Federated learning (FL) assisted unmanned aerial vehicle (UAV) networks have been considered as a promising technology for the future Internet of Things (IoT) scenarios, where both the communication cost and computational cost can be reduced. In this paper, a FL assisted UAV networks are considered for performing image classification tasks in IoT, where an aerial base station (ABS) is deployed as the aggregation center and UAVs are deployed as the local entities. Firstly, the system performance is analyzed in terms of algorithm convergence and energy consumption. Then, we propose a link adaptive approach for the FL assisted UAV networks, where a link quality selection scheme and a model parameter selection scheme are conceived at the ABS and each UAV, respectively. Finally, the system performance is evaluated by simulation results, showing that the proposed link adaptive approach is capable of attaining a faster convergence performance and a higher classification accuracy compared to the conventional FL scheme.

An orthogonal time sequency multiplexing (OTSM) scheme using practical signaling functions is proposed under strong phase noise (PHN) scenarios. By utilizing the transform relationships between the delay-sequency (DS), time-frequency (TF) and time-domains, we first conceive the DS-domain input-output relationship of our OTSM system, where the conventional zero-padding is discarded to increase the spectral efficiency. Then, the unconditional pairwise error probability is derived, followed by deriving the bit error ratio (BER) upper bound in closed-form. Moreover, we compare the BER performance of our OTSM system based on several practical signaling functions. Our simulation results demonstrate that the upper bound derived accurately predicts the BER performance in the case of moderate to high signal-to-noise ratios (SNRs), while harnessing practical window functions is capable of attaining an attractive out-of-band emission (OOBE) vs. BER trade-off.

In this paper, we investigate an anti-jamming hybrid beamforming (HBF) design in millimeter-wave (mmWave) massive MIMO systems for reliable wireless communications. Different from the conventional schemes designed by assuming perfect channel state information (CSI) of both communication channels and jamming channels. We aim to design the HBF scheme by minimizing the dispersion of the output signals in statistics without the knowledge of the jamming channel information. Two partially-connected HBF architectures are considered. Specifically, we propose a two-stage robust HBF design by solving the formulated non-convex problem. First, we introduce an auxiliary vector that represents the product of an analog beamformer (ABF) and a digital beamformer (DBF), and propose a pseudo-Newton method assisted beamforming algorithm by relaxing the non-convex constraint on the ABF. In the second stage, we separately optimize the ABF and DBF for two different partially-connected HBF architectures by proposing a matrix decomposition-based alternating minimization method. Finally, simulation results are provided for demonstrating the superiority of our proposed robust HBF schemes over other benchmark schemes by considering the security threat of potential jamming attacks, especially when the number of snapshots is small.

Millimeter-wave (mmWave) communication using lens antenna array has emerged as a promising techique for the next generation wireless communication systems, which has the advantage of considerably reducing the number of required radio-frequency (RF) chains. However, the beamspace channel estimation is a challenging problem due to the fact that the number of RF chains is much smaller than that of transmit/receive antennas at the base station (BS). In this paper, motivated by the sparsity structure of lens-antenna based beamspace channels, we first model beamspace channel estimation problem as a sparse signal recovery problem, which can be solved by the compressed sensing (CS) algorithms. Then, we propose a simplified learned approximate message passing (SL-AMP) network by pre-learning the prior parameters of beamspace channel. Specifically, we use the Wasserstein generative adversarial net (GAN) to learn the underlying channel distribution and generate the corresponding samples to address the lack of training data problem. Next we choose the Bernoulli Gaussian mixture distribution to capture the channel prior and derive the corresponding shrinkage function. Finally, simulations results show that the proposed SL-AMP network can achieve relatively better performance with much lower training complexity compared with the existing LAMP network.

In this paper, a three-dimensional (3D) geometry-based stochastic model (GBSM) empowered by reconfigurable intelligent surface (RIS) is presented for multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) communication systems. Owing to the channel non-stationarity, spherical wavefront, and antenna configurations in RIS-empowered V2V channel, the geometry-based channel models suffer from high computational complexity, thereby leading to high hardware burden. To address this issue, a novel beam domain channel model (BDCM) is generated from the proposed geometry-based channel model through a beamforming operation based on discrete Fourier transform (DFT). To describe the non-stationarities of the V2V channels empowered by RIS, the channel model presented in this paper introduces real-time velocities and accelerations to capture the motion features of the communication terminals. The propagation characteristics including spatial cross-correlation functions (CCFs), temporal autocorrelation functions (ACFs), frequency correlation functions (FCFs), and channel capacities of the proposed communication system are derived and discussed. Some comparisons between the propagation characteristics of the proposed GBSM and those based on BDCM with respect to the different physical parameters of RIS and different environmental variables are investigated. Furthermore, numerical results indicate that the proposed channel model works well by changing the velocity parameters in different motion states.

In orthogonal time sequency multiplexing (OTSM) modulation, the information symbols are conveyed in the delay-sequency domain upon exploiting the inverse Walsh Hadamard transform (IWHT). It has been shown that OTSM is capable of attaining a bit error ratio (BER) similar to that of orthogonal time-frequency space (OTFS) modulation at a lower complexity, since the saving of multiplication operations in the IWHT. Hence we provide its BER performance analysis and characterize its detection complexity. We commence by deriving its generalized input-output relationship and its unconditional pairwise error probability (UPEP). Then, its BER upper bound is derived in closed form under both ideal and imperfect channel estimation conditions, which is shown to be tight at moderate to high signal-to-noise ratios (SNRs). Moreover, a novel approximate message passing (AMP) aided OTSM detection framework is proposed. Specifically, to circumvent the high residual BER of the conventional AMP detector, we proposed a vector AMP-based expectation-maximization (VAMP-EM) detector for performing joint data detection and noise variance estimation. The variance auto-tuning algorithm based on the EM algorithm is designed for the VAMP-EM detector to further improve the convergence performance. The simulation results illustrate that the VAMP-EM detector is capable of striking an attractive BER vs. complexity trade-off than the state-of-the-art schemes as well as providing a better convergence. Finally, we propose AMP and VAMP-EM turbo receivers for low-density parity-check (LDPC)-coded OTSM systems. It is demonstrated that our proposed VAMP-EM turbo receiver is capable of providing both BER and convergence performance improvements over the conventional AMP solution.

In this paper, we propose a three-dimensional (3D) physics-based double reconfigurable intelligent surface (RIS) cooperatively assisted multiple-input multiple-output (MIMO) stochastic channel model for unmanned aerial vehicle (UAV)-to-ground communication scenarios. The double-RIS is distributed on the surface of buildings can assist the UAV transmitter to reflect its own signals to the ground receiver (GR), and simultaneously enhance the propagation by passive beamforming on the RISs. In the proposed channel model, we derive thepath power gainsfor different propagation links between the UAV and GR, thus enabling the proposed channel model to effectively characterize both large- and small-scale fading characteristics of realistic UAV-to-ground communication systems. Furthermore, we derive the critical propagation properties of the proposed channel model, including the spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), and frequency correlation functions (FCFs), with respect to different RIS reflection phase configurations as well as different RIS orientation angles. Numerical simulation results demonstrate the propagation characteristics of the proposed double-RIS assisted UAV-to-ground channel model behave better than those of traditional channel models with single-RIS link or LoS link, thereby validating the necessity of introducing double-RIS into UAV-to-ground communication.

This paper studies the reconfigurable intelligent surface (RIS)-enhanced channel estimation and device activity detection technique for the next generation massive internet of things (IoT) networks. Thanks to its low cost, RIS can be introduced into massive IoT networks to extend the area coverage and support more online devices. However, introducing RIS also brings new challenges in channel estimation and device detection for massive connectivity systems owning to the resulting cascaded channel and its inherent passive characteristics. To address this issue, we first formulate the RIS-aided channel estimation and device activity detection as a joint sparse signal recovery problem by simultaneously exploring the sparsity of sporadic transmission and RIS-aided channel links. After that, an RIS-aided generalized Turbo multiple measurement vector algorithm to estimate the channels between the devices and the base station, and detect the active devices jointly under different channel distributions, i.e., the Bernoulli Gaussian scale mixture distribution and the Bernoulli Gaussian approximation distribution. Furthermore, we analyze the state evolution equations of the proposed channel estimation technique and the theoretical detection results from the perspective of missing detection and false alarm probabilities are also provided. Numerical results confirm the correctness of the theoretical analysis, and show that RIS is beneficial for improving the mean square error performance of the channel estimators, as well as the active device detection performance of detectors in massive connectivity systems.

Space-time shift keying-aided orthogonal time frequency space modulation-based multiple access (STSK-OTFS-MA) is proposed for reliable uplink transmission in high-Doppler scenarios. As a beneficial feature of our STSK-OTFS-MA system, extra information bits are mapped onto the indices of the active dispersion matrices, which allows the system to enjoy the joint benefits of both STSK and OTFS signalling. Due to the fact that both the time-, space- and DD-domain degrees of freedom are jointly exploited, our STSK-OTFS-MA achieves increased diversity and coding gains. To mitigate the potentially excessive detection complexity, the sparse structure of the equivalent transmitted symbol vector is exploited, resulting in a pair of low-complexity near-maximum likelihood (ML) multiuser detection algorithms. Explicitly, we conceive a progressive residual check-based greedy detector (PRCGD) and an iterative reduced-space check-based detector (IRCD). Then, we derive both the unconditional single-user pairwise error probability (SU-UPEP) and a tight bit error ratio (BER) union-bound for our single-user STSK-OTFS-MA system employing the ML detector. Furthermore, the discrete-input continuous-output memoryless channel (DCMC) capacity of the proposed system is derived. The optimal dispersion matrices (DMs) are designed based on the maximum attainable diversity and coding gain metrics. Finally, it is demonstrated that our STSK-OTFS-MA system achieves both a lower BER and a higher DCMC capacity than its conventional spatial modulation (SM) and its orthogonal frequency-division multiplexing (OFDM) counterparts. As a benefit, the proposed system strikes a compelling BER vs. system complexity as well as BER vs. detection complexity trade-offs.

Reconfigurable intelligent surface (RIS)-assisted communications has been a hot topic due to its promising advantages for future wireless networks. Existing works on RIS-assisted channel modeling have mainly focused on far-field propagation condition with planar wavefront assumption. In essence, the far-field condition does not always hold because the RIS array dimension may be comparable to the terminal distance, especially in RIS-assisted mobile networks. To this end, we propose a hybrid far- and near-field stochastic channel model for characterizing a RIS-assisted vehicle-to-vehicle (V2V) propagation environment, which takes into account both far-field and near-field propagation conditions. To achieve the balance between the modeling accuracy and complexity for the investigation of the RIS-assisted V2V propagation characteristics, we develop a sub-array partitioning scheme to dynamically divide the entire RIS array into several smaller sub-arrays, which makes planar wavefront assumption applicable for the sub-arrays. Important channel statistical properties, including spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), and frequency correlation functions (FCFs), are derived and investigated. Simulation results are provided to show the performance of the proposed sub-array partition based hybrid far- and near-field modeling solution for RIS-assisted V2V channels.

Non-orthogonal multiple access (NOMA) based fog radio access networks (F-RANs) offer high spectrum efficiency, ultra-low delay, and huge network throughput, and this is made possible by edge computing and communication functions of the fog access points (F-APs). Meanwhile, caching-enabled F-APs are responsible for edge caching and delivery of a large volume of multimedia files during the caching phase, which facilitates further reduction in the transmission energy and burden. The need of the prevailing situation in industry is that in NOMA-based F-RANs, energy-efficient resource allocation, which consists of cache placement (CP) and radio resource allocation (RRA), is crucial for network performance enhancement. To this end, in this paper, we first characterize an NOMA-based F-RAN in which F-APs of caching capabilities underlaid with the radio remote heads serve user equipments via the NOMA protocol. Then, we formulate a resource allocation problem for maximizing the defined performance indicator, namely network profit, which takes caching cost, revenue, and energy efficiency into consideration. The NP-hard problem is decomposed into two sub-problems, namely the CP sub-problem and RRA sub-problem. Finally, we propose an iterative method and a Stackelberg game based method to solve them, and numerical results show that the proposed solution can significantly improve network profit compared to some existing schemes in NOMA-based F-RANs.

Channel estimation is one of the main challenges for implementing reconfigurable intelligent surface (RIS)-aided communication system with a large number of antennas at the base station (BS) because RIS is equipped with many passive reflective elements without active transmitting/receiving and signal processing abilities. In this paper, we focus on the channel estimation in a general RIS-aided multi-user mmWave communication system. Specifically, a novel two-phase channel estimation scheme consisting of on-line and off-line phases is proposed to estimate the BS-RIS channel, RIS-user channel, and BS-user channel, respectively. Inspired by the characteristics of few scatterers in mmWave communication systems, the antenna domain channels are converted into the virtual angular domain channels by using the discrete Fourier transform (DFT) matrix. Thus, the estimation problem can be formulated as a compressed sensing (CS) problem, and solved by using efficient vector approximate message passing (VAMP) algorithm with expectation-maximization (EM) to learn unknown parameters and obtain the estimates simultaneously. Simulation results show that, the above algorithm with two choices of prior distributions in the proposed mmWave RIS-aided multi-user system has fast convergence speed and desirable estimation performance.

Reconfigurable intelligent surface (RIS) consisting of a large number of programmable near-passive units has been a hot topic in wireless communications due to its capability in providing smart radio environments to enhance the communication performance. However, the existing research are mainly based on simplistic channel models, which will, in principle, lead to inaccurate analysis of the system performance. In this paper, we propose a general three-dimensional (3D) wideband non-stationary end-to-end channel model for RIS assisted multiple-input multiple-output (MIMO) communications, which takes into account the physical properties of RIS, such as unit numbers, unit sizes, array orientations and array configurations. By modeling the RIS by a virtual cluster, we describe the end-to-end channel by a superposition of virtual line-of-sight (V-LoS), single-bounced non-LoS (SB-NLoS), and double-bounced NLoS (DB-NLoS) components. We also derive an equivalent cascaded channel model and show the equivalence between end-to-end and cascaded modeling of RIS channels. Then, a sub-optimal solution with low complexity is used to derive the RIS reflection phases. The impact of physical properties of RIS, such as unit numbers, unit sizes, array orientations, array configurations and array relative locations, on channel statistical characteristics has been investigated and analyzed, the results demonstrate that the proposed model is helpful for characterizing the RIS-assisted communication channels.

Index modulation (IM) has been recognized as a compelling candidate for the next generation wireless networks. This paper considers index modulation-orthogonal frequency division multiplexing (IM-OFDM) with single subcarrier activation, where a single subcarrier is activated for transmitting an amplitude-phase modulated (APM) symbol in each IM group. Furthermore, a reduced-complexity approximate message passing aided symbol detector (AMPD) is proposed for reducing the detection complexity at the receiver side. The system performance of IM-OFDM with single subcarrier activation is investigated in terms of spectral efficiency (SE), peak-to-average power ratio (PAPR), bit error ratio (BER), as well as detection complexity. Our studies show that IM-OFDM with single subcarrier activation is capable of attaining low PAPR. Moreover, IMOFDM with single subcarrier activation outperforms the classic OFDM using APM for a SE of 1 bit/s/Hz, when high power amplifier (HPA) is employed. Furthermore, our investigation shows that low detection complexity can be achieved by the proposed AMPD for IM-OFDM with single subcarrier activation at the cost of a certain BER performance loss.

For efficiently investigating the statistical properties of wideband multiple-input multiple-output (MIMO) channels for vehicle-to-vehicle (V2V) communication scenarios, we propose a novel computationally efficient solution to estimate the parameters of the proposed channel model for different propagation delays in this paper. To be specific, we first introduce a Unitary transformation method to estimate the propagation delay of the proposed channel model for the first tap in the preliminary stage before the mobile transmitter (MT) and mobile receiver (MR) move. Then, we estimate the real-time angular parameters based on the estimated delay and moving time/directions/velocities of the MT and MR. Furthermore, we estimate the expressions of the real-time complex channel impulse responses (CIRs), which can be used to characterize the physical properties of the proposed channel model, by substituting the estimates of the time-varying AoD and AoA and model parameters into the complex CIRs. Numerical results of the channel characteristics fit the theory results very well, which validate that the proposed channel model is practical for characterizing the beyond fifth-generation (B5G) V2V communication systems.

In this article, we propose a three-dimensional (3D) multi-input multi-output (MIMO) channel model for air-to-ground (A2G) communications in unmanned aerial vehicles (UAV) environments, where the UAV transmitter and ground receiver are in motion in the air and on the ground, respectively. A novel angular estimation algorithm is proposed to estimate the real-time azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) based on the non-stationary nature of the channel model. In the model, we investigate the time-varying spatial cross-correlation functions (CCFs) and temporal auto-correlation functions (ACFs) with respect to the different moving directions and velocities of the UAV transmitter and ground receiver. Furthermore, we derive and study the Doppler power spectral densities (PSDs) and power delay profiles (PDPs) of the proposed channel model. Numerical results show that characteristics of the proposed channel model are very close to those of practical measurements, which provide a new and practical approach to evaluate the performance of next generation UAV-MIMO communication systems.

Index modulation (IM) is an attractive concept for next generation communication systems. However, as an emerging technique, there are still some challenges need to be tackled in this frontier. In this paper, we consider two aspects of IM, which are diversity and detection complexity. Specifically, we propose a linear precoding assisted index modulation (LPIM) scheme for orthogonal frequency division multiplexing (OFDM) systems. We commence by analyzing the diversity and coding gains of the proposed scheme. Moreover, a detailed codebook design criterion is proposed. Then, our LPIM codebook is designed based on the maximum diversity and coding gain criteria. In contrast to the signaling model of the existing full diversity precoder designed for OFDM, we introduce a modeling method to link the zero-valued IM symbols to the origin of a Lattice for implementing our full diversity precoder designed for OFDM-IM. Both analytical and computer simulation results are provided for characterizing the attainable performance of OFDM-LPIM, demonstrating that it is capable of achieving full diversity as well as an attractive coding gain. However, the maximum-likelihood (ML) detection complexity of OFDM-LPIM is excessive, hence a low-complexity generalized iterative residual check detector (GIRCD) is proposed, which is inspired by the existing sparse recovery algorithms. Finally, computer simulation results are provided for demonstrating that GIRCD can provide a beneficial trade-off between bit error ratio performance and complexity.

With growing human maritime activities, supporting low-cost and high-speed information services for users at sea has become an imperative focus. In this paper, we consider a maritime relay multicast system including a shore-based base station and several offshore relay nodes, and propose an energy efficient relay design scheme. Specifically, we formulate the relay design problem as a power minimization problem under users' quality-of-service (QoS) constraints, and the problem is approximated and solved using the feasible point pursuit successive convex approach. Furthermore, an iterative algorithm is proposed with exponential complexity. In order to reduce the computational complexity, a low-complexity distributed algorithm is conceived and its closed-form solution is derived. Finally, simulation results show that our proposed scheme is beneficial in terms of achieving a higher communication rate as well as of yielding a better energy efficiency.

In order to beneficially exploit wireless spectral resources, spectrum sharing between communication systems and radar systems has become a popular research topic. However, traditional network association strategies may not result in an efficient co-system. We circumvent this problem by formulating a partially observable Markov decision process (POMDP) aided network association scheme. For maximizing the network throughput, whilst minimizing the interference imposed on the radar user, communication users are capable of adaptively selecting underlay or overlay access mode. Moreover, a near-optimal reinforcement learning algorithm is proposed considering both the computational complexity and feasibility. Finally, simulations are conducted in order to evaluate the effectiveness of our proposed POMDP based network association scheme.

Terrestrial-satellite networks (TSNs) play a significant role in achieving 100\% geographic coverage in the next generation of wireless networks. In TSNs, multimedia transmission is an important application scenario, where efficient content delivery solutions are required for effectively alleviating network congestion. In this paper, we study multicast beamforming problems in TSNs by reusing the entire bandwidth for providing efficient solutions for content delivery. In order to mitigate the interference imposed by multicast transmission of the satellite, we formulate the cooperative multicast beamforming problems for the TSN under the quality of service (QoS) constraints. Then, according to different considerations, the semidefinite relaxation (SDR) method is applied for the beamforming design of the satellite, whilst the recently developed feasible point pursuit successive convex approximation (FPP-SCA) approach is adopted for tackling the beamforming design problem of the base station. The system performance is studied by simulation results. Our investigations show that our TSN is capable of attaining a sufficient high rate for supporting multimedia transmission.

The recently developed orthogonal frequency-division multiplexing relying on index modulation (OFDM-IM) is adopted to the in-vehicle power line communications (PLCs) in order to combat the deleterious effects of frequency-selective fading and impulsive noise, whilst improving the energy efficiency of data communications. Furthermore, the low density parity check (LDPC) coding is invoked to further enhance the reliability of in-vehicle PLCs, which is of particular importance in light-weight airborne vehicles. For aiding LDPC decoding, a reduced complexity soft-decision detection scheme is proposed. The performance of the LDPC-coded OFDM-IM system is studied by simulation, when assuming communications over in-vehicle PLC channels. Our studies show that LDPC-coded OFDM-IM is capable of combating frequency-selective fading, mitigating impulsive noise, as well as striking a compelling trade-off between the spectral efficiency and energy efficiency for in-vehicle PLCs.

Hidden semi‐Markov modelling of the asynchronous impulsive noise (IN) encountered in indoor broadband power line communications (PLCs) is investigated by considering the statistical distributions of both the inter‐arrival time and the duration of asynchronous IN components. Then, the bit error ratio (BER) of orthogonal frequency division multiplexing systems using Q ‐ary quadrature amplitude modulation is analysed with the aid of the proposed noise model, when communicating over dispersive indoor power line channels inflicting asynchronous IN in addition to the background noise. The authors’ simulation results confirm the accuracy of the analysis and quantify the impact of various factors on the achievable BER performance. The grave impact of asynchronous IN on indoor broadband PLCs suggests that efficient techniques have to be designed for mitigating its effects.