Unmanned aerial vehicles (UAVs) can use the collected data to perform machine-learning tasks and enhance their intelligence level. The distributed framework of federated learning (FL) is suitable for resource-constrained UAV swarms. However, the dynamic channel conditions caused by the mobility of the UAVs impact the wireless FL performance. Additionally, data insufficiency and heterogeneity also affect the performance of the trained models. Thus, in this paper, we propose a joint training, trajectory, and beamforming optimization for an energy-efficient FL scheme that leverages the mobility of UAVs to enhance the model training efficiency, namely TTBO-FL. We adopt a two-level FL for the UAV network, where a high-level UAV (H-UAV) is for model aggregation and a set of low-level UAVs (L-UAVs) is for model training. Considering the dynamic channel conditions, we design a three-dimensional uniform linear array (3D ULA) and implement 3D analog beamforming to increase communication efficiency among UAVs. Moreover, we introduce transfer learning techniques and regularization constraints to mitigate the problem of data insufficiency and heterogeneity. Then, we formulate an optimization problem for UAV trajectory planning, local epoch adjustment, and beamforming, and adopt a soft actor-critic (SAC)-based algorithm to solve it. The simulation results show that, compared to the baseline schemes, the proposed schemes achieve higher model accuracy and lower energy consumption for the UAV swarm.

Federated learning (FL) is suitable for unmanned aerial vehicles (UAVs) and ground devices to exchange model parameters periodically and learn a shared model without transmitting raw data. However, the existing UAV-assisted FL systems consider all learning phases to be equally important, which is inconsistent with the critical learning period (CLP) that exists in the FL training process, leading to significant overheads and inefficient resource utilization. Besides, data heterogeneity and insufficiency among devices also affect the performance of the trained models. To address the above issues, in this paper, we propose a CLP-aware FL framework that identifies unequally important learning stages and implements corresponding FL training strategies. Given the significant differences between global and local model parameter distributions in the early training epochs, we introduce a Federated Kullback-Leibler divergence (KLD) Norm (FKN) metric that measures the KLD between these distributions for efficient CLP detection. To capture the data drift caused by environmental shift in UAV swarms, we also develop a computationally efficient Federated Drift Norm (FDN) metric to enable online detection of CLP. We formulate an optimization problem for CLP-based participating device selection, UAV visit frequencies to different devices, and model aggregation period, then adopt a deep reinforcement learning (DRL)-based algorithm to solve it. Simulation results show that our strategy reduces energy consumption while maintaining model accuracy compared to baselines, i.e., CriticalFL, pFedBayes, and FedExp.

Distributed path planning for multiple unmanned aerial vehicles (UAVs) plays a significant role in data collection systems. However, insufficient collaboration among multiple UAVs and inadequate emergency response capability in the case of UAV failure will lead to longer collection time. To address these challenges, we put forward a set of novel distributed path planning schemes in plural data collection scenarios, aimed at minimizing the task completion time and enhancing robustness. Specifically, a distributed framework of multiple UAVs collaboration data collection system (MUC-DCS) is designed, which can effectively avoid repeated data collection and UAV collision through inter-UAV communication. In order to optimize the task completion time in the MUC-DCS, a distributed path planning algorithm based on multi-agent deep Q-networks (DPP-MDQN) is proposed. In addition, so as to improve the algorithm efficiency, the pheromone is set to represent the state information and reward function. Further, we develop an emergency response strategy and propose a distributed path planning algorithm for emergency response (DPP-ER), enabling response to sudden UAV failure as well as ensuring the robustness and timeliness of MUC-DCS. The simulation results show that DPP-MDQN is superior to existing distributed algorithms and reduces the task completion time, as well as DPP-ER can effectively handle the sudden emergency of UAV failure.

The unmanned aerial vehicle (UAV) aided mobile edge computing (MEC) network has attracted significant attention due to its enhanced computing power, reliable network connectivity, dynamic environment adaptability, which however still suffer from non-instantaneous channel reconstruction and wireless security threats. Therefore, this paper considers a reconfigurable intelligent surface (RIS) assisted UAV-MEC network, aiming to minimize UAV energy consumption by jointly optimizing task offloading rate, user scheduling coefficient, RIS phase and UAV trajectory with the constraints of secure offloading rate. Given the multi-variable coupling and non-convex nature of this optimization problem, we decouple it into three subproblems, which include the user scheduling and offloading ratio optimization, the RIS phase optimization, and the UAV trajectory optimization. For the fractional programming problem involving RIS phase optimization, the Dinkelbach algorithm is used to transform it into a parametric subtraction problem, thus obtaining a closed-form solution for the RIS phases. Furthermore, the successive convex approximation (SCA) algorithm is employed for the UAV trajectory optimization subproblem. Ultimately, a double-layer iterative optimization algorithm based on block coordinate descent (BCD) is proposed to solve the original non-convex optimization problem. Simulation results confirm its superior performance in saving UAV energy compared to other baseline schemes.

To support large-scale connectivity of massive machine-type communication (mMTC) devices in Industrial Internet of Things (IIoT) scenario, a grant-free (GF) transmission aided non-orthogonal multiple access (NOMA) system is considered in this paper, where the devices with diverse behavior characteristics coexist. In order to reflect the diversity of IIoT scenario, multiple types of MTC devices are taken into account, and the network environment is divided into stationary mode and overload mode based on the differences in behavior characteristics of devices. Further, the successful access probability maximization problem is established to obtain the priority-aware access strategy. To solve this problem, we propose a novel distributed Q-learning algorithm and an adaptive update (AdaUpdate) aided priority-aware deep Q network (PA-DQN) algorithm for the stationary mode and the overload mode, respectively. Simulation results demonstrate that the proposed algorithms achieve better performance than that of traditional reinforcement learning algorithms and random access algorithm in terms of overall access efficiency and satisfying the access requirements of emergency devices preferentially.

In this letter, we propose a deep learning-based method for the direction of arrival (DOA) estimation in the low signal-to-noise ratio (SNR) scenario. Specifically, the DOA estimation is modeled as a multi-label classification task, and a novel dual class token Vision Transformer (DCT-ViT) is designed to fit it. Different from the classical ViT architecture with a single class token, the DCT-ViT includes two class tokens which are located at the beginning and end of the latent vector sequence, respectively. This architecture enables enhanced information mining and feature extraction from the array signal data in order to improve the accuracy of DOA estimation. Furthermore, a single DCT-ViT model can accommodate different source numbers by leveraging a training dataset with different numbers of sources. Simulation results illustrate that our proposed method outperforms existing methods in the low SNR scenario, including classical model-based and other deep learning-based methods.

The power amplifier (PA) is an indispensable component in wireless communication systems, while the nonlinearity induced by PA can lead to significant performance degradation. The conventional nonlinearity equalization (NLE) method can effectively mitigate the nonlinear effects and provide superior BER performance but requires intensive computational complexity. To this end, we propose a novel NLE method in the time domain, which can significantly reduce the computational complexity without sacrificing the BER performance. Specifically, we first propose a novel PA representation of the sum of products (SPs), which is a unified time-domain representation for several typical memory and memoryless PA models. On this basis, the SPs-iterative least square equalizer (SPs-ILSE) method is proposed to mitigate the impact of both the memory and memoryless PA’s nonlinear distortions at the receiver side. The computational complexity of complex multiplication (CCCM) in the proposed method isO(NlogN) for the memoryless PA models andO(KN2) for the memory PA models. Moreover, considering the commonly utilized PA models, we also derive the closed-form expression for the achievable SINR of the SPs-ILSE method in the ideal conditions. Numerical results show that (i) the closed-form SINR expression is valid for both the memory and memoryless scenarios (ii) the proposed method exhibits the superior bit error rate (BER) performance in comparison to several relevant nonlinear signal processing methods such as digital pre-distortion (DPD), and power amplifier nonlinearity cancellation (PANC) (iii) the proposed NLE method achieves the same BER performance as the previous NLE method, i.e., reconstruction of distorted signals (RODS), while the CCCM of the proposed method is much lower.

The massive multi-input multi-output (mMIMO) systems are vulnerable to both pilot contamination and pilot spoofing attack (PSA), which jeopardize the uplink channel estimation and cause information leakage in the downlink transmissions. Motivated by the canonical large-scale fading precoding (LSFP) [1], a secure LSFP is proposed to eliminate the impact of the coexistence of pilot contamination and PSA. The proposed framework enables multi-cell coordinated transmissions leveraging the large-scale fading coefficients, thus is suitable to be implemented in mMIMO systems with limited fronthaul. Specifically, the presence of the eavesdropper is first identified by designing a distributed mixture-of-experts neural network (D-MoENN)-based PSA detector, which combines the detected results of distributed nodes to improve the detection accuracy. Subsequently, an optimal jamming base station (BS) is selected by designing a D-MoENN-based localizer, which estimates the locating cell of the eavesdropper and selects the nearest jamming BS to the eavesdropper. Numerical results show that the proposed D-MoENN-based PSA detector outperforms the existing detectors in the low SNR regime. Moreover, the average secrecy rate achieved by the secure LSFP with the jamming BS selected by the D-MoENN-based localizer is close to the upper bound achieved by the genie-aided selector that perfectly knows the location of the eavesdropper.

In this paper, we investigate the trainable-step-size least mean square (TSS-LMS) algorithm, which combines the LMS algorithm model with deep learning for the direction of arrival (DOA) estimation using adaptive filtering. Although the existing variable-step-size LMS approaches have been proposed to improve the DOA estimation accuracy, they are generally unsuitable for scenarios with the limited number of snapshots. Therefore, we propose a deep learning-based algorithm driven by the LMS model in this work, which incorporates the trainable step size. More specifically, the iterative mathematical model based on the LMS algorithm is unfolded into a deep network, where each iteration corresponds to a layer of this network. Based on this structure, the iteration step size in each layer is set as a trainable variable, allowing for adaptation during the training of the network. Through deep learning with the received signal dataset, the TSS-LMS network improves the adaptability and DOA estimation accuracy of the LMS algorithm for the limited snapshots scenario. Simulation results show that our proposed method is effective and outperforms existing algorithms in terms of DOA estimation performance and computational complexity trade-off.

Intelligent reflective surfaces (IRSs) as low energy consumption and easy to attach devices have been widely applied in the field of anti-jamming recently. In particular, the combination of IRS and unmanned aerial vehicle (UAV), as IRS-UAV, further expands the scope of IRS services. In this article, the joint IRS selection and beamforming optimization problem has been investigated in multiple IRS-UAV-assisted anti-jamming D2D networks. To solve the above optimization problem, a distributed matching-based selection and$Q$-learning-based beamforming optimization algorithm (DMQ) was proposed. In detail, the optimization problem is decomposed into two subproblems, namely, the IRS selection subproblem is formulated as a noncommutative many-to-many matching game model to describe peer effects and uncertainty selection quotas, and the passive beamforming optimization subproblem is solved by a reinforcement algorithm to satisfy the complex environment. Numerical simulations confirm the convergence and near-optimal performance of the proposed scheme with lower latency and greater robustness.

In this paper, physical layer security techniques are investigated for cooperative multi-input multi-output (C-MIMO), which operates as an underlaid cognitive radio system that coexists with a primary user (PU). The underlaid secrecy paradigm is enabled by improving the secrecy rate towards the C-MIMO receiver and reducing the interference towards the PU. Such a communication model is especially suitable for implementing Industrial Internet of Things (IIoT) systems in the unlicensed spectrum, which can trade off spectral efficiency and information secrecy. To this end, we propose an eigenspace-adaptive precoding (EAP) method and formulate the secrecy rate optimization problem, which is subject to both the single device power constraint and the interference power constraint. This precoder design is enabled by decomposing the original optimization problem into eigenspace selection and power allocation sub-problems. Herein, the eigenvectors are adaptively selected by the transmitter according to the channel conditions of the underlaid users and the PUs. In addition, a simplified EAP method is proposed for large-dimensional C-MIMO transmission, exploiting the additional spatial degree of freedom for a low-complexity secrecy precoder design. Numerical results show that by transmitting signal and artificial noise in the properly selected eigenspace, C-MIMO can eliminate the secrecy outage and outperforms the fixed eigenspace precoding methods. Moreover, the proposed simplified EAP method for the large-dimensional C-MIMO can significantly improve the secrecy rate.

The integration of mobile-edge computing (MEC) and energy harvesting (EH) can potentially improve the network performances and prolong the battery life of the device. In this article, we study the resource management problem in the device-to-device (D2D)-aided wireless powered MEC networks where one device can forward or execute computation data for other devices with its resources. Our problem seeks to optimize the computation offloading strategy, transmission power, energy transmit power, as well as CPU speed to maximize the long-term utility energy efficiency (UEE). UEE is defined as the achieved computation data per unit energy. Since the formulated problem is in fractional form and hard to solve, we employ the Dinkelbach algorithm to transform the problem into a parametric subtractive form. Furthermore, considering that the formulated problem is time varying and stochastic due to the dynamic task arrival rate and battery level, we transform the long-term problem into deterministic drift-plus-penalty subproblems for each time slot by introducing virtual queues and adopting the Lyapunov optimization theory. The proposed scheme can balance the optimal UEE and stable data queue by introducing the control parameter$V$. Theoretically, we reveal the tradeoff between the UEE and stable queue length for wireless powered MEC systems as$[O(1/V), O(V)]$. Finally, the simulations illustrate the efficiency of the proposed scheme compared with the existed work in terms of the UEE, stable queue length, and battery level.

In this letter, we explore the problem of DOA estimation using neural networks for two closely spaced sources. Since the traditional high-resolution techniques based on classical algorithms cannot achieve high-accuracy DOA estimation in the presence of two closely spaced sources, especially at low signal-to-noise ratios (SNR), we propose a novel DOA estimation method based on a cascaded neural network to address this problem. Specifically, this network comprises two parts: the SNR classification network and the DOA estimation network. The latter network contains two estimation subnetworks, which are appropriate for different SNRs by training with noisy data and activated by the output of the SNR classification network. Simulation results demonstrate that the estimation performance of our proposed method achieves much better than that of the existing algorithms under various conditions, especially for the scenes with low SNRs or small snapshot number.

In this paper, we investigate the secrecy performance of energy-constrained wireless-powered networks with considering the passive eavesdropping scenario, where the simultaneous wireless information and power transfer-based full-duplex self-jamming (SWIPT-FDSJ) scheme is developed. The maximal ratio transmission protocol is applied at the multi-antenna source such that the wireless signals are designated to the destination directly. Besides, the energy harvesting and full-duplex self-jamming operations are adopted at the energy-constrained destination to prolong its lifetime as well as to confuse the eavesdropper. Specifically, the exact and asymptotic closed-form expressions of the connection outage probability (COP), the secrecy outage probability (SOP), and the secrecy throughput of the proposed system are obtained, based on which we optimize the time-switching ratio to maximize the secrecy throughput. We also degenerate the proposed SWIPT-FDSJ scheme to the reduced half-duplex with no self-jamming (HDNSJ) scheme. The finds suggest that in the HDNSJ scheme, adding the antenna number of the source only benefits the COP performance, but has no impact on the SOP performance. By contrast, it will promote the COP and SOP performance at the same time in the SWIPT-FDSJ scheme, which eventually results in the great improvement of secrecy throughput. In addition, we present the practical application condition of the SWIPT-FDSJ scheme. It is demonstrated that the secrecy throughput performance of the SWIPT-FDSJ scheme is much superior to the HDNSJ scheme on condition that the application condition is satisfied.

Orthogonal matching pursuit (OMP) and its improved algorithms are widely used as hybrid precoding solutions in millimeter wave massive MIMO systems. The existing modified hybrid precoding schemes reduce computational complexity, however, they cause the loss of spectral efficiency to some extent. In this paper, we propose a novel generalized orthogonal matching pursuit (gOMP) algorithm based on order-recursive least squares (ORLS) in order to balance both computational cost and spectral efficiency. Compared to OMP algorithm, the maximum iteration of gOMP-ORLS algorithm can be reduced by choosing more than one vector in each iteration. Meanwhile, it has much lower implementation complexity due to avoiding matrix inversion operation. More importantly, the realized spectral efficiency of the proposed algorithm is higher than other existing algorithms. The simulation results as well as our detailed analysis demonstrate that a) the proposed gOMP-ORLS algorithm can achieve the approximately same spectral efficiency as OMP algorithm; b) it can reduce computational complexity and improve precoding efficiency prominently.

The fifth generation (5G) networks have been envisioned to support the explosive growth of data demand caused by the increasing traditional high-rate mobile users and the expected rise of interconnections between human and things. To accommodate the ever-growing data traffic with scarce spectrum resources, cognitive radio (CR) is considered a promising technology to improve spectrum utilization. We study the power control problem for secondary users in an underlay CR network. Unlike most existing studies which simplify the problem by considering only a single primary user or channel, we investigate a more realistic scenario where multiple primary users share multiple channels with secondary users. We formulate the power control problem as a non-cooperative game with coupled constraints, where the Pareto optimality and achievable total throughput can be obtained by a Nash equilibrium (NE) solution. To achieve NE of the game, we first propose a projected gradient based dynamic model whose equilibrium points are equivalent to the NE of the original game, and then derive a centralized algorithm to solve the problem. Simulation results show that the convergence and effectiveness of our proposed solution, emphasizing the proposed algorithm, are competitive. Moreover, we demonstrate the robustness of our proposed solution as the network size increases.

This work studies the secrecy outage performance of buffer-aided dual-hop multi-antenna relay systems without eavesdropper's channel state information (CSI). By modeling the dynamic buffer state transitions with the Markov chain, the secrecy outage probability at each state is investigated and the stationary distribution probabilities of all states are subsequently derived. Using the total probability theorem, the closed-form expression of the secrecy outage probability of the system is finally obtained. It demonstrates that due to the fully exploitation of the available channels, the buffer-aided relay selection yields to better performance than Best Relay Selection (BRS), even when less relays and antennas are utilized. It is also shown that the buffer-aided relaying only results in a small performance degradation when the buffers are constrained to finite size, thus can be well applied to practical relaying cooperative networks. Simulation results are given to verify the theoretical analysis.

In this paper, we present a secrecy outage performance analysis of wireless powered communication networks with multiple eavesdroppers, where an energy-limited information source with multiple antennas harvests the radio frequency (RF) energy from a dedicated power beacon (PB) before transmission. To exploit the benefits of multiple antennas at source, two popular multi-antenna transmission schemes, i.e., maximal ratio transmission and transmit antenna selection, are investigated for two intercepting ways at Eves, i.e., non-colluding and colluding scenarios, respectively. Specifically, adopting the time-switching protocol at PB, we derive exact and asymptotic closed-form expressions of the secrecy outage probability for both two transmission schemes taking into account the outdated channel state information (CSI). From our analysis, several important concluding remarks are obtained as follows: a) Full secrecy diversity order can be achieved by both two transmission schemes with no feedback delay, however, it reduces to zero in the presence of feedback delay; b) MRT scheme always outperforms TAS scheme with no feedback delay. However, TAS scheme achieves a similar performance as MRT scheme or even better in moderate and even serious feedback delay conditions.

In this paper, we investigate the average throughput of a multi-antenna wireless powered communication network where an energy-constrained user harvests energy from a hybrid access-point (AP) equipped with multiple antennas in the downlink, and then transmits information to the AP in the uplink using the harvested energy. Specifically, we consider a more practical scenario, i.e., nonlinear energy harvester, as compared with the traditional linear model. In order to evaluate the key parameters, such as the transmit power, antenna numbers, time-splitting, channel fading severity, on the performance of the considered system, we derive closed-form expressions of the average throughput for both delay tolerant and delay intolerant transmission modes in Nakagami-m fading channel. In addition, to further exploit the insights on the application of the considered system, the asymptotic analysis for the achievable throughput are also provided in two special cases, i.e., high transmit power regime and high saturation threshold regime. Finally, our results demonstrate that the considered system exhibits the throughput saturation phenomenon, and the parameters of channel fading severity produce a different impact on the average throughput in the two transmission modes.

We propose a novel channel model for massive multiple-input multiple-out (MIMO) communication systems that incorporate the spherical wave-front assumption and non-stationary properties of clusters on both the array and time axes. Because of the large dimension of the antenna array in massive MIMO systems, the spherical wave-front is assumed to characterize near-field effects resulting in angle of arrival (AoA) shifts and Doppler frequency variations on the antenna array. Additionally, a novel visibility region method is proposed to capture the non-stationary properties of clusters at the receiver side. Combined with the birth-death process, a novel cluster evolution algorithm is proposed. The impacts of cluster evolution and the spherical wave-front assumption on the statistical properties of the channel model are investigated. Meanwhile, corresponding to the theoretical model, a simulation model with a finite number of rays that capture channel characteristics as accurately as possible is proposed. Finally, numerical analysis shows that our proposed non-stationary channel model is effective in capturing the characteristics of a massive MIMO channel.

In this paper, we investigate physical layer security for simultaneous wireless information and power transfer in amplify-and-forward relay networks. We propose a joint robust cooperative beamforming and artificial noise scheme for secure communication and efficient wireless energy transfer. Specifically, by treating the energy receiver as a potential eavesdropper and assuming that only imperfect channel state information can be obtained, we formulate an optimization problem to maximize the worst-case secrecy rate between the source and the legitimate information receiver under both the power constraint at the relays and the wireless power harvest constraint at the energy receiver. Since such a problem is non-convex and hard to tackle, we propose a two-level optimization approach which involves a one-dimensional search and semidefinite relaxation. Simulation results show that the proposed robust scheme achieves better worst-case secrecy rate performance than other schemes.

In this paper, we investigate the secrecy performance of full-duplex multi-antenna spectrum-sharing wiretap networks in which a jamming signal is simultaneously transmitted by the full-duplex secondary receiver (Bob) based on the zero forcing beamforming (ZFB) algorithm. For the security enhancement, we propose the two antenna reception schemes: 1) random selection combining (RSC) where Bob selects LB antennas at random to combine the received signals and 2) generalized selection combining (GSC) where Bob selects LB strongest antennas to combine the received signals. We derive the exact closed-form expressions for the secrecy outage probability of full-duplex multi-antenna spectrum-sharing wiretap networks with ZFB algorithm. In order to explore a new design of the proposed schemes, we provide tractable asymptotic approximations for the secrecy outage probability in high signal-to-noise ratio regime under two distinct scenarios. From the analysis, we demonstrate that: 1) when the main channel is much better than the eavesdropper's channel, GSC/ZFB scheme achieves full diversity NB, while RSC/ZFB scheme only achieves partial diversity LB and 2) GSC/ZFB scheme achieves better secrecy performance than RSC/ZFB with different antenna numbers at Bob.

This paper studies the achievable secrecy performance of multiple-input multiple-output wiretap channels in the presence of imperfect channel state information (CSI) with practical low-complexity transmission schemes. In particular, we propose a general order transmit antenna selection and power-efficient output-threshold maximal ratio combining scheme. Two separate cases depending on the availability of the eavesdropper's CSI at the transmitter are considered. New closed-form expressions of the secrecy outage probability and the average secrecy rate are obtained. In addition, the secrecy diversity order and array gains, high signal-to-noise ratio slope, and power offset are characterized through asymptotic analysis, which enables the characterization of the effect of imprecise transmit antenna selection, output-threshold, and imperfect CSI on the secrecy performance. Numerical results are presented to validate the main outcomes of this paper.

In this paper, we investigate the secrecy performance of dual-hop amplify-and-forward (AF) multi-antenna relaying systems over Rayleigh fading channels by taking into account the direct link between the source and destination. To improve the secrecy performance, two linear processing schemes at relay and maximal ratio combining (MRC) at destination are proposed, namely, Zero-forcing/MRC (ZF/MRC) and Maximal ratio transmission/MRC (MRT/MRC). For these schemes, we present new tight analytical expressions of the secrecy outage probability. In addition, we examine the performance in high signal-to-noise ratio (SNR) regimes, and present simple secrecy outage approximations for all schemes. The results reveal that: 1) The MRT/MRC scheme achieves a full diversity order of M+1, while the ZF/MRC scheme achieves a diversity order ofM, where M is the number of antennas at relay. 2) The ZF/MRC scheme outperforms the MRT/MRC scheme in the low SNR regime, while becomes inferior to the MRT/MRC scheme in the high SNR regime.

The optimal detection of generalized spatial modulation (GSM) technique is the maximum likelihood (ML) algorithm. However, the computational complexity of ML detection is high and increases dramatically with the increase of the number of transmit antennas and active antennas. To tackle this problem, we propose low-complexity suboptimum detectors by exploiting a special property of GSM-MIMO systems, i.e., the spatial constraint of active antennas. Specifically, the proposed detections are designed based on a greedy algorithm termed Multipath Matching Pursuit (MMP). Using the spatial constraint of active antennas, the proposed detections achieve better performance than that of the original MMP algorithm with lower computational complexity. Moreover, the numerical results are also provided to demonstrate the superiority of the proposed detections.

In this paper, we analyze the secrecy performance of multi-antenna cognitive wiretap network, where the secondary transmitter (Alice) communicates with the secondary receiver (Bob) in the presence of an eavesdropper (Eve). Specifically, we investigate the cases of maximal-ratio combining (MRC) with half-duplex (HD) and selection combining/Zero forcing beamforming (SC/ZFB) scheme with full-duplex (FD) operation, respectively. Assuming the Rayleigh fading, closed-form expressions for the secrecy outage probability of cognitive wiretap channels with MRC and SC/ZFB are derived. Furthermore, we provide simple asymptotic approximations for the secrecy outage probability and find that both schemes achieve full diversity. In addition, simulation results reveal that MRC outperforms SC/ZBF in the low interference threshold regime, while the opposite holds in the high interference threshold regime.

In this paper, we consider a multiuser downlink wiretap network consisting of one base station (BS) equipped with AA antennas, NB single-antenna legitimate users, and NE single-antenna eavesdroppers over Nakagami-m fading channels. In particular, we introduce a joint secure transmission scheme that adopts transmit antenna selection at the BS and explores threshold-based selection diversity scheduling over legitimate users to achieve a good secrecy performance while maintaining low implementation complexity. More specifically, in an effort to quantify the secrecy performance of the considered system, two practical scenarios are investigated, i.e.: in Scenario I, the eavesdropper's channel state information (CSI) is unavailable at the BS, and in Scenario II, the eavesdropper's CSI is available at the BS. For Scenario I, novel exact closed-form expressions for the secrecy outage probability are derived, which are valid for general networks with an arbitrary number of legitimate users, antenna configurations, number of eavesdroppers, and the switched threshold. For Scenario II, we take into account the ergodic secrecy rate as the principle performance metric, and derive novel exact closed-form expressions for the ergodic secrecy rate. In addition, we also provide simple and asymptotic expressions for secrecy outage probability and ergodic secrecy rate under two distinct cases, i.e.: in Case I, the legitimate user is located close to the BS, and in Case II, both the legitimate user and eavesdropper are located close to the BS. Our important findings reveal that the secrecy diversity order is AAmA and the slope of secrecy rate is one under Case I, while the secrecy diversity order and the slope of secrecy rate collapse to zero under Case II, where the secrecy performance floor occurs. Finally, when the switched threshold is carefully selected, the considered scheduling scheme outperforms other well-known existing schemes in terms of the secrecy performance and complexity tradeoff.

We investigate the secrecy performance of dual-hop amplify-and-forward multi-antenna relaying systems over Rayleigh fading channels, considering the direct link between the source and the destination. In order to exploit the available direct link and the multiple antennas for secrecy improvement, different linear processing schemes at the relay and different diversity combining techniques at the destination are proposed, namely: 1) zero-forcing/maximal ratio combining (ZF/MRC); 2) ZF/selection combining (ZF/SC); 3) maximal ratio transmission/MRC (MRT/MRC); and 4) MRT/SC. For all these schemes, we present new closed-form approximations for the secrecy outage probability. Moreover, we investigate a benchmark scheme, i.e., cooperative jamming/ZF (CJ/ZF), where the secrecy outage probability is obtained in exact closed-form. In addition, we present asymptotic secrecy outage expressions for all the proposed schemes in the high signal-to-noise ratio (SNR) regime, in order to characterize key design parameters, such as secrecy diversity order and secrecy array gain. The outcomes of this paper can be summarized as follows: 1) MRT/MRC and MRT/SC achieve a full diversity order of M + 1, ZF/MRC and ZF/SC achieve a diversity order of M, while CJ/ZF only achieves unit diversity order, where M is the number of antennas at the relay; 2) ZF/MRC (ZF/SC) outperforms the corresponding MRT/MRC(MRT/SC) in the low SNR regime, while becomes inferior to the corresponding MRT/MRC (MRT/SC) in the high SNR; and 3) all the proposed schemes tend to outperform the CJ/ZF with moderate number of antennas, and linear processing schemes with MRC attain better performance than those with SC.

In this paper, we investigate the secrecy performance of multiuser dual-hop relay networks where a base station (BS) communicates with multiple legitimate users through the assistance of a trustful regenerative relay in the presence of multiple eavesdroppers. In particular, the maximal ratio transmission scheme is exploited at the BS and a threshold-based multiuser scheduling scheme is employed over the legitimate users, while concerning the imperfect decoding at the regenerative relay. To evaluate the secrecy performance of the considered system, two practical situations are addressed based on the availability of eavesdropper's channel state information (CSI), i.e., Scenario I, where the eavesdropper's CSI is not available at the relay, and Scenario II, where the eavesdropper's CSI is available at the relay. For both the scenarios, we further consider two eavesdropping modes, i.e., colluding eavesdropping and non-colluding eavesdropping. For Scenario I, new exact and asymptotic closed-form expressions for the secrecy outage probability (SOP) are derived. For Scenario II, we derive new exact and asymptotic closed-form expressions for the ergodic secrecy rate (ESR). The asymptotic SOPs demonstrate that the secrecy diversity order is independent of the number of legitimate users NB and eavesdroppers NE, the number of antennas equipped at eavesdroppers AE, as well as the fading factor of the wiretap channel mE. Furthermore, we also determine the secrecy multiplexing gain and the power cost to explicitly quantify the impact of the legitimate channel and wiretap channel on the ESR. Our findings demonstrate that increasing the switching threshold, the number of antennas at the BS, and the number of legitimate users has a positive impact on secrecy performance.

In this paper, we propose general-order transmit antenna selection to enhance the secrecy performance of multiple-input-multiple-output multieavesdropper channels with outdated channel state information (CSI) at the transmitter. To evaluate the effect of the outdated CSI on the secure transmission of the system, we investigate the secrecy performance for two practical scenarios, i.e., Scenarios I and II, where the eavesdropper's CSI is not available at the transmitter and is available at the transmitter, respectively. For Scenario I, we derive exact and asymptotic closed-form expressions for the secrecy outage probability in Nakagami-m fading channels. In addition, we also derive the probability of nonzero secrecy capacity and the ε-outage secrecy capacity, respectively. Simple asymptotic expressions for the secrecy outage probability reveal that the secrecy diversity order is reduced when the CSI is outdated at the transmitter, and it is independent of the number of antennas at each eavesdropper NE, the fading parameter of the eavesdropper's channel mE, and the number of eavesdroppers M. For Scenario II, we make a comprehensive analysis of the average secrecy capacity obtained by the system. Specifically, new closed-form expressions for the exact and asymptotic average secrecy capacity are derived, which are valid for general systems with an arbitrary number of antennas, number of eavesdroppers, and fading severity parameters. Resorting to these results, we also determine a high signal-to-noise ratio power offset to explicitly quantify the impact of the main channel and the eavesdropper's channel on the average secrecy capacity.

In this study, a dual‐hop multiple‐input–multiple‐output relay network with the N th‐best relay selection scheme in the presence of co‐channel interference is studied. Specifically, the N th‐best relay is selected based on the channel state information (CSI) of the first hop. The authors first consider the CSI is perfect feedback and derive exact as well as asymptotic closed‐form expressions for the outage probability. Results reveal that the diversity order of N R × min{ N S ( K − N + 1), N D } is achieved when there is no feedback delay, where N S , N R and N D represent the number of antennas at the source, the relays and the destination, respectively, K is the number of the relays and N (1 ≤ N ≤ K ) represents the rank of relay chosen. Then, they investigate the outage performance of the system with feedback delay, and exact and asymptotic outage probability expressions are also obtained. Results illustrate that outdated CSI degrades the diversity order of the system to min{ N R , N D }, which is independent of the number of antennas at the source, the number of relays and the rank of the relay chosen. The findings of the study provide valuable insights into the practical system design.

This paper presents a comprehensive performance analysis of multiuser multiple antenna amplify-and-forward relaying networks employing opportunistic scheduling with feedback delay and co-channel interference over Rayleigh fading channels. Specifically, we derive exact as well as approximate closed-form expressions for the outage probability and average symbol error rate (SER) of the system. In addition, simple asymptotic expressions at the high signal-to-noise ratio (SNR) regime are obtained, which facilitate the characterization of the achievable diversity order and coding gain of the system. Moreover, two novel ergodic capacity bounds valid for general systems with arbitrary number of antennas and users are proposed. Finally, the optimum power allocation scheme in terms of minimizing the average SER is studied, and simple analytical solutions are obtained. Simulation results are provided to corroborate the derived analytical expressions, and it is demonstrated that the ergodic capacity bounds remain sufficiently tight across the entire range of SNRs and the proposed power allocation scheme offers significant improvements on the SER performance. The findings of the paper suggest that the full diversity order can only be achieved when there is ideal feedback, i.e., no feedback delay, and the diversity order always reduces to one in the presence of feedback delay. Also, the impact of key parameters such as the number of antennas and users on the system performance is intimately dependent on the level of feedback delay.

In this paper, we propose transmit antenna selection with maximal ratio combining (TAS/MRC) in dual-hop decode-and-forward spectrum-sharing relaying networks with the primary user's interference and outdated channel state information (CSI). In this network, a single antenna that maximizes the received SNR is selected at the secondary transmitter, and the MRC is adopted at the secondary receiver. To efficiently evaluate the impact of key parameters on the system performance, we derive the exact analytical expression for the outage probability of the secondary network in a Rayleigh fading channel. Moreover, we present simple asymptotic expressions for the outage probability in a high SNR regime, which reveal practical insights on the achievable diversity order and coding gain. The findings suggest that whether the outdated CSI concerning the secondary transmission links has significant impact on the outage probability of the system depends on the interference power constraint at primary receivers. Specifically, under the proportional interference power constraint, the achievable diversity order is affected by imperfect CSI regarding the secondary transmission links, and the diversity-multiplexing tradeoff is independent of the primary network. However, under the fixed interference power constraint, the error floor is displayed, and the achievable diversity order reduces to zero regardless of the CSI concerning the secondary transmission links.

In this paper, we investigate the ergodic capacity and the average symbol error probability (SEP) of uplink cognitive cellular networks with opportunistic scheduling in Nakagami-m fading channels. Considering the same opportunistic scheduling scheme as [1], we derive closed-form expressions for the ergodic capacity and the average SEP of the system. Depending on closed-form expressions, we further investigate the impact of various key system parameters, i.e., channel fading severity, primary user's target outage probability and primary user's transmission rate, on cognitive user's performance. Theoretical results, verified by simulations, about the ergodic capacity and the average SEP are expressed in terms of the Meijer's G-function and the confluent hypergeometric function of the second kind, respectively. From the simulations, we get that the ergodic capacity and the average SEP are independent of the number of cognitive users and the transmit power of primary user.