Professor Yue Gao

The low earth orbit (LEO) satellite network is undergoing rapid development with the maturing of satellite communications and rocket launch technologies, and the demand for a global coverage network. However, current satellite communication networks are constrained by limited transmitting signal power, resulting in the use of large-size and energy-consuming ground terminals to provide additional gain. This paper proposes a novel technology called distributed beamforming to address such challenges and support direct communications from LEO satellites to smartphones. The proposed distributed beamforming technique is based on the superposition of electromagnetic (EM) waves and aims to enhance the received signal strength. Furthermore, we utilize EM wave superposition to increase the link budget and provide the coverage pattern formed by the distributed antenna array, which will be affected by the array structure and the transmitter parameters. In addition, the impact of Doppler frequency shift and time misalignment on the performance of distributed beamforming is investigated. Numerical results show that the enhancement of the received power depends on the angle formed by those radiated beams and can be up to the square of the number of beams; namely, a maximum enhancement of 6 dB could be obtained by using two satellites and a maximum of 12 dB increase through four satellites, which provide a clear guideline for the design of distributed beamforming for future satellite communications.

Small satellites in Low Earth Orbit (LEO) attract much attention from both industry and academia. The latest production and launch technologies constantly drive the development of LEO constellations. However, the wideband signal, except text messages, cannot be transmitted directly from an LEO satellite to a standard mobile cellular phone due to the insufficient link budget. The current LEO constellation network has to use an extra ground device to receive the signal from the satellite first and then forward the signal to the User Equipment (UE). To achieve direct network communications between LEO satellites and UE, we propose a novel distributed beamforming technology based on the superposition of electromagnetic (EM) waves radiated from multiple satellites that can significantly enhance the link budget in this paper. EM full-wave simulation and Monte Carlo simulation results are provided to verify the effectiveness of the proposed method. The simulation results show a nearly 6 dB enhancement using two radiation sources and an almost 12 dB enhancement using four sources. The received power enhancement could be doubled compared to the diversity gain in Multiple-Input and Single-Output (MISO). Furthermore, other practical application challenges, such as the synchronization and Doppler effect, are also presented.

Conference Title: 2021 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM) Conference Start Date: 2021, Nov. 28 Conference End Date: 2021, Nov. 30 Conference Location: Guangzhou, ChinaRecent deployments of satellite constellations have taken an increased interest in Low Earth Orbit (LEO) owing to the advantages of lower path loss, lower latency in terms of the propagation delay and coverage of concentrated pockets of area. Due to the nature of these orbits, antennas on the ground station with the ability to scan over wide angles is necessitated. Phased array antennas are the ideal candidates in such a scenario owing to their low cost, low profile and wide-angle scanning performance. In this paper, a phased array antenna is designed and presented to operate around the n261 band of 5GNRFR2 and the 28 GHz Ka-band with an azimuthal beamscanning range of 50° on either side of the boresight. There is also a symmetric elevation scan around boresight of +/-25° attained without the use of phase shifters. The antenna element consists of a multilayer series aperture coupled antenna fed by a meander line. It offers good performance in terms of beamscanning cost as the phase shifter requirement is reduced, owing to the use of frequency scanning in elevation and phase scanning only in the azimuth, drastically reducing the cost. The phased array has a good total efficiency around 80%. The antenna has been designed and its performance validated by full wave simulations in the solver CST Studio Suite.

The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

Cognitive radio provides a feasible solution for alleviating the lack of spectrum resources by enabling secondary users to access the unused spectrum dynamically. Spectrum sensing and learning, as the fundamental function for dynamic spectrum sharing in 5G evolution and 6G wireless systems, have been research hotspots worldwide. This paper reviews classic narrowband and wideband spectrum sensing and learning algorithms. The sub-sampling framework and recovery algorithms based on compressed sensing theory and their hardware implementation are discussed under the trend of high channel bandwidth and large capacity to be deployed in 5G evolution and 6G communication systems. This paper also investigates and summarizes the recent progress in machine learning for spectrum sensing technology.

The Internet of Things (IoT) pervades every aspect of our daily lives and industrial productions since billions of interconnected devices are deployed everywhere of the globe. However, the seamless IoT unveils a number of physical-layer threats, such as jamming and spoofing that decrease the communication performance and the reliability of the IoT systems. As the process of identifying the modulation format of signals corrupted by noise and fading, automatic modulation classification (AMC) plays a vital role in physical-layer security as it can detect and identify the pilot jamming, deceptive jamming, and sybil attacks. In this article, we propose a novel cyclic correntropy vector (CCV)-based AMC method using long short-term memory densely connected network (LSMD). Specifically, cyclic correntropy model-driven feature CCV is first extracted using the received signals as it contains both the second-order and the higher order characteristics of cyclostationary. Then, the extracted CCV feature is put into the data-driven LSMD which mainly consists of long short-term memory (LSTM) network and dense network (DenseNet). Moreover, an additive cosine loss is utilized to train the LSMD for maximizing the interclass feature differences and minimizing the intraclass feature variations. Simulations demonstrate that the proposed CCV-LSMD method yields superior performance than other recent schemes.

As wireless technology continues to expand, there is a growing concern about the efficient use of spectrum resources. Even though a significant portion of the spectrum is allocated to licensed primary users (PUs), studies indicate that their actual utilization is often limited to between 5% to 10% [1]. The underutilization of spectrum has given rise to cognitive radio (CR) technology, which allows secondary users (SUs) to opportunistically access these underused resources [2]. However, wideband spectrum sensing, the key of CR, is limited by the need for high-speed analog-to-digital converters (ADCs), which are costly and power-hungry. Compressed spectrum sensing (CSS) addresses this challenge by employing sub-Nyquist rate sampling. The efficiency of active transmission detection heavily depends on the quality of spectrum reconstruction. There are various reconstruction methods in CSS, each with its merits and drawbacks. Still, existing algorithms have not tapped into the full potential of sub-sampling sequences, and their performance notably drops in noisy environments [3,4]. The GHz Bandwidth Sensing (GBSense) project1) introduces an innovative approach for GHz bandwidth sensing. GBSense incorporates advanced sub-Nyquist sampling methods and is compatible with low-power devices. This project also prompted the GBSense Challenge 2021, which centered on sub-Nyquist reconstruction algorithms, with four leading algorithms to be presented and evaluated in this paper.

Millimeter Wave (mmWave) band can be a solution to serve the vast number of Internet of Things (IoT) and Vehicle to Everything (V2X) devices. In this context, Cognitive Radio (CR) is capable of managing the mmWave spectrum sharing efficiently. However, Cognitive mmWave Radios are vulnerable to malicious users due to the complex dynamic radio environment and the shared access medium. This indicates the necessity to implement techniques able to detect precisely any anomalous behaviour in the spectrum to build secure and efficient radios. In this work, we propose a comparison framework between deep generative models: Conditional Generative Adversarial Network (C-GAN), Auxiliary Classifier Generative Adversarial Network (AC-GAN), and Variational Auto Encoder (VAE) used to detect anomalies inside the dynamic radio spectrum. For the sake of the evaluation, a real mmWave dataset is used, and results show that all of the models achieve high probability in detecting spectrum anomalies. Especially, AC-GAN that outperforms C-GAN and VAE in terms of accuracy and probability of detection.

Cellular connectivity for a massive number of Unmanned Aerial Vehicles (UAVs) will overcrowd the radio spectrum and cause spectrum scarcity. Incorporating Cognitive Radio (CR) with UAVs (Cognitive-UAV-Radios) has been proposed to overcome such an issue. However, the broadcasting nature of CR and the dominant line-of-sight links of UAV makes the Cognitive-UAV-Radios susceptible to jamming attacks. In this paper, we propose a framework to detect smart jammer, which locates and attacks the UAV commands with low Jamming-to-Signal-Power-Ratio (JSR). Smart jammer is more challenging than the types of jammers that always require high power values. Our work focuses on learning a Dynamic Bayesian Network (DBN) to model and analyze the signals' behaviour statistically. A Markov Jump Particle Filter (MJPF) is employed to perform predictions and consequently detect jamming signals. The results are satisfactory in terms of detection probability and false alarm rate that outperform the conventional Energy Detector approach.

A switchable multibeam antenna array with a compact planar feeding network is presented. Operating within 1 GHz bandwidth at 28 GHz, this 4 × 4 antenna can generate onebeam, two-beam and four-beam patterns based on two phase states, which are controlled by the reconfigurable feeding network. The whole structure is validated by simulating in CST Studio. The antenna is a promising candidate in millimeterwave Massive MIMO for applications where multiple beams are required simultaneously.

Multiple-input multiple-output filterbank multicarrier communication (MIMO-FBMC) is a promising technique to achieve very tight spectrum confinement (thus, higher spectral efficiency) as well as strong robustness against dispersive channels. In this paper, we present a novel training design for MIMO-FBMC system which enables efficient estimate of frequency-selective channels (associated to multiple transmit antennas) with only one non-zero FBMC symbol. Our key idea is to design real-valued orthogonal training sequences (in the frequency domain) which displaying zero-correlation zone properties in the time-domain. Compared to our earlier proposed training scheme requiring at least two non-zero FBMC symbols (separated by several zero guard symbols), the proposed scheme features ultra-low training overhead yet achieves channel estimation performance comparable to our earlier proposed complex training sequence decomposition(CTSD). Our simulations validate that the proposed method is an efficient channel estimation approach for practical preamble-based MIMO-FBMC systems.

In this paper, a bilateral teleoperation system (BTS) is proposed for planetary rover remote control and operation. This is a continuation of the work presented in on computer-simulated virtual environment, that can be used as a predictive display module in the BTS. In the proposed system, a haptic device is used by the human operators to control planetary rovers as driving a car. Dynamic model of the rover is used and a passive-based bilateral teleoperation scheme is adopted to achieve the stability of the close-loop control. Kinematic model of the rover is used to get the torque distribution algorithm and applied to calculate the corresponding torque for each wheel based on the desired linear speed and heading angle of the rover. Buffer unit is designed to address the issue of variable time-delay caused by the Internet. Lab-based experiments have been carried out using the BH2 rover. The test results demonstrate good performance of the proposed system.

The annual global production of chickens exceeds 25 billion birds, which are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an 'iceberg indicator' of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotation, which is very labour-intensive and time-consuming. Thus, a novel convolutional neural network-based model, light-VGG11, was developed to automatically identify chicken distress calls using recordings (3363 distress calls and 1973 natural barn sounds) collected on an intensive farm. The light-VGG11 was modified from VGG11 with significantly fewer parameters (9.3 million versus 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e. precision (94.58%), recall (94.89%), F1-score (94.73%) and accuracy (95.07%), therefore more useful for model deployment in practice. To additionally improve light-VGG11's performance, we investigated the impacts of different data augmentation techniques (i.e. time masking, frequency masking, mixed spectrograms of the same class and Gaussian noise) and found that they could improve distress calls detection by up to 1.52%. Our distress call detection demonstration on continuous audio recordings, shows the potential for developing technologies to monitor the output of this call type in large, commercial chicken flocks.

MmWave communication suffers from severe path loss due to high frequency and is sensitive to blockages because of high penetration loss, especially in mobile communication scenarios. It highly depends on line-of-sight channels and narrow beams, and thus efficient beam tracking and beam alignment are necessary techniques to maintain robust communication links, in which tracking user mobility lays the foundation for beam tracking. In this article, ML techniques are applied to learn the mobility of the mobile mmWave users and predict their moving directions. Moreover, this article builds up an experiment environment by using the National Instruments mmWave transceiver system and our designed high gain antenna operated at 28 GHz carrier frequency, and then collects experimental data of the transmitted mmWave signals, which are next trained by deep learning algorithms. A deep neural network is learned and then used to predict a user's moving direction with up to 80 percent prediction accuracy in mmWave communication without the support of traditional channel estimation.

—Anomaly detection is an essential part of spectrum monitoring applications. Malicious users and malfunctioning nodes could be identified via anomaly detection methods. Meanwhile , the spectrum bands that would be utilized in future 6G or satellite communication system settings are going to be wider than ever. Acquiring Nyquist sampled data from such a spectrum would require components with a very high sampling rate. To monitor a wide spectrum, a compressive sensing recovery algorithm combined with a sub-sampling approach could accomplish the task with a lower hardware cost. To solve the anomaly detection problem using a sub-sampled data stream, a joint signal recovery and anomaly detection solution utilizing an adversarial autoencoder (AAE) structure are proposed in this article. An AAE is constructed via an autoencoder and a discriminator weaved together. The discriminator would guide the autoencoder to drive its extracted feature onto a designed feature space, while the autoencoder would provide a reconstruction of the original Nyquist sampled signal. The proposed AAE structure could learn the distribution of the signal from either labelled or unlabelled training data, enabling it to work on both supervised and unsupervised data sets. The proposed AAE has shown superior reconstruction and detection performance on very sparse sampling scenarios.

In this paper, we present a novel sequence design for efficient channel estimation in multiple input multiple output filterbank multicarrier (MIMO-FBMC) system with offset QAM modulation. Our proposed sequences, transmitted over one FBMC/OQAM symbol, are real-valued in the frequency domain and display zero-correlation zone properties in the time-domain. The latter property enables optimal channel estimation for a least-square estimator in frequency-selective fading channels. To further improve the system performance, we mitigate the data interference by an iterative feedback loop between channel estimation and FBMC demodulation. Simulation results validate that our proposed real-valued orthogonal sequences and the iterative channel estimation and demodulation scheme provide a practical solution for enhanced performance in preamble-based MIMO-FBMC systems.

This paper presents two new algorithms for wideband spectrum sensing at sub-Nyquist sampling rates, for both single nodes and cooperative multiple nodes. In single-node spectrum sensing, a two-phase spectrum sensing algorithm based on compressive sensing is proposed to reduce the computational complexity and improve the robustness at secondary users (SUs). In the cooperative multiple nodes case, the signals received at SUs exhibit a sparsity property that yields a low-rank matrix of compressed measurements at the fusion center. This therefore leads to a two-phase cooperative spectrum sensing algorithm for cooperative multiple SUs based on low-rank matrix completion. In addition, the two proposed spectrum sensing algorithms are evaluated on the TV white space (TVWS), in which pioneering work aimed at enabling dynamic spectrum access into practice has been promoted by both the Federal Communications Commission and the U.K. Office of Communications. The proposed algorithms are tested on the real-time signals after they have been validated by the simulated signals in TVWS. The numerical results show that our proposed algorithms are more robust to channel noise and have lower computational complexity than the state-of-the-art algorithms.

This letter shows how slugs of liquid metal can be used to connect/disconnect large areas of metalization and achieve a radiation performance not possible by using conventional switches. The proposed antenna can switch its operating bandwidth between ultrawideband and narrowband by connecting/disconnecting the ground plane for the feedline from that of the radiator. This could be achieved by using conventional semiconductor switches. However, such switches provide point-like contacts. Consequently, there are gaps in electrical contact between the switches. Surface currents, flowing around these gaps, lead to significant back radiation. In this letter, the slugs of a liquid metal are used to completely fill the gaps. This significantly reduces the back radiation, increases the bore-sight gain, and produces a pattern identical to that of a conventional microstrip patch antenna. Specifically, the realized gain and total efficiency are increased by 2 dBi and 24%, respectively. The antenna has potential applications in wireless systems employing cognitive radio (CR) and spectrum aggregation.

With increasing demand of wireless radio spectrum, fixed spectrum assignment policy leads to spectrum scarcity worldwide. However, most portion of spectrum is inefficiently used, which urges the development of dynamic spectrum access techniques [1]. The concept of cognitive radio (CR) is proposed as a possible solution to solve the spectral congestion problem. It provides the capability to utilize spectrum bands more efficiently in an opportunistic manner without much interruptions to primary users [2]–[4]. In the cognitive radio networks (CRNs), sensors are used to detect the presence of licensed users and find spectrum holes for dynamic spectrum access. Traditional spectrum sensing is usually carried out by CR nodes. This procedure requires complex computation and sufficient storage space to download software packages.

Evolving from cognitive radio networks (CRNs), the concept has developed into a new paradigm of cloud and big data-based next-generation CRNs due to huge amount of data processing, complicated spectrum resource scheduling, and real-time information exchange. In cloud-based CRNs, control channels are needed for CR nodes to perform certain handshakings for network self-organization, spectrum sensing, network coordination, and flexible data connections. This paper investigates a transmission scheme for control channel (CC) in cloud-based CRNs, which is over several noncontiguous spectral holes. Transform domain communication system (TDCS)-based transmission scheme with spectrally-constrained sequence design is presented for CC. A practical testbed design for TDCS-based CC with multiple National Instruments PXIe devices and six universal software defined radio reconfigurable input/output devices is presented. Details of system design as well as main implementation challenges are described. Bit-error rate of the system is validated through both theoretical analysis and simulation results under realistic channel conditions.

Certain limitations exist in autonomous software and guidance, navigation, and control architectures developed for extraterrestrial planetary exploration rovers in regard to fault tolerance, changes in environment, and changes in rover capabilities. To address these limitations, this paper outlines a self-reconfiguring guidance, navigation, and control architecture, using an ontology-based rational agent to enable autonomous reconfiguration of mission goals, software architecture, software components, and the control of hardware components during the run time. This new architecture was evaluated through implementation onboard a rover and tested in challenging, Mars-like environments, both simulated and real world, and was found to be highly reliable, fault tolerant, and adaptable.

Redundant and nonoperational buildings at nuclear sites go through the process of ‘decommissioning’, involving decontamination of nuclear waste material and demolition of physical infrastructure. One challenging problem currently faced by the nuclear industry during this process is the segregation of redundant waste material into a choice of ‘post-processes’ based upon the nature and extent of its radioactivity that may pose a serious threat to the environment. Following an initial inspection, waste materials are subjected to treatment, disruption and consigned to various types of export containers. To date, the process of objects (waste) classification is performed manually. In order to automate this process, robotic platforms can be deployed that utilise robust and fast vision systems for visual classification of nuclear waste material. This paper proposes a novel solution incorporating a machine vision system for autonomous identification of waste material from decommissioned nuclear plants. Rotation and scale invariant moments are used to describe object shapes in the visual scene whereas a random forest learning algorithm performs object classification. Using nuclear waste simulants (from the nuclear plant decommissioning process), an exhaustive ‘proof-of-concept’ quantitative assessment of the proposed technique is performed, in order to test its applicability within the current problem domain.

: In recent decades, terrain modelling and reconstruction techniques have increased research interest in precise short and long distance autonomous navigation, localisation and mapping within field robotics. One of the most challenging applications is in relation to autonomous planetary exploration using mobile robots. Rovers deployed to explore extraterrestrial surfaces are required to perceive and model the environment with little or no intervention from the ground station. Up to date, stereopsis represents the state-of-the art method and can achieve short-distance planetary surface modelling. However, future space missions will require scene reconstruction at greater distance, fidelity and feature complexity, potentially using other sensors like Light Detection And Ranging (LIDAR). LIDAR has been extensively exploited for target detection, identification, and depth estimation in terrestrial robotics, but is still under development to become a viable technology for space robotics. This paper will first review current methods for scene reconstruction and terrain modelling using cameras in planetary robotics and LIDARs in terrestrial robotics; then we will propose camera-LIDAR fusion as a feasible technique to overcome the limitations of either of these individual sensors for planetary exploration. A comprehensive analysis will be presented to demonstrate the advantages of camera-LIDAR fusion in terms of range, fidelity, accuracy and computation.

In cognitive radio networks, cooperative spectrum sensing (CSS) has been a promising approach to improve sensing performance by utilizing spatial diversity of participating secondary users (SUs). In current CSS networks, all cooperative SUs are assumed to be honest and genuine. However, the presence of malicious users sending out dishonest data can severely degrade the performance of CSS networks. In this paper, a framework with high detection accuracy and low costs of data acquisition at SUs is developed, with the purpose of mitigating the influences of malicious users. More specifically, a low-rank matrix completion based malicious user detection framework is proposed. In the proposed framework, in order to avoid requiring any prior information about the CSS network, a rank estimation algorithm and an estimation strategy for the number of corrupted channels are proposed. Numerical results show that the proposed malicious user detection framework achieves high detection accuracy with lower data acquisition costs in comparison with the conventional approach. After being validated by simulations, the proposed malicious user detection framework is tested on the real-world signals over TV white space spectrum.

TV White Spaces (TVWS) technology allows wireless devices to opportunistically use locally-available TV channels enabled by a geolocation database. The UK regulator Ofcom has initiated a pilot of TVWS technology in the UK. This paper concerns a large-scale series of trials under that pilot. The purposes are to test aspects of white space technology, including the white space device and geolocation database interactions, the validity of the channel availability/powers calculations by the database and associated interference effects on primary services, and the performances of the white space devices, among others. An additional key purpose is to perform research investigations such as on aggregation of TVWS resources with conventional resources and also aggregation solely within TVWS, secondary coexistence issues and means to mitigate such issues, and primary coexistence issues under challenging deployment geometries, among others. This paper provides an update on the trials, giving an overview of their objectives and characteristics, some aspects that have been covered, and some early results and observations.

The simultaneous wireless information and power transfer (SWIPT) has recently attracted much attention since both information and energy are integrated within radio frequency signals, which is considered to be one of the most important technologies for future networks. However, security becomes one of the most critical issues in SWIPT networks due to its broadcasting features over wireless media and the transferring requirements for strong signal strength. To improve the security of SWIPT networks, in this paper, a SWIPT system enabled by full-duplex (FD) jamming is proposed, where an FD energy-limited receiver with the power splitting (PS) structure, powered by a transmitter, generates jamming signals. Both linear and nonlinear energy harvesting (EH) models are considered. To evaluate the secrecy throughput (ST) of the system, we derive the closed-form connection outage probability and secrecy outage probability for the proposed PS-based scheme in the delay-constrained transmission (DCT) mode. The ST is maximized by optimizing the PS ratio based on the asymptotic cases of the high signal-to-noise ratio as well as the perfect self-interference cancellation (SIC). Besides, the exact integral forms of the ergodic secrecy capacity and their closed-form lower bounds are presented for both the PS-based and the time switching (TS)-based schemes in the delay-tolerant transmission (DTT) mode. Simulation results suggest that the PS-based scheme outperforms the TS-based scheme for the DTT mode. For the DCT mode, the PS-based scheme can achieve better performance than the TS-based scheme only in low target communication rate, small PS/TS ratio, high transmit power, and weak path loss. Meanwhile, given a tolerable amount of SIC, the ST obtained in the linear EH model is higher than that achieved in the nonlinear EH model.

Adaptive compressed spectrum sensing (ACSS) can effectively save sampling resources in wideband spectrum sensing. Almost all of the existing ACSS algorithms are based on the discrete multitone signal model. However, real-world spectra are always multiband signals. In this paper, we derive mathematical models and algorithms enabling the ACSS suitable for multiband signals, which can save sampling resources and has lower computational complexity. Firstly, we introduce the multicoset sampling system into ACSS to sample multiband signals. Besides, we propose a leave-one-out cross-validation (LOOCV) based ACSS scheme with low sampling costs. To save sampling resources, we choose only one sampling channel as a testing subset to validate reconstructed signal and repeat this several times with different sampling channels. Then, we use the mean of the multiple validation results to determine the accuracy of the reconstructed signal. To reduce computational complexity, we propose a LOOCV-ACSS algorithm, in which we only perform the least square method several times in the LOOCV procedure, rather than the complicated compressed sensing reconstruction algorithms. Numerical simulations and real-world signal test results demonstrate that our derivation and algorithms are effective to reduce the sampling cost while keeping the same performance as conventional algorithms.

In cognitive radio enabled vehicular ad-hoc networks (CR-VANETs), the secondary users, i.e., the secondary intelligent vehicles have the ability to perceive the driving environment and use the unoccupied spectrum of primary users for data transmission. In this paper, we consider the radar assisted CR-VANETs, in which the secondary users firstly sense the surroundings using radar modules periodically and Swerling 0, Swerling 2 and Swerling 4 target models are considered respectively. Note that the secondary users access the spectrum of primary users and communicate with their corresponding receivers when no one is detected by the low range radar module. Moreover, we design the joint radar sensing and data transmission frame structure and formulate the radar sensing-throughput tradeoff problem mathematically. It is proved that the formulated tradeoff indeed has the optimal radar sensing time which yields the maximum throughput for the secondary networks. Numerical results and simulations verify that the optimal radar sensing time achieving the maximum throughput for Swerling 0 target model is {0.05}\; \text{ms} when the radius of the radar sensing region is 320 meters, the densities of the primary users and secondary users are \lambda _p=0.01 / m^2 and \lambda _s=0.2 / m^2, the received radar SNR is 10 dB and the detection probability is 99.9%. For the Swerling 2 and Swerling 4 target models, the optimal radar sensing times achieving the maximum throughput is {0.14}\; \text{ms} and {0.08}\; \text{ms} respectively when the detection probability is 99%.

The increasingly popular characteristic mode analysis (CMA) techniques in the free-space have been well-established, while the half-space counterpart is less concerned. In this communication, we consider the CMA for half-space platforms such as vehicles on the ground and ships on the sea surface, which is also commonly mounted with antennas. To overcome the difficulty of CMA's application on electrically large targets, the half-space multilevel fast multipole algorithm is integrated into the eigensolver. Numerical results of half-space CMA for realistic platforms from FM-band to {L} -band are presented to validate the accuracy and the robustness of the proposed method. It has been shown that this communication can be used as a legitimate CMA tool for the antenna design and integration on half-space platforms.

Beamspace multiple-input multiple-output (B-MIMO) provides a promising solution for reducing the number of required expensive radio frequency (RF) chains without obvious performance loss. However, the high computational complexities of beam selection algorithms prohibits their practical applications in 5G millimeter wave (mmWave) massive MIMO communication systems. Reviewing existing beam selection procedures and using the matrix perturbation theory, the complexity reduction on beam selection for beamspace MIMO is considered in this letter. Two beam selection algorithms, one for B-MIMO systems with classic zero-forcing (ZF) precoder and the other for the systems with QR precoder are proposed. Theoretical analyses and numerical simulations all show that the proposed beam selection algorithms possess a much lower complexity than conventional ones.

Channel estimation is crucial to beamforming techniques in directional millimetre wave (mmWave) communications, which is generally designed based on channel state information static. However, due to the Doppler effect caused by the mobility of users, such as unmanned aerial vehicles, high-speed trains and autonomous vehicles, the mmWave channel is changing rapidly. Spatial channel covariance, defined by long-term statistic information of channels, is a promising solution to reduce channel estimation frequency and can be used to design hybrid precoders. In this paper, we first proposed a highly mobile hybrid mmWave multi-user (MU) multiple input multiple output (MIMO) system based on transition probabilities which can represent moving action of the MU. Secondly, we investigate compressive sensing based spatial channel covariance estimation based on the proposed dynamic system. We then propose a dynamic covariance forward-backward pursuit (DCFBP) algorithm which introduces forward and backward mechanisms to reconstruct the Hermitian sparse covariance matrix. We further explore the constructed MU sensing matrix quality for conventional sparse Bayesian learning (SBL) framework. The updated sparse Bayesian learning (Updated-SBL) algorithm is developed to reduce the total squared coherence of a constructed sensing matrix with updated receive precoder. Numerical analysis demonstrates the proposed DCFBP method outperforms the benchmark methods. The total squared coherence of the proposed Updated-SBL algorithm is dramatically reduced. and the superiority of this algorithm is validated compared with other benchmark methods with comparable computation complexity.

The development of the Internet-of-Things (IoT) security is comparatively slower than the pace of the IoT innovations. The seamless IoT network operates in an untrusted environment and is exposed to many malicious active attacks. As the process of identifying the modulation format of signals is corrupted by noise and fading, automatic modulation classification (AMC) can be viewed as an effective approach to counter physical-layer threats for IoT as it can detect and identify the pilot jamming, deceptive jamming, and Sybil attacks. Nowadays, data-driven deep learning (DL) techniques, which are capable of extracting discriminative features and perform better robustness to channel and noise conditions, have drawn widespread attention. The deep residual network (ResNet) has a strong representative ability, which can learn latent information repeatedly from the received signals and improve the classification accuracy. Meanwhile, the gated recurrent unit (GRU), which is capable of exploiting temporal information of the received signal can expand the dimension of the signal features for satisfactory classification performance. Considering the advantages of the above networks, this article proposes a novel gated recurrent residual neural network (GrrNet) for feature-based AMC, where the amplitude and phase of the received signal are utilized as the inputs of GrrNet. In GrrNet, a ResNet extractor module is first designed to extract the highly representative features and then temporal information is obtained by the subsequent GRU module which is capable of processing the representative features with the arbitrary length for modulation classification. Moreover, extensive simulations are conducted to verify the classification performance and robustness of the proposed GrrNet and it is shown that GrrNet outperforms other recent DL-based AMC methods. Moreover, the influence of the network parameters, symbol length, and frequency offset on performance is also explored.

In this paper, a compact, highly integrated multiplexing filtering antenna operating at 4.7/5.2/6.0/6.6 GHz is proposed for the first time. Different from traditional antennas, the proposed antenna has one shared radiator but four ports working in different frequency bands and thus, it can simultaneously support four different transmission channels. The proposed multiplexing antenna is composed of a patch with a U-shaped slot, two substrate integrated waveguide (SIW) cavities, and four resonator-based frequency-selective paths. The resonator-based paths can not only enhance the inter-channel isolations but also improve the impedance bandwidth. The design principles and the methods of controlling the four operating bands are studied. Measurement results agree reasonably well with the simulations, showing four channels from 4.5 to 4.8 GHz, 5.1 to 5.3 GHz, 5.85 to 6.3 GHz, and 6.4 to 6.6 GHz, respectively. The antenna also exhibits a high isolation of over 25 dB between the channels. In addition, the proposed antenna has a consistent broadside radiation pattern and polarization in the four bands, manifesting the proposed multiplexing filtering antenna can be a promising candidate for multi-service wireless communication systems.

Sub-Nyquist spectrum sensing and learning are investigated from theory to practice as a promising approach enabling cognitive and intelligent radios and wireless systems to work in GHz channel bandwidth. These techniques would be helpful for future electromagnetic spectrum sensing in sub-6 GHz, millimetre-wave, and Terahertz frequency bands. However, challenges such as computation complexity, real-time processing and theoretical sampling limits still exist. We issued a challenge with a reference sub-Nyquist algorithm, open data set and awards up to 10,000 USD to stimulate novel approaches and designs on sub-Nyquist spectrum sensing and learning algorithms to promote relative research and facilitate the theory-to-practice process of promising ideas.

In this article, we investigate resource allocation with edge computing in Internet-of-Things (IoT) networks via machine learning approaches. Edge computing is playing a promising role in IoT networks by providing computing capabilities close to users. However, the massive number of users in IoT networks requires sufficient spectrum resource to transmit their computation tasks to an edge server, while the IoT users were developed to have more powerful computation ability recently, which makes it possible for them to execute some tasks locally. Then, the design of computation task offloading policies for such IoT edge computing systems remains challenging. In this article, centralized user clustering is explored to group the IoT users into different clusters according to users' priorities. The cluster with the highest priority is assigned to offload computation tasks and executed at the edge server, while the lowest priority cluster executes computation tasks locally. For the other clusters, the design of distributed task offloading policies for the IoT users is modeled by a Markov decision process, where each IoT user is considered as an agent which makes a series of decisions on task offloading by minimizing the system cost based on the environment dynamics. To deal with the curse of high dimensionality, we use a deep Q -network to learn the optimal policy in which deep neural network is used to approximate the Q -function in Q -learning. Simulations show that users are grouped into clusters with optimal number of clusters. Moreover, our proposed computation offloading algorithm outperforms the other baseline schemes under the same system costs.

This paper presents designs and prototypes of low cost multiple input multiple output (MIMO) antennas for 5G and millimetre-wave (mm-wave) applications. The proposed MIMOs are fabricated using 3D printing and are able deliver beams in multiple directions that provide continuous and real time coverage in the elevation of up to {\mp }30^{\circ } without using phase shifters. This equips the proposed MIMO with a superior advantage of being an attractive low cost technology for 5G and mm-wave applications. The proposed MIMO antennas operate at the 28 GHz 5G band, with wide bandwidth performance exceeds 4 GHz and with beam switching ability of up to {\mp }30^{\circ } in the elevation plane. The direction of the main beam of the single element antenna in the MIMO is steered over the entire bandwidth through introducing 3D printed walls with different heights on the side of the 3D printed radiating antenna. Unlike all other available beam steering techniques; the proposed wall is not only able to change the direction of the beam of the antenna, but also it is able to increase the overall directivity and gain of the proposed antenna and MIMO at the same time over the entire bandwidth.

Compressive sensing (CS) is a technique frequently adopted in wireless communications. By utilizing CS, a receiver could sense the state of channels with sub-Nyquist analog to digital converters when signals are sparse. Traditional CS methods struggle with non-sparse signals due to their intrinsic sparsity assumption. Therefore, we propose using deep learning (DL) to solve the vector support recovery problem with channels' high occupancy. The simulation results show that the proposed CS framework powered by DL can perform better than a traditional CS analytical benchmark, both in high and low channel occupation regions. We also observe that the ML can work under a lower sampling rate than traditional CS methods. To process data sampled with high channel numbers, a divide and conquer tactic is implemented.

The space-air-ground integrated network (SAGIN) aims to provide seamless wide-area connections, high throughput and strong resilience for 5G and beyond communications. Acting as a crucial link segment of the SAGIN, unmanned aerial vehicle (UAV)-satellite communication has drawn much attention. However, it is a key challenge to track dynamic channel information due to the low earth orbit (LEO) satellite orbiting and three-dimensional (3D) UAV trajectory. In this paper, we explore the 3D channel tracking for a Ka-band UAV-satellite communication system. We firstly propose a statistical dynamic channel model called 3D two-dimensional Markov model (3D-2D-MM) for the UAV-satellite communication system by exploiting the probabilistic insight relationship of both hidden value vector and joint hidden support vector. Specifically, for the joint hidden support vector, we consider a more realistic 3D support vector in both azimuth and elevation direction. Moreover, the spatial sparsity structure and the time-varying probabilistic relationship between degree patterns named the spatial and temporal correlation, respectively, are studied for each direction. Furthermore, we derive a novel 3D dynamic turbo approximate message passing (3D-DTAMP) algorithm to recursively track the dynamic channel with the 3D-2D-MM priors. Numerical results show that our proposed algorithm achieves superior channel tracking performance to the state-of-the-art algorithms with lower pilot overhead and comparable complexity.

We are delighted to introduce this special section of the IEEE Transactions on Cognitive Communications and Networking (TCCN), which aims at addressing the evolution of cognitive radio (CR) to intelligence radio and networks by exploring recent advances in artificial intelligence (AI) and machine learning (ML). We have selected 14 articles for this special section after a rigorous review process, which are briefly discussed as follows.

A design criterion for a compact 3D printed high gain corrugated plate antenns that has high aperture efficiency and wide bandwidth is presented in this paper. The proposed design criterion is validated numerically and experimentally by fabricating 3D printed and Aluminium prototypes for X-band and Ka-band applications. The proposed antenna structure consists of two layers, where the electromagnetic energy (EM) is launched into a cavity that exists between both layers and the EM energy is coupled to the surface of the second layer. The second layer is the radiating structure which consists of three slots surrounded by a rectangular cavity and periodic corrugations that significantly improve the gain of the antennas. The 3D printed prototypes of the proposed antennas are fabricated and tested to validate the proposed design criterion, and their performance is compared to the Aluminium metallic counterparts. Using 3D printing technology, to fabricate the proposed antennas offer low cost and low weight alternatives to the Aluminium metallic prototypes. The measured results of the fabricated prototypes show high gain, high aperture efficiency, low side lobe level, and low cross polarization performance over a wide bandwidth.

It has been 20 years since the concept of cognitive radio (CR) was proposed, which is an efficient approach to provide more access opportunities to connect massive wireless devices. To improve the spectrum efficiency, CR enables unlicensed usage of licensed spectrum resources. It has been regarded as the key enabler for intelligent communications. In this article, we will provide an overview on the intelligent communication in the past two decades to illustrate the revolution of its capability from cognition to artificial intelligence (AI). Particularly, this article starts from a comprehensive review of typical spectrum sensing and sharing, followed by the recent achievements on the AI-enabled intelligent radio. Moreover, research challenges in the future intelligent communications will be discussed to show a path to the real deployment of intelligent radio. After witnessing the glorious developments of CR in the past 20 years, we try to provide readers a clear picture on how intelligent radio could be further developed to smartly utilize the limited spectrum resources as well as to optimally configure wireless devices in the future communication systems.

The book provides a comprehensive introduction to the key techniques and technologies that help to achieve autonomous space systems for cost-effective, high performing planetary robotic missions. Main topics covered include robotic vision, surface navigation, manipulation, mission operations and autonomy, being explained in both theoretical principles and practical use cases. The book recognizes the importance of system design hence discusses practices and tools that help take mission concepts to baseline design solutions, making it a practical piece of scientific reference suited to a variety of practitioners in planetary robotics.

In addition to its utility in terrestrial-based applications, Automated Planning and Scheduling (P&S) has had a growing impact on space exploration. Such applications require an influx of new technologies to improve performance while not comprimising safety. As a result, a reliable method to rapidly assess the effectiveness of new P&S algorithms would be desirable to ensure the fulfillment of of all software requirements. This paper introduces RoBen, a mission-independent benchmarking tool that provides a standard framework for the evaluation and comparison of P&S algorithms. RoBen considers metrics derived from the model (the system on which the P&S algorithm will operate) as well as user input (e.g., desired problem complexity) to automatically generate relevant problems for quality assessment. A thorough description of the algorithms and metrics used in RoBen is provided, along with the preliminary test results of a P&S algorithm solving RoBen-generated problems.

This paper presents an analysis on performance of an ultra dense network (UDN) with and without cell cooperation from the perspective of network information theory. We propose a UDN performance metric called Total Average Geometry Throughput which is independent from the user distribution or scheduler etc. This performance metric is analyzed in detail for UDN with and without cooperation. The numerical results from the analysis show that under the studied system model, the total average geometry throughput reaches its maximum when the inter-cell distance is around 6 ~ 8 meters, both without and with cell cooperation. Cell cooperation can significantly reduce inter-cell interference but not remove it completely. With cell cooperation and an optimum number of the cooperating cells the maximum performance gain can be achieved. Furthermore, the results also imply that there is an optimum aggregate transmission power if considering the energy cost per bit.

In a cognitive radio (CR) system, cooperative spectrum sensing (CSS) is the key to improving sensing performance in deep fading channels. In CSS networks, signals received at the secondary users (SUs) are sent to a fusion center to make a final decision of the spectrum occupancy. In this process, the presence of malicious users sending false sensing samples can severely degrade the performance of the CSS network. In this paper, with the compressive sensing (CS) technique being implemented at each SU, we build a CSS network with double sparsity property. A new malicious user detection scheme is proposed by utilizing the adaptive outlier pursuit (AOP) based low-rank matrix completion in the CSS network. In the proposed scheme, the malicious users are removed in the process of signal recovery at the fusion center. The numerical analysis of the proposed scheme is carried out and compared with an existing malicious user detection algorithm.