Dr Ahmed Elzanaty

—A specific limitation of spatial modulation (SM) is that the number of transmit antennas must be a power of two, otherwise it will cause a fractional bits problem. To solve this problem, this paper proposes a novel transmission scheme, called golden angle modulation aided fractional spatial modulation (GAM-FSM), which exploits the property of golden angle modulation (GAM), i.e., it can have an arbitrary number of constellation points or the modulation order of GAM can be any positive integer. In addition, the average bit error probability (ABEP) and mutual information (MI) of our proposed GAM-FSM scheme are derived. To further enhance the system performance , geometric and probabilistic constellation shaping aided GAM-FSM schemes are investigated and optimized under three optimization criteria, maximization of the minimum Euclidean distance (MMED), minimization of the the bit error rate (MBER) and maximization of the MI (MMI). Simulation results reveals the superiority of our proposed GAM-FSM over the conventional fractional spatial modulation (FSM) systems. Besides, simulation results also show that our proposed constellation shaping aided GAM-FSM schemes exhibit significant system performance improvements compared to the one without constellation shaping. Index Terms—MIMO, fractional spatial modulation (FSM), golden angle modulation (GAM), average bit error probability (ABEP), mutual information (MI), geometric and probabilistic constellation shaping.

In this paper, we consider an uplink transmission of a multiuser single-input multiple-output (SIMO) system assisted with multiple reconfigurable intelligent surfaces (RISs). We investigate the energy efficiency (EE) maximization problem with an electromagnetic field (EMF) exposure constraint. In order to solve the problem, we present a lower bound for the EE and adopt an alternate optimization problem. Then, we propose the Energy Efficient Multi-RIS (EEMR) algorithm to obtain the optimal transmit power of the users and phase shifts of the RISs. Moreover, we study this problem for a system with a central RIS and compare the results. The simulation results show that for a sufficient total number of RIS elements, the system with distributed RISs is more energy efficient compared to the system with a central RIS. In addition, for both the systems the EMF exposure constraints enforce a trade-off between the EE and EMF-awareness of the system.

Resource allocation has become one of the main challenges in the 5G network which is playing an important role to improve the quality of wireless networks. The design of optimal resource allocation (such as power allocation for the tradeoff between spectral and energy efficient) in wireless communication systems is generically classified as non-convex mixed-integer nonlinear programming (MINLP) and in general it is NP-hard problem, which is formulated as a functional optimization problem with nonlinear constraints. In this paper, in order to decrease the complexity of global optimization algorithm, meta-heuristic algorithms are used on large scales. One of the meta-heuristic algorithms is Grey Wolf Optimizer (GWO) which is used to trade with the issues regarding resource allocation of the 5G network are investigated. GWO is an alternative method to the traditional methods and it is efficient to solve a various optimization problem. This work targets the ability of GWO to address power allocation optimization problems in wireless communication systems. The penalty method is used to handle optimization constraints depending on the fundamental of GWO are investigated. In addition, the important relation between energy efficiency (EE) and spectral efficiency (SE) of power allocation is considered the one of the applications of GWO will be carried out.

The upcoming 6G technology is expected to operate in near-field (NF) radiating conditions thanks to high-frequency and electrically large antenna arrays. While several studies have already addressed this possibility, it is worth noting that NF models introduce heightened complexity, the justification for which is not always evident in terms of performance improvements. Therefore, this paper delves into the implications of the disparity between NF and far-field (FF) models concerning communication, localization, and sensing systems. Such disparity might lead to a degradation of performance metrics like localization accuracy, sensing reliability, and communication efficiency. Through an exploration of the effects arising from the mismatches between NF and FF models, this study seeks to illuminate the challenges confronting system designers and offer valuable insights into the balance between model accuracy, which typically requires a high complexity and achievable performance. To substantiate our perspective, we also incorporate a numerical performance assessment confirming the repercussions of the mismatch between NF and FF models.

In this chapter, we consider the design of localization algorithms for reconfigurable intelligent surface (RIS)-aided models under different practical channel model settings. More specifically, we utilize the compressed sensing (CS) to localize a user equipment (UE) direction and position in both far-field and near-field multipath environment, respectively; we extend our work by performing a super resolution localization using the atomic norm minimization for a user located in a single and path near-field channel. On the other hand, we propose RIS phase design that aims to minimize the localization error by maximizing the signal-to-noise ratio (SNR).

—Internet of things (IoT) services have grown to become an integral part of our everyday lives. However, the gap in IoT connectivity between rural and urban areas is growing, leading to what is called the digital divide problem. In this regard, we propose an architecture for IoT data collection in rural areas via mobile fog nodes. We study the effect of gateway mobility in LoRaWAN on the network communication flow and transmission parameters. The limits for reliable communication at different moving speeds are analytically computed, then validated by both numerical simulations and real experiments. The numerical results show that it is beneficial to use spreading factors (SF) lower than 11 for vehicle speeds up to 150 km/hr, with SF7 being the optimum in synchronized transmission.

Over the past few years, the prevalence of wireless devices has become one of the essential sources of electromagnetic (EM) radiation to the public. Facing with the swift development of wireless communications, people are skeptical about the risks of long-term exposure to EM radiation. As EM exposure is required to be restricted at user terminals, it is inefficient to blindly decrease the transmit power, which leads to limited spectral efficiency and energy efficiency (EE). Recently, rate-splitting multiple access (RSMA) has been proposed as an effective way to provide higher wireless transmission performance, which is a promising technology for future wireless communications. To this end, we propose using RSMA to increase the EE of massive MIMO uplink while limiting the EM exposure of users. In particularly, we investigate the optimization of the transmit covariance matrices and decoding order using statistical channel state information (CSI). The problem is formulated as non-convex mixed integer program, which is in general difficult to handle. We first propose a modified water-filling scheme to obtain the transmit covariance matrices with fixed decoding order. Then, a greedy approach is proposed to obtain the decoding permutation. Numerical results verify the effectiveness of the proposed EM exposure-aware EE maximization scheme for uplink RSMA.

Recent advances in Big Data Analytics are primarily driven by innovations in Artificial Intelligence and Machine Learning Methods. Due to the richness of data sources at the edge and with the increasing privacy concerns, Distributed privacy-preserving machine learning (ML) methods are increasingly becoming the norm for training ML models on federated big data. In a popular approach known as Federated learning (FL), service providers leverage end-user data to train ML models to improve services such as text auto-completion, virtual keyboards, and item recommendations. FL is expected to grow in importance with the increasing focus on big data, privacy and 5G/6G technologies. However, FL faces significant challenges such as heterogeneity, communication overheads, and privacy preservation. In practice, training models via FL is time-intensive and worse its dependent on client participation who may not always be available to join the training. Our empirical analysis shows that client availability can significantly impact the model quality which motivates the design of an availability-aware selection scheme. We propose A2FL to mitigate the quality degradation caused by the under-representation of the global client population by prioritizing the least available clients. Our results show that, compared to state-of-the-art methods, A2FL can improve the client diversity during the training and hence boost the trained model quality.

Sixth-generation cellular networks (6G) are expected to involve not only data communications but also sensing capabilities, enabling a wide range of applications. This paper proposes a novel Dual Functional Shared Aperture reconfigurable intelligent surface (RIS) design that enables the coexistence between radar sensing and millimeter wave (mm-wave) communication. Some RIS meta-elements integrate radio-frequency (RF)-feeds to support the transmission/reception of multiple-input multiple-output (MIMO) radar signals, permitting holographic beam focusing/forming in near/far fields without phase shifters. The designed Dual Functional RIS provides a synergy between communication and sensing modalities, particularly in scenarios where both far-field and near-field interactions play critical roles. Primarily, we emphasize far-field conditions and antenna-related aspects which serve as a foundational framework for future work considering that both communication and radar detection take place in the near field. We show adding an RF-feed to the meta-element incurs additional amplitude loss in the spectrum of interest. At the same time, increasing the number of radar antennas (i.e., elements with RF-feed) improves the radar angular accuracy.

Signal compression is essential for energy and bandwidth efficient communication and storage systems. In this paper, we provide two practical approaches for source compression of noisy sparse and non-strictly sparse (compressible) sources. The proposed schemes are based on channel coding theory to construct a source encoder that decreases the number of transmitted bits while preserving the fidelity of the reconstructed signal at the receiver by exploiting its sparsity. In addition, a model order selection scheme is proposed to detect the nonzero elements of sparse vectors embedded in noise, or to find a nonlinear sparse approximation of compressible signals. As illustrated by numerical results, our approach provides a lower distortion-rate function compared to previously known methods. For example, the proposed schemes achieve a lower distortion, about 2 orders of magnitude, compared to compressed sensing, for the same rate.

The deployment of 5G networks is sometimes questioned due to the impact of ElectroMagnetic Field (EMF) generated by Radio Base Station (RBS) on users. The goal of this work is to analyze such issue from a novel perspective, by comparing RBS EMF against exposure generated by 5G smartphones in commercial deployments. The measurement of exposure from 5G is hampered by several implementation aspects, such as dual connectivity between 4G and 5G, spectrum fragmentation, and carrier aggregation. To face such issues, we deploy a novel framework, called 5G-EA , tailored to the assessment of smartphone and RBS exposure through an innovative measurement algorithm, able to remotely control a programmable spectrum analyzer. Results, obtained in both outdoor and indoor locations, reveal that smartphone exposure (upon generation of uplink traffic) dominates over the RBS one. Moreover, Line-of-Sight locations experience a reduction of around one order of magnitude on the overall exposure compared to Non-Line-of-Sight ones. In addition, 5G exposure always represents a small share (up to 38%) compared to the total one radiated by the smartphone. This work was supported by the PLAN-EMF Project (KAUST-CNIT) under Award OSR-2020-CRG9-4377.

Installing more base stations (BSs) into the existing cellular infrastructure is an essential way to provide greater network capacity and higher data rates in the 5th-generation cellular networks (5G). However, a non-negligible amount of the population is concerned that such network densification will generate a notable increase in exposure to electric and magnetic fields (EMF) over the territory. In this paper, we analyze the downlink, uplink, and joint downlink&uplink exposure induced by the radiation from BSs and personal user equipment (UE), respectively, in terms of the received power density and exposure index. In our analysis, we consider the EMF restrictions set by the regulatory authorities such as the minimum distance between restricted areas (e.g., schools and hospitals) and BSs, and the maximum permitted exposure. Exploiting tools from stochastic geometry, mathematical expressions for the coverage probability and statistical EMF exposure are derived and validated. Tuning the system parameters such as the BS density and the minimum distance from a BS to restricted areas, we show a trade-off between reducing the population’s exposure to EMF and enhancing the network coverage performance. Then, we formulate optimization problems to maximize the performance of the EMF-aware cellular network while ensuring that the EMF exposure complies with the standard regulation limits with high probability. For instance, the exposure from BSs is two orders of magnitude less than the maximum permissible level when the density of BSs is less than 20 BSs/km^2.

We provide a probabilistic framework for the analysis of the restricted isometry constants (RICs) of finite dimensional Gaussian measurement matrices. The proposed method relies on the exact distribution of the extreme eigenvalues of Wishart matrices, or on its approximation based on the gamma distribution. In particular, we derive tight lower bounds on the cumulative distribution functions (CDFs) of the RICs. The presented framework provides the tightest lower bound on the maximum sparsity order, based on sufficient recovery conditions on the RICs, which allows signal reconstruction with a given target probability via different recovery algorithms.

The inadequate use of wireless spectrum resources has recently motivated researchers and practitioners to look for new ways to improve resource efficiency. As a result, new cognitive radio technologies have been proposed as an effective solution. The Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management examines the emerging technologies being used to overcome radio spectrum scarcity. Providing timely and comprehensive coverage on topics pertaining to channel estimation, spectrum sensing, communication security, frequency hopping, and smart antennas, this research work is essential for use by educators, industrialists, and graduate students, as well as academicians researching in the field. The inadequate use of wireless spectrum resources has recently motivated researchers and practitioners to look for new ways to improve resource efficiency. As a result, new cognitive radio technologies have been proposed as an effective solution. The Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management examines the emerging technologies being used to overcome radio spectrum scarcity. Providing timely and comprehensive coverage on topics pertaining to channel estimation, spectrum sensing, communication security, frequency hopping, and smart antennas, this research work is essential for use by educators, industrialists, and graduate students, as well as academicians researching in the field.

In this letter, we investigate the signal-to-interference-plus-noise-ratio (SINR) maximization problem in a multi-user massive multiple-input-multiple-output (massive MIMO) system enabled with multiple reconfigurable intelligent surfaces (RISs). We examine two zero-forcing (ZF) beamforming approaches for interference management namely BS-UE-ZF and BS-RIS-ZF that enforce the interference to zero at the users (UEs) and the RISs, respectively. Then, for each case, we resolve the SINR maximization problem to find the optimal phase shifts of the elements of the RISs. Also, we evaluate the asymptotic expressions for the optimal phase shifts and the maximum SINRs when the number of the base station (BS) antennas tends to infinity. We show that if the channels of the RIS elements are independent and the number of the BS antennas tends to infinity, random phase shifts achieve the maximum SINR using the BS-UE-ZF beamforming approach. The simulation results illustrate that by employing the BS-RIS-ZF beamforming approach, the asymptotic expressions of the phase shifts and maximum SINRs achieve the rate obtained by the optimal phase shifts even for a small number of the BS antennas.

The fifth-generation cellular network requires dense installation of radio base stations (BS) to support the ever-increasing demands of high throughput and coverage. The ongoing deployment has triggered some health concerns among the community. To address this uncertainty, we propose an EMF-aware probabilistic shaping design for hardware-distorted communication systems. The proposed scheme aims to minimize human exposure to radio frequency (RF) radiations while achieving the target throughput using probabilistic shaping. The joint optimization of the transmit power and nonuniform symbol probabilities is a non–convex optimization problem. Therefore, we employ alternate optimization and successive convex approximation to solve the subsequent problems. Our findings reveal a significant reduction in the users' exposure to EMF while achieving the requisite quality of service with the help of probabilistic shaping in a hardware-distorted communication system.

The massive deployment of advanced wireless networks is essential to support broadband connectivity, low latency communication, and Internet of Things applications. Nevertheless, in the time of coronavirus disease (COVID-19) there is a massive amount of misinformation and uncertainty about the impact of fifth-generation cellular network (5G) networks on human health. In this paper, we investigate the main categories of misinformation regarding 5G, i.e., fake theories, the misconception of 5G features, and open questions that require further research. Then, we propose two novel approaches for the design of electromagnetic field (EMF)-aware cellular networks that can reduce human exposure to radio frequency radiation.

In this chapter, we consider the design of localization algorithms for reconfigurable intelligent surface (RIS)-aided models under different practical channel model settings. More specifically, we utilize the compressed sensing (CS) to localize user equipment (UE) direction and position in both far-field and near-field multipath environments respectively; we extend our work by performing a super-resolution localization using the atomic norm minimization for a user located in a single and path near-field channel. On the other hand, we propose RIS phase design that aims to minimize the localization error by maximizing the signal-to-noise ratio (SNR).

Smart radio environments aided by reconfigurable intelligent surfaces (RIS) have attracted much research attention recently. We propose a joint optimization strategy for beamforming (BF), RIS phases, and power allocation to maximize the minimum signal-to-noise ratio (SINR) of an uplink RIS-aided communication system. The users are subject to constraints on their transmit power. We derive a closed-form expression for the BF vectors and a geometric programming-based solution for power allocation. We propose two solutions for optimizing the phase shifts at the RIS, one based on the matrix lifting method and one using an approximation for the minimum function. We also propose a heuristic algorithm for optimizing quantized phase shift values. The proposed algorithms are of practical interest for systems with constraints on the maximum allowable electromagnetic field exposure. For instance, considering 16-element RIS, 4-antenna base station, and 2 users, numerical results show that the proposed algorithm achieves a gain close to 300% in terms of minimum SINR compared to a scheme with random RIS phases.

Environmental monitoring of delicate ecosystems or pristine sites is critical to their preservation. The communication infrastructure for such monitoring should have as little impact on the natural ecosystem as possible. Because of their wide range capabilities and independence from heavy infrastructure, low-power wide area network protocols have recently been used in remote monitoring. In this regard, we propose a mobile vehicle-mounted gateway architecture for IoT data collection in communication-network-free areas. The limits of reliable communication are investigated in terms of gateway speed, throughput, and energy consumption. We investigate the performance of various gateway arrival scenarios, focusing on the trade-off between freshness of data, data collection rate, and end-node power consumption. Then we validate our findings using both real-world experiments and simulations. In addition, we present a case study exploiting the proposed architecture to provide coverage for Wadi El-Gemal national park in Egypt. The results show that reliable communication is achieved over all spreading factors (SFs) for gateway speeds up to 150 km/h with negligible performance degradation at SFs=11,12 at speeds more than 100 km/h. The synchronized transmission model ensures the best performance in terms of throughput and power consumption at the expense of the freshness of data. Nonsynchronized transmission allows time-flexible data collection at the expense of increased power consumption. The same throughput as semisynchronized transmission is achieved using four gateways at only five times the energy consumption, while a single gateway requires seventeen times the amount of energy. Furthermore, increasing the number of gateways to ten increases the throughput to the level achieved by the synchronized scenario while consuming eight times the energy.

—Recent advances in Big Data Analytics are primarily driven by innovations in Artificial Intelligence and Machine Learning Methods. Due to the richness of data sources at the edge and with the increasing privacy concerns, Distributed privacy-preserving machine learning (ML) methods are increasingly becoming the norm for training ML models on federated big data. In a popular approach known as Federated learning (FL), service providers leverage end-user data to train ML models to improve services such as text auto-completion, virtual keyboards, and item recommendations. FL is expected to grow in importance with the increasing focus on big data, privacy and 5G/6G technologies. However, FL faces significant challenges such as heterogeneity, communication overheads, and privacy preservation. In practice, training models via FL is time-intensive and worse its dependent on client participation who may not always be available to join the training. Our empirical analysis shows that client availability can significantly impact the model quality which motivates the design of an availability-aware selection scheme. We propose A2FL to mitigate the quality degradation caused by the under-representation of the global client population by prioritizing the least available clients. Our results show that, compared to state-of-the-art methods, A2FL can improve the client diversity during the training and hence boost the trained model quality.

Next-generation cellular networks could witness the creation of smart radio environments (SREs), where walls and objects will be coated with reconfigurable intelligent surfaces (RISs) to strengthen the communication and localization performance. In fact, RISs have been recently introduced not only to overcome communication blockages due to obstacles but also for high-precision localization of mobile users in GPS denied environments, e.g., indoors. Towards such a vision, this paper presents the localization performance limits for communication scenarios where a single next generation NodeB base station (gNB), equipped with multiple antennas, infers the position and the orientation of a user equipment (UE) in a reconfigurable intelligent surface (RIS)-assisted smart radio environment (SRE). We consider a signal model that is valid also for near-field propagation conditions, as the usually adopted far-field assumption does not always hold, especially for large RISs. For the considered scenario, we derive the Cramér-Rao lower bound (CRLB) for assessing the ultimate localization and orientation performance of synchronous and asynchronous signalling schemes. In addition, we propose a closed-form RIS phase profile that well suits joint communication and localization, and we perform extensive numerical results to assess the performance of our scheme for various localization scenarios and for various RIS phase design. Numerical results show that the proposed scheme can achieve remarkable performance even in asynchronous signalling, and that the proposed phase design, based on signal-to-noise ratio (SNR), approaches the numerical optimal phase design that minimizes the CRLB.

Given the increasing number of space-related applications, research in the emerging space industry is becoming more and more attractive. One compelling area of current space research is the design of miniaturized satellites, known as CubeSats, which are enticing because of their numerous applications and low design-and-deployment cost. The new paradigm of connected space through CubeSats makes possible a wide range of applications, such as Earth remote sensing, space exploration, and rural connectivity. CubeSats further provide a complementary connectivity solution to the pervasive Internet of Things (IoT) networks, leading to a globally connected cyber-physical system. This paper presents a holistic overview of various aspects of CubeSat missions and provides a thorough review of the topic from both academic and industrial perspectives. We further present recent advances in the area of CubeSat communications, with an emphasis on constellation-and-coverage issues, channel modeling, modulation and coding, and networking. Finally, we identify several future research directions for CubeSat communications, including Internet of space things, low-power long-range networks, and machine learning for CubeSat resource allocation.

A prevalent theory circulating among the non-scientific community is that the intensive deployment of base stations over the territory significantly increases the level of electromagnetic field (EMF) exposure and affects population health. To alleviate this concern, in this work, we propose a network architecture that introduces tethered unmanned aerial vehicles (TUAVs) carrying green antennas to minimize the EMF exposure while guaranteeing a high data rate for users. In particular, each TUAV can attach itself to one of the possible ground stations at the top of some buildings. The location of the TUAVs, transmit power of user equipment, and association policy are optimized to minimize the EMF exposure. Unfortunately, the problem turns out to be a mixed integer non-linear programming (MINLP), which is non-deterministic polynomial-time (NP) hard. We propose an efficient low-complexity algorithm composed of three submodules. Firstly, we propose an algorithm based on the greedy principle to determine the optimal association matrix between the users and base stations. Then, we offer two approaches, modified k -mean and shrink and realign (SR) process, to associate each TUAV with a ground station. Also, we put forward two algorithms based on the golden search and SR process to adjust the TUAV's position within the hovering area over the building. Finally, we consider the dual problem that maximizes the sum rate while keeping the exposure below a predefined value, such as the level enforced by the regulation. Numerical results show that TUAVs with green antennas can effectively mitigate the EMF exposure by more than 20% compared to fixed green small cell while achieving a higher data rate.

Spectrum sensing in wideband cognitive radio networks is challenged by several factors such as hidden primary users (PUs), overhead on network resources, and the requirement of high sampling rate. Compressive sensing has been proven effective to elevate some of these problems through efficient sampling and exploiting the underlying sparse structure of the measured frequency spectrum. In this paper, we propose an approach for collaborative compressive spectrum sensing. The proposed approach achieves improved sensing performance through utilizing Kronecker sparsifying bases to exploit the two dimensional sparse structure in the measured spectrum at different, spatially separated cognitive radios. Experimental analysis through simulation shows that the proposed scheme can substantially reduce the mean square error (MSE) of the recovered power spectrum density over conventional schemes while maintaining the use of a low-rate ADC. We also show that we can achieve dramatically lower MSE under low compression ratios using a dense measurement matrix but using Nyquist rate ADC.

Spectrum Sensing in wideband cognitive radio networks is considered one of the challenging issues facing opportunistic utilization of the frequency spectrum. Collaborative compressive sensing has been proposed as an effective technique to alleviate some of these challenges through efficient sampling that exploits the underlying sparse structure of the measured frequency spectrum. In this paper, we propose to model this problem as a compressive support recovery problem, and apply the adaptive Sequential Compressive Sensing (SCS) approach to recover spectrum holes. We propose several fusion techniques to apply the proposed approach in a collaborative manner. The experimental analysis through simulations shows that the proposed scheme can substantially increase the probability of spectrum hole detection as compared to traditional CS recovery approaches while using a very low sampling rate analog to information converter, and without requiring the knowledge of any statistical information about the environmental noise.

In the context of compressed sensing, we provide a new approach to the analysis of the symmetric and asymmetric restricted isometry property for Gaussian measurement matrices. The proposed method relies on the exact distribution of the extreme eigenvalues for Wishart matrices, or on its approximation based on the Tracy-Widom law, which in turn can be approximated by means of properly shifted and scaled Gamma distributions. The resulting probability that the measurement submatrix is ill conditioned is compared with the known concentration of measure inequality bound, which has been originally adopted to prove that Gaussian matrices satisfy the restricted isometry property with overwhelming probability. The new analytical approach gives an accurate prediction of such probability, tighter than the concentration of measure bound by many orders of magnitude. Thus, the proposed method leads to an improved estimation of the minimum number of measurements required for perfect signal recovery.

One of the key issues in the acquisition of sparse data by means of compressed sensing is the design of the measurement matrix. Gaussian matrices have been proven to be information-theoretically optimal in terms of minimizing the required number of measurements for sparse recovery. In this paper, we provide a new approach for the analysis of the restricted isometry constant (RIC) of finite dimensional Gaussian measurement matrices. The proposed method relies on the exact distributions of the extreme eigenvalues for Wishart matrices. First, we derive the probability that the restricted isometry property is satisfied for a given sufficient recovery condition on the RIC, and propose a probabilistic framework to study both the symmetric and asymmetric RICs. Then, we analyze the recovery of compressible signals in noise through the statistical characterization of stability and robustness. The presented framework determines limits on various sparse recovery algorithms for finite size problems. In particular, it provides a tight lower bound on the maximum sparsity order of the acquired data allowing signal recovery with a given target probability. Also, we derive simple approximations for the RICs based on the Tracy-Widom distribution.

The deployment of the fifth-generation (5G) wireless communication services requires the installation of 5G next-generation Node-B Base Stations (gNBs) over the territory and the wide adoption of 5G User Equipment (UE). In this context, the population is concerned about the potential health risks associated with the Radio Frequency (RF) emissions from 5G equipment, with several communities actively working toward stopping the 5G deployment. To face these concerns, in this work, we analyze the health risks associated with 5G exposure by adopting a new and comprehensive viewpoint, based on the communications engineering perspective. By exploiting our background, we investigate the alleged health effects of 5G exposure and critically review the latest works that are often referenced to support the health concerns from 5G. We then precisely examine the up-to-date metrics, regulations, and assessment of compliance procedures for 5G exposure, by evaluating the latest guidelines from the Institute of Electrical and Electronics Engineers (IEEE), the International Commission on Non-Ionizing Radiation Protection (ICNIRP), the International Telecommunication Union (ITU), the International Electrotechnical Commission (IEC), and the United States Federal Communications Commission (FCC), as well as the national regulations in more than 220 countries. We also thoroughly analyze the main health risks that are frequently associated with specific 5G features (e.g., multiple-input multiple-output (MIMO), beamforming, cell densification, adoption of millimeter waves, and connection of millions of devices). Finally, we examine the risk mitigation techniques based on communications engineering that can be implemented to reduce the exposure from 5G gNB and UE. Overall, we argue that the widely perceived health risks that are attributed to 5G are not supported by scientific evidence from communications engineering. In addition, we explain how the solutions to minimize the health risks from 5G (including currently unknown effects) are already mature and ready to be implemented. Finally, future works, e.g., aimed at evaluating long-term impacts of 5G exposure, as well as innovative solutions to further reduce the RF emissions, are suggested.

An important modulation technique for Internet of Things (IoT) is the one proposed by the low power long range (LoRa) alliance. In this paper, we analyze the M -ary LoRa modulation in the time and frequency domains. First, we provide the signal description in the time domain, and show that LoRa is a memoryless continuous phase modulation. The cross-correlation between the transmitted waveforms is determined, proving that LoRa can be considered approximately an orthogonal modulation only for large M . Then, we investigate the spectral characteristics of the signal modulated by random data, obtaining a closed-form expression of the spectrum in terms of Fresnel functions. Quite surprisingly, we found that LoRa has both continuous and discrete spectra, with the discrete spectrum containing exactly a fraction 1/M of the total signal power.

Spectrum-sharing backscatter communication (SSBC) systems are among the most prominent technologies for ultralow power and spectrum-efficient communications. In this article, we propose an underlay SSBC system, in which the secondary network is a backscatter communication system. We analyze the performance of the secondary network under a transmit power adaption strategy at the secondary transmitter, which guarantees that the interference caused by the secondary network to the primary receiver is below a predetermined threshold. We first derive a novel analytical expression for the cumulative distribution function (CDF) of the instantaneous signal-to-noise ratio of the secondary network. Capitalizing on the obtained CDF, we derive novel accurate approximate expressions for the ergodic capacity, effective capacity, and average bit-error rate. We further validate our theoretical analysis using the extensive Monte Carlo simulations.

The growing interest around the cyber-physical systems (CPS), populated with open systems counting myriads of devices, is calling for new technologies both in telecommunications and software engineering with full integration among them. One of the most promising wireless communication technologies for the CPS is LoRaWAN, which enables long range transmission with low power consumption. Typical application scenarios include smart-homes, smart-cities, precision agriculture, and intelligent transportation. On the software side, novel paradigms are emerging to dominate the complexity introduced by the CPS with a large number of spatially distributed devices. Among them, aggregate computing is gaining traction, for it enables expressing the behavior of aggregates of devices by considering their ensemble as a single computational entity, allowing expressive space-time computations. In this paper, we introduce a software architecture which allows aggregate programming software to execute on a network of LoRa-communicating devices. We also provide an open source prototype implementing such architecture, which we use to study the current limitations of existing aggregate programming interpreters in resource constrained scenarios. We conclude by drawing recommendations for developing such interpreters in order to pave the way to a more power- and data-efficient design.

We study frame synchronization (FS) based on the transmission of known sequences (synchronization words) for M-PSK signals in the presence of additive white Gaussian noise and phase offset due to imperfect carrier phase estimation. In particular, we derive optimal and simple suboptimal metrics for noncoherent FS of M-PSK modulation with M ≥ 4. We show that a simple ℓ 1 -norm correction of the noncoherent correlation gives large improvements in terms of synchronization error probability. For example, more than 2 dB are gained with respect to usual correlation tests in terms of signal to noise ratio, assuming QPSK with a synchronization error probability 10 -3 . Finally, we illustrate that the proposed technique is better than correlation based metric also for M-QAM systems, as well as in the presence of small frequency offsets.

In this paper, we propose a practical adaptive coding modulation scheme to approach the capacity of free-space optical (FSO) channels with intensity modulation/direct detection based on probabilistic shaping. The encoder efficiently adapts the transmission rate to the signal-to-noise ratio, accounting for the fading induced by the atmospheric turbulence. The transponder can support an arbitrarily large number of transmission modes using a low complexity channel encoder with a small set of supported rates. Hence, it can provide a solution for FSO backhauling in terrestrial and satellite communication systems to achieve higher spectral efficiency. We propose two algorithms to determine the capacity and capacity-achieving distribution of the scheme with unipolar M -ary pulse amplitude modulation ( M -PAM) signaling. Then, the signal constellation is probabilistically shaped according to the optimal distribution, and the shaped signal is channel encoded by an efficient binary forward error correction scheme. Extensive numerical results and simulations are provided to evaluate the performance. The proposed scheme yields a rate close to the tightest lower bound on the capacity of FSO channels. For instance, the coded modulator operates within 0.2 dB from the M -PAM capacity, and it outperforms uniform signaling with more than 1.7 dB, at a transmission rate of 3 bits per channel use.

Data originating from devices and sensors in Internet of Things scenarios can often be modeled as sparse signals. In this paper, we provide new source compression schemes for noisy sparse and non-strictly sparse sources, based on channel coding theory. Specifically, nonlinear excision filtering by means of model order selection or thresholding is first used to detect the support of the non-zero elements of sparse vectors in noise. Then, the sparse sources are quantized and compressed using syndrome-based encoders. The theoretical performance of the schemes is provided, accounting for the uncertainty in the support estimation. In particular, we derive the operational distortion-rate and operational distortion-energy of the encoders for noisy Bernoulli-uniform and Bernoulli-Gaussian sparse sources. It is found that the performance of the proposed encoders approaches the information-theoretic bounds for sources with low sparsity order. As a case study, the proposed encoders are used to compress signals gathered from a real wireless sensor network for environmental monitoring.

Data originating from many devices and sensors can be modeled as sparse signals. Hence, efficient compression techniques of such data are essential to reduce bandwidth and transmission power, especially for energy constrained devices within machine to machine communication scenarios. This paper provides accurate analysis of the operational distortion-rate function (ODR) for syndrome-based source encoders of noisy sparse sources. We derive the probability density function of error due to both quantization and pre-quantization noise for a type of mixed distributed source comprising Bernoulli and an arbitrary continuous distribution, e.g., Bernoulli-uniform sources. Then, we derive the ODR for two encoding schemes based on the syndromes of Reed-Solomon (RS) and Bose, Chaudhuri, and Hocquenghem (BCH) codes. The presented analysis allows designing a quantizer such that a target average distortion is achieved. As confirmed by numerical results, the closed-form expression for ODR perfectly coincides with the simulation. Also, the performance loss compared to an entropy based encoder is tolerable.

Intelligent reflecting surface (IRS) is considered as a promising technology for enhancing the transmission rate in cellular networks. Such improvement is attributed to considering a large IRS with high number of passive reflecting elements, optimized to properly focus the incident beams towards the receiver. However, to achieve this beamforming gain, the channel state information (CSI) should be efficiently acquired at the base station (BS). Unfortunately, the traditional pilot estimation method is challenging, because the passive IRS does not have radio frequency (RF) chains and the number of channel coefficients is proportional to the number of IRS elements. In this paper, we propose a novel semi-blind channel estimation method where the reflected channels are estimated using not only pilot but also data symbols, reducing the channel estimation overhead. The performance of the system is analytically investigated in terms of the uplink achievable sum-rate. The proposed scheme achieves higher energy and spectrum efficiency while being robust to channel estimation errors. For instance, the proposed scheme achieves an 80% increase in spectrum efficiency compared to pilot-only based schemes, for IRSs with N=32 elements.

Conventional wireless techniques are becoming inadequate for beyond-5G networks due to latency and bandwidth considerations. To improve the error performance of wireless communication systems, we propose physical layer network coding (PNC) in an intelligent reflecting surface (IRS)-assisted environment. We consider an IRS-aided butterfly network, where we propose an algorithm for obtaining the optimal IRS phases. Analytic expressions for the bit error rate (BER) are derived. Numerical results demonstrate that the proposed scheme significantly improves the BER performance. For instance, the BER at the relay in the presence of a 32-element IRS is three orders of magnitudes less than that without an IRS.

Reconfigurable intelligent surface (RIS) can be crucial in next-generation communication systems. However, designing the RIS phases according to the instantaneous channel state information (CSI) can be challenging in practice due to the short coherent time of the channel. In this regard, we propose a novel algorithm based on the channel statistics of massive multiple input multiple output systems rather than the CSI. The beamforming at the base station (BS), power allocation of the users, and phase shifts at the RIS elements are optimized to maximize the minimum signal to interference and noise ratio (SINR), guaranteeing fair operation among various users. In particular, we design the RIS phases by leveraging the asymptotic deterministic equivalent of the minimum SINR that depends only on the channel statistics. This significantly reduces the computational complexity and the amount of controlling data between the BS and RIS for updating the phases. This setup is also useful for electromagnetic fields (EMF)-aware systems with constraints on the maximum user's exposure to EMF. The numerical results show that the proposed algorithms achieve more than 100% gain in terms of minimum SINR, compared to a system with random RIS phase shifts, with 40 RIS elements, 20 antennas at the BS and 10 users, respectively.

This letter provides tight upper bounds on the weak restricted isometry constant for compressed sensing with finite Gaussian measurement matrices. The bounds are used to develop a unified framework for the guaranteed recovery assessment of jointly sparse matrices from multiple measurement vectors. The analysis is based on the exact distribution of the extreme singular values of Gaussian matrices. Several joint sparse reconstruction algorithms are analytically compared in terms of the maximum support cardinality ensuring signal recovery, i.e., mixed norm minimization, MUSIC, and OSMP based algorithms.

Hardware distortions (HWDs) render drastic effects on the performance of communication systems. They are recently proven to bear asymmetric signatures; and hence can be efficiently mitigated using improper Gaussian signaling (IGS), thanks to its additional design degrees of freedom. Discrete asymmetric signaling (AS) can practically realize the IGS by shaping the signals' geometry or probability. In this paper, we adopt the probabilistic shaping (PS) instead of uniform symbols to mitigate the impact of HWDs and derive the optimal maximum a posterior detector. Then, we design the symbols' probabilities to minimize the error rate performance while accommodating the improper nature of HWD. Although the design problem is a non-convex optimization problem, we simplified it using successive convex programming and propose an iterative algorithm. We further present a hybrid shaping (HS) design to gain the combined benefits of both PS and geometric shaping (GS). Finally, extensive numerical results and Monte Carlo (MC) simulations highlight the superiority of the proposed PS over conventional uniform constellation and GS. Both PS and HS achieve substantial improvements over the traditional uniform constellation and GS with up to one order magnitude in error probability and throughput.

Reconfigurable intelligent surfaces (RISs) are expected to play a significant role in the next generation of wireless cellular technology. This paper proposes an uplink localization scheme using a single-snapshot solution for user equipment (UE) that is located in the near-field of the RIS. We propose utilizing the atomic norm minimization method to achieve super-resolution localization accuracy. We formulate an optimization problem to estimate the UE location parameters (i.e., angles and distances) by minimizing the atomic norm. Then, we propose to exploit strong duality to solve the atomic norm problem using the dual problem and semidefinite programming (SDP). The RIS is controlled and designed using estimated parameters to enhance the beamforming capabilities. Finally, we compare the localization performance of the proposed atomic norm minimization with compressed sensing (CS) in terms of localization error. The numerical results show a superior performance of the proposed atomic norm method over the CS where a sub-cm level of accuracy can be achieved under some of the system configuration conditions using the proposed atomic norm method.

Radio localization is applied in high-frequency (e.g., mmWave and THz) systems to support communication and provide location-based services without extra infrastructure. For solving localization problems, a simplified, stationary, narrowband far-field channel model is widely used due to its compact formulation. However, with increased array size in extra-large multiple-input-multiple-output (XL-MIMO) systems and increased bandwidth at upper mmWave bands, the effect of channel spatial non-stationarity (SNS), spherical wave model (SWM), and beam squint effect (BSE) cannot be ignored. In this case, localization performance will be affected when an inaccurate channel model deviates from the true model. In this work, we employ the misspecified Cramér-Rao lower bound to lower bound the localization error using a simplified mismatched model while the observed data is governed by a more complex true model. The simulation results show that among all the model impairments, the SNS has the least contribution, the SWM dominates when the distance is small compared to the array size, and the BSE has a more significant effect when the distance is much larger than the array size. Index Terms—5G/6G localization, spatial non-stationarity, spherical wave model, beam squint effect, MCRB.

The deployment of the 5th-generation cellular networks (5G) and beyond has triggered health concerns due to the electric and magnetic fields (EMF) exposure. In this letter, we propose a novel architecture to minimize the population exposure to EMF by considering a smart radio environment with a reconfigurable intelligent surface (RIS). Then, we optimize the RIS phases to minimize the exposure in terms of the exposure index (EI) while maintaining a minimum target quality of service. The proposed scheme achieves up to 20% reduction in EI compared to schemes without RISs.

Reconfigurable intelligent surfaces (RISs) are considered among the key techniques to be adopted for sixth-generation cellular networks (6G) to enhance not only communications but also localization performance. In this regard, we propose a novel single-anchor localization algorithm for a state-of-the-art architecture where the position of the user equipment (UE) is to be estimated at the base station (BS) with the aid of a RIS. We consider a practical model that accounts for both near-field propagation and multipath environments. The proposed scheme relies on a compressed sensing (CS) technique tailored to address the issues associated with near-field localization and model mismatches. Also, the RIS phases are optimized to enhance the positioning performance, achieving more than one order of magnitude gain in the localization accuracy compared to RISs with non-optimized phases.

Visible light communication (VLC) is a promising technology for 6th-generation (6G) networks because of its attractive feature such as a wide unlicensed spectrum. In this paper, a novel adaptive coded spatial modulation scheme with probabilistic shaping (PS) is proposed to approach the capacity of the spatial modulation (SM) in VLC channels with intensity modulation and direct detection (IM/DD). In the proposed scheme, spatial and constellation symbols are probabilistically shaped depending on the user's location inside the room and the optical signal-to-noise ratio (OSNR). Moreover, we optimize the channel coding rate to maximize further the achievable rate of the proposed scheme for a given OSNR. Finally, we propose an algorithm to compute the capacity-achieving distribution of the proposed scheme with unipolar M-ary pulse amplitude modulation (PAM) signaling. The proposed scheme outperforms uniform and an orthogonal frequency-division multiplexing (OFDM) based scheme in terms of spectral efficiency (SE) and/or frame error rate (FER). For example, for 8-PAM signaling with N = 8 transmit antennas, the proposed scheme operates within 0.2 dB from the unipolar M-PAM SM VLC channel signaling capacity and outperforms the uniform and OFDM based schemes in terms of FER by at least 1.1 dB and 1.3 dB at a normalized data rate of 1.33 bits per channel use per sub-carrier (b/cu/sc), respectively.