance, green scheduling schemes have demonstrated that they can significantly reduce transmit power and improve the energy efficiency of cellular systems. We also provide a performance analysis of some of the existing schemes in order to highlight some of the challenges that need to be addressed to make green scheduling more effective in heterogeneous networks. Indeed, the coordination between tiers and the rate fairness between the users of different tiers are important issues that have not yet been addressed. In addition, most existing designs exhibit a computational complexity that is too high for being deployed in a real system.

Coordination between two or more multiple access channel (MAC) receivers can enlarge the achievable rate region of the whole system. This paper focuses on coordination by sharing the codebooks of the users between the receivers of MACs. We first define the achievable rate region of the time invariant multiple coordinated MAC (MCMAC) and subsequently derive its achievable rate region. We later express the achievable rate region in terms of the dominating points. We base our numerical analysis on the two-user two-receiver Gaussian coordinated MAC and make comparison with the interference channel, full cooperation and the individual MAC performance analysis. It is observed that this approach though suboptimal is less complex in comparison with full cooperation and that the MCMAC rate region is at least equal to the rate region of the uncoordinated approach. Over several channel states, the rate region of MCMAC exceeds that of the uncoordinated approach.

In this paper, we propose a tight closed-form approximation of the Energy Efficiency vs. Spectral Efficiency (EE-SE) trade-off for the uplink of a linear cellular communication system with base station cooperation and uniformly distributed user terminals. We utilize the doubly-regular property of the channel to obtain a closed form approximation using the Marˇcenko Pasture law. We demonstrate the accuracy of our expression by comparing it with Monte-Carlo simulation and the EE-SE trade-off expression based on low-power approximation. Results show the great tightness of our expression with Monte-Carlo simulation.We utilize our closed form expression for assessing the EE performance of cooperation for both theoretical and realistic power models. The theoretical power model includes only the transmit power, whereas the realistic power model incorporates the backhaul and signal processing power in addition to the transmit power. Results indicate that for both power models, increasing the number of antennas leads to an improvement in EE performance, whereas, increasing the number of cooperating BSs results in a loss in EE when considering the realistic power model.