A covert ISAC transmission scheme based on an innovative heterogeneous sub-connected hybrid analog and digital (H2AD) multiple-input multiple-output (MIMO) transceiver is investigated in this paper. Specifically, H2AD ISAC system possesses the capability to detect the point-like target while covertly transmitting confidential information to a singleantenna legitimate user, and enabling secure transmission without detection by the warden. The objective is to maximize the covert transmission rate for legitimate users while adhering to the Cramér-Rao bound (CRB) threshold. However, due to the coupling of multiple variables under the H2AD transceiver framework, the optimization problem becomes non-convex. To tackle the challenging, an alternating optimization algorithm based on Dinkelbach’s transformation and semidefinite relaxation (DTSDR) is proposed to design the analog and digital beamforming along with the sensing signal. Then, by utilizing historical system states and optimizing for long-term returns, an improved distributional soft Actor-Critic with three refinements (DSACv2) algorithm framework based on deep reinforcement learning (DRL) is proposed. Simulation results demonstrate that incorporating the novel H2AD MIMO antenna array into ISAC system design enhances the covert performance while ensuring target sensing performance.

Next-generation wireless network should accomplish integrated sensing, communication, and computation (ISCC) capabilities. This paper proposes a novel covert transmission scheme based on active reconfigurable intelligent surface (RIS)-enabled full-duplex (FD) unmanned aerial vehicle (UAV)-ISCC framework, where the multi-functional UAV realizes simultaneous target sensing and uplink (UL) covert communication, as well as performing edge computing (EC) for users. To maximize the minimum covert transmission rate (CTR) among all UL users, UAV transmit beamforming and trajectory, RIS weights, power allocation and signal processing in a FD UL transmission system are jointly devised. To tackle the intractable non-convex problem, we leverage second order cone programming (SOCP), penalty-dual-decomposition (PDD) and successive convex approximation (SCA), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Simulation results show that by incorporating the active RIS and UAV techniques into the optimization design, the covert transmission performance of ISCC systems are improved while ensuring a certain level of target sensing and EC performance.

The upcoming generation of Internet of Things (IoT) technology requires the seamless integration of two fundamental modules: communication for information sharing and computation for data processing. In parallel, security and privacy are essential requirements for future IoT deployments. Thus, taking effective measures to address security concerns in complex IoT networks is evolving into a valuable research topic. Driven by these demands, we investigate the physical layer security robust resource allocation issue in cooperative relay-assisted integrated communication and over-the-air computation networks for IoT applications. Moreover, we consider a practical scenario where only imperfect channel state information is available. To guarantee the communication security between IoT sensors and the legitimate base station, we propose to jointly design the transmit and receive processing factors, along with relay amplification coefficients. Specifically, we formulate a robust non-convex optimization problem that maximizes the sum achievable secrecy rate, subject to transmit power constraints at sensors and relays, as well as the maximal tolerable computation error constraint of the legitimate base station. Subsequently, to address the intractably robust non-convex problem, we propose an efficient generalize Lagrangian dual transform based algorithm with cutting-set method. To demonstrate the robustness and effectiveness of our proposed algorithm, two benchmark algorithms are considered. The first is the weighted minimum mean square error based algorithm that incorporates the cutting-set method, while the second is the conservative approximation based algorithm. In comparison to the former, our proposed algorithm demonstrates faster convergence; in relation to the latter, it obtains superior performance.

In wireless communications with high mobility, an interpolation-based channel estimation scheme shows the feasibility of windowed orthogonal frequency division multiplexing (OFDM) systems, where severe inter-carrier interference (ICI) is mitigated by a wrap-around based frequency-varying Viterbi detection scheme. In the interpolation-based channel estimation, insertion of pilot and null subcarriers leads to a decrease in available spectral rate. To reduce null subcarriers and thus improve available spectral rate, we propose an iterative channel estimation and signal decoding scheme, which mitigates the ICI introduced by reduction of null subcarriers. In the proposed iterative scheme, an initial coarse channel estimation procedure is conducted before the iterative channel estimation and signal decoding procedure. Two procedures are skillfully designed to prevent error propagation. Furthermore, the properties of raised-cosine roll-off window function are exploited to design the proposed scheme, where repetition data transmissions are proposed to safeguard against decoding errors. Simulation results demonstrate that the available spectral rate increases at the price of tolerable performance degradation.

In this paper, we study a multiple-input-multiple-output (MIMO) non-orthogonal multiple access (NOMA) downlink system, where a base station employs beamforming to transmit multiple data streams simultaneously to a central user and a cell-edge user. Over each beam, the orthogonal frequency division multiplexing (OFDM) modulated signals intended for both users are superimposed and then transmitted. Because of beamforming and OFDM modulation, the central user may have weaker beams compared with the cell-edge user. This causes the unsuccessful successive interference cancellation (SIC). To solve this problem, we derive the necessary and sufficient conditions to ensure the successful SIC for arbitrary beamforming matrices. Based on these conditions, we design a specialized beamforming structure where signals over weak beams can be orthogonally separated from those over strong ones. We propose that joint encoding and decoding are applied to signals over weak beams and similarly to those over strong ones. To optimize the beamforming matrices, we propose a constrained convex concave procedure based algorithm and a matrix fractional programming based algorithm. It is verified through simulation results that the proposed scheme performs better than the conventional OFDM-NOMA scheme.

In future networks, the integrated sensing, communication and computation (ISCC) has gradually become a research hotspot. In this paper, we investigate a novel computation resource allocation scheme for reconfigurable intelligent surfaces (RIS) backscatter communication (BackCom)-aided ISCC system. We consider the joint design of transmit beamforming at BS and the reflecting coefficients at RIS as well as the computation resource allocation of each user. The optimization problem for the max-min computation efficiency (CE) under the constraints of power consumption, the Cramér-Rao bound (CRB) for angles estimation and communication requirement of each user is formulated. To deal with the intractable optimization problem, the block coordinate descent (BCD) algorithm is utilized to tackle the joint optimization problem. We propose the penalty function-based successive convex approximation (SCA) method to optimize the reflecting coefficients and the majorization-minimization (MM) framework to design the transmit beamforming, respectively. In addition, considering the high complexity of the proposed SCA based algorithm, we design a low-complexity beamforming and reflection coefficient scheme for a special case of single target scenario. Simulation results show that the introduction of RIS-BackCom can improve the efficiency of computing and maintain the tradeoff between CE and sensing performance.

The flexibility and maneuverability of unmanned aerial vehicles (UAVs) lend themselves to tracking users and operating as an aerial base station carrying out communication enhancement functionality. A core challenge neglected by most existing works is that the true-but-unknown obstacles can jeopardize UAV flight security and shadow its communication links with users, resulting in poor achievable rate and high collision risks. In this paper, a deep-reinforcement-learning (DRL)-based UAV target tracking and digital-twin (DT)-assisted collision avoidance method is proposed to optimize UAV’s communication performance while tracking moving users. Toward this end, Twin Delayed Deep Deterministic policy gradient (TD3) as a novel and policy-based DRL algorithm is used to construct an agent responsible for adaptive deciding UAV flying control actions. To efficiently detect unknown obstacles in a flight environment, an orthogonal frequency division multiple (OFDM)-based integrated sensing and communication (ISAC) system is investigated, endowing UAV’s agent with real-time obstacle distance. Finally, we present a DT obstacle model construction mechanism and integrate it with TD3 agent training. The extensive simulations demonstrate the reward convergence of the TD3 algorithm and the communication improvement with stable user tracking and reliable collision avoidance, compared with conventional approaches.

With the rapid evolution of wireless technologies, the deep integration of sensing, communication and computation has heralded a novel and promising paradigm. In this paper, we propose a secure beamforming design framework for integrated sensing, non-orthogonal multiple access (NOMA) communication and over-the-air computation (AirComp) networks, which can provide multi-functional intelligent services for communication-intensive, computation-intensive, delay-sensitive and security-sensitive applications. In the considered network, each dual-functional intelligent device engages in NOMA information transmission and AirComp. Meanwhile, the triple-functional base station conducts target sensing, NOMA signal decoding and data aggregation simultaneously. Our aim is to maximize the sum secrecy rate (SSR) of NOAM devices while ensuring that the quality of service requirements for both sensing and AirComp are met within the transmit power constraints imposed on all nodes. The formulated optimization problem involves coupled variables and logarithmic determinant, thus it is highly non-convex. To solve it, we propose an efficient matrix-extended generalized Lagrangian dual transformation based algorithm with penalty method, which can obtain the Karush-Kuhn-Tucker (KKT) solution to the original problem with low-complexity and convergence guarantee. Additionally, the well-known successive convex approximation based algorithm is also employed to address the formulated SSR maximization problem. However, its computational complexity significantly exceeds that of our proposed algorithm. Finally, extensive experiments demonstrate the performance improvement of our proposal compared with the benchmark approaches.

Vehicle-to-everything (V2X) applications are usually powered by vehicular batteries and thus are power limited in general. Reconfigurable intelligent surfaces (RISs) are capable of improving the spectral efficiency and conserving energy of the wireless communications, due to the planar array architecture of which is superior beamforming gain and energy-efficient. In this paper, we study a novel design scheme where a double-RIS-enabled integrated sensing and communication (ISAC) system in vehicular network performs both a single target sensing and multi-user communications synchronously. Specifically, two transmit power budget minimization problems are formulated based on Cramér-Rao bound (CRB)-based framework under the known target location model, and radar signal-to-noise ratio (SNR)-related framework under the uncertain target location model, respectively. For the former, we propose an efficient solver based on alternative direction method of multipliers (ADMM) technique to obtain high-quality solutions for transmit beamforming and phase shifts. For the latter, an efficient algorithm based on penalty-dual-decomposition (PDD) and second order cone programming (SOCP) approaches is proposed. Simulation results demonstrate the effectiveness of two proposed algorithms and also show the superiority of our developed schemes over state-of-the-art benchmark ISAC schemes.

In this article, channel estimation (CE) of an active intelligent reflecting surface (IRS) aided uplink Internet of Things (IoT) network is investigated. First, the least square (LS) estimators for the direct channel and the cascaded channel are presented, respectively. The corresponding mean-square errors (MSEs) of channel estimators are derived. Subsequently, in order to evaluate the influence of adjusting the transmit power at the IoT devices or the reflected power at the active IRS on Sum-MSE performance, two situations are considered. In the first case, under the total power sum constraint of the IoT devices and active IRS, the closed-form expression of the optimal power allocation (PA) factor is derived. In the second case, when the transmit power at the IoT devices is fixed, there exists an optimal reflective power at active IRS. To further improve the estimation performance, the convolutional neural network (CNN)-based direct CE (CDCE) algorithm and the CNN-based cascaded CE (CCCE) algorithm are designed. Finally, simulation results demonstrate the existence of an optimal PA strategy that minimizes the Sum-MSE, and further validate the superiority of the proposed CDCE/CCCE algorithms over their respective traditional LS and minimum MSE (MMSE) baselines.

The multiple-input-multiple-output (MIMO) scheme enhances the capacity and reliability of secure Internet of Things (IoT) communication systems. In this article, an MIMO secure IoT communication system which includes a multiantenna transmitter, a multiantenna legitimate receiver, and a multiantenna eavesdropper is investigated. Considering that the transmitter and receiver are deterministic whereas the eavesdropper is either deterministic or randomly distributed, we theoretically derive the secrecy outage probabilities (SOPs) in independent and identically distributed (i.i.d.) as well as full-correlated Rayleigh fading channels. The i.i.d. or full-correlated Wishart matrices are introduced to express the mutual information rates from the transmitter to the legitimate receiver and eavesdropper. By analyzing the joint probability density functions of unordered eigenvalues of Wishart matrices, we obtain the moment generating functions (MGFs) of mutual information rates. Employing inverse Laplace transforms of the MGFs, the SOPs are obtained. Simulation results show the close agreement between the simulated and analytical results.

Integrated sensing and communication (ISAC), which simultaneously achieves wireless sensing and communication, is promising for next-generation wireless networks. In this paper, we consider a joint state sensing and communication system, where a multi-antenna ISAC transceiver simultaneously senses a sensing target and conveys a message to a multi-antenna communication receiver in multiple-input-multiple-output (MIMO) real Gaussian channels. Our goal is to optimize the signal input probability distribution to obtain the approximate capacity-distortion region. The formulated optimization problem is difficult because of high optimization dimension and high storage complexity. To reduce the optimization dimension, it is theoretically proved that over each transmitting antenna, the optimal signal inputs over different symbol durations should follow an independent and identical distribution. To reduce the storage complexity, it is also theoretically proved that the signal input probability distribution optimization over MIMO channels is equivalent to that over multiple-input-single-output channels. We propose an alternating optimization based Blahut-Arimoto algorithm to solve the optimization problem. Numerical results illustrate that the proposed joint state sensing and communication system achieves the larger capacity-distortion region than both the basic time-sharing (TS) and improved TS schemes.

To combine the orthogonal frequency division multiplexing (OFDM) modulation with non-orthogonal multiple access (NOMA) technique, it is required to ensure the successful successive interference cancellation procedure. Because of the fast Fourier transform (FFT) and inverse FFT employed at the OFDM transceivers, the central user may have weak subcarriers and the cell-edge user may have strong subcarriers. In this paper, we propose that for the central or cell-edge user, signals over the weak (strong) subcarriers are jointly encoded and decoded. Our objective is to maximize the weighted sum average achievable rates at both the central and cell-edge users through power allocation optimization. To solve the optimization problem, we propose a constrained convex-concave procedure (CCCP) based locally optimal solution, a Lagrangian dual transformation based locally optimal solution, and a prime decomposition based near-optimal solution. The prime decomposition based algorithm, which solves the problem based on water-filling, has extremely low computational complexity. It is shown through simulation results that the proposed scheme outperforms the conventional OFDM-NOMA scheme. Furthermore, it is found that the results obtained by the CCCP based solution, Lagrangian dual transformation based solution, and prime decomposition based solution match one another.

In this paper, we consider a physical layer security issue in a multiple-input multiple-output (MIMO) relay assisted over-the-air computation (AirComp) network, where sensors send their sensing data to the access point (AP) via a harvest and forward relay, and an eavesdropper (Eve) attempts to wiretap the aggregation result of sensors’ sensing signals. In the first time slot, the AP applies artificial noise (AN) to protect the aggregation result from being wiretapped by the Eve, and the relay harvests energy from the received signal according to the power splitting protocol. In the second time slot, the relay forwards the received signal, containing AN and sensors’ sensing signals, to the AP. The channel state information (CSI) between each node is assumed to be imperfect, and the channel uncertainties are molded as bounded errors. Specifically, we investigate a joint secure beamforming and power splitting design to minimize the worst-case mean-square error (MSE) at the AP, subject to the worst-case MSE constraint of the Eve and the worst-case transmit power constraints at each node. Different from the common approach that ignores the higher-order terms of channel uncertainties caused by cascade channels, by exploiting the block coordinate descent (BCD) algorithm, cutting set (CS) method and constrained concave-convex procedure (CCCP), we propose a BCD-CS-CCCP algorithm attempting to solve the robust optimization problem while retaining the higher-order terms of channel uncertainties. Numerical results indicate the effectiveness of our proposed schemes.

Future Internet-of-Things applications may require both high-quality wireless data aggregation and communication simultaneously. In this paper, we propose an integrated over-the-air computation (AirComp) and non-orthogonal multiple access (NOMA) communication scheme in a wireless uplink system, which consists of a data fusion center, a base station, and associated multiple users. Each user participates in AirComp and information transmission simultaneously. The data fusion center is responsible for wireless data aggregation and the base station is responsible for data collection of all users. Our aim is to minimize the computation mean square error (CMSE) at the data fusion center while satisfying the total transmission rate requirement at the base station. The formulated optimization problem is non-convex. To solve the problem, we propose a one-dimensional search based globally optimal solution, an alternating optimization based locally optimal solution, and a generalized Rayleigh quotient based globally optimal solution. Numerical results illustrate that although the locally optimal solution has the shortest average program execution time (PET), it achieves the suboptimal system performance. The generalized Rayleigh quotient based globally optimal solution, which addresses the CMSE minimization problem in a different manner, has much lower average PET than the one-dimensional search based globally optimal solution.

For reconfigurable intelligent surface (RIS)-aided wireless communications, the phase hopping scheme forces the wireless channel to experience multiple channel conditions and thus improves the system reliability. In this paper, we propose a phase hopping based RIS-aided downlink non-orthogonal multiple access system. For the proposed system, we study the outage balancing problem, where the closed-form expression of ergodic capacity, an increasing function with respect to the variance of overall channels, is theoretically derived. One challenge to solve the outage balancing problem is to obtain the cumulative distribution function (CDF) of the variance of overall channels. For Rayleigh fading channels, the CDF is obtained using the characteristic function of the product of channel gains and the inverse theorem. For Rician fading channels, we approximate the variance of overall channels as a Gamma distributed random variable. The outage balancing problem is solved by a bisection search. To show the advantages to the phase hopping scheme, we also solve the outage balancing problem for a static random phases based system. Simulation results show that at moderate and high signal-to-noise ratio, the proposed phase hopping based system is superior to the static random phases based one.

In this work, we consider the optimality of signalling for covert sensing, where a legitimate receiver intends to estimate unknown variables based on the received signals from a transmitter, while ensuring that the probability of these signals being detected by a warden Willie is negligible. Specifically, we consider additive white Gaussian noise (AWGN) for both the estimation channel and detection channel, based on which we first reveal that Gaussian signalling is not optimal in terms of maximizing the estimation accuracy (e.g., maximizing the Fisher information) subject to a covertness constraint, e.g., guaranteeing a Kullback-Leibler divergence being no large than a specific value. To this end, we explicitly show that a skew normal distribution with an optimized skew parameter can achieve a higher estimation accuracy than a normal distribution subject to the same covertness constraint. Furthermore, we develop a framework based on calculus of variations and the Runge-Kutta method to identify the optimal signalling for covert sensing. As expected and explicitly shown in our numerical results, the identified optimal signalling outperforms both the normal and skew normal distributed signalling, which demonstrates the necessity of optimizing signalling in the context of covert sensing.

The use of a reconfigurable intelligent surface (RIS) in the enhancement of the rate performance is considered to involve the limitation of the RIS being a passive reflector. To address this issue, we propose a RIS-aided amplify-and-forward (AF) relay network in this paper. By jointly optimizing the beamforming matrix at AF relay and the phase-shift matrices at RIS, two schemes are put forward to address a maximizing signal-to-noise ratio (SNR) problem. First, aiming at achieving a high rate, a high-performance alternating optimization (AO) method based on Charnes–Cooper transformation and semidefinite programming (CCT-SDP) is proposed, where the optimization problem is decomposed into three subproblems solved using CCT-SDP, and rank-one solutions can be recovered using Gaussian randomization. However, the optimization variables in the CCT-SDP method are matrices, leading to extremely high complexity. To reduce the complexity, a low-complexity AO scheme based on Dinkelbachs transformation and successive convex approximation (DT-SCA) is proposed, where the variables are represented in vector form, and the three decoupling subproblems are solved using DT-SCA. Simulation results verify that compared to three benchmarks (i.e., a RIS-assisted AF relay network with random phase, an AF relay network without RIS, and a RIS-aided network without AF relay), the proposed CCT-SDP and DT-SCA schemes can harvest better rate performance. Furthermore, it is revealed that the rate of the low-complexity DT-SCA method is close to that of the CCT-SDP method.

In this paper, we investigate the physical layer security of an untrusted relay assisted over-the-air computation (AirComp) network, where each node is equipped with multiple antennas and the relay is operated in an amplify-and-forward mode. The relay receives the data from each sensor and sends them to the access point (AP) in the first and second time slot, respectively. The AP applies artificial noise (AN) to protect the aggregation of sensors’ data from being wiretapped by the untrusted relay in the first time slot. In particular, we are interested in minimizing the computation distortion measured by the mean-squared error (MSE) via jointly optimizing beamforming matrices at all nodes, subject to the MSE constraint at the relay and individual power constraints at the AP, the relay and each sensor. In the case of the perfect channel state information (CSI), we convert the nonconvex MSE minimization problem into a difference-of-convex (DC) form and propose a constrained concave-convex procedure that can obtain a local minimum to solve the DC problem. We also generalize the framework to an imperfect CSI case where the additional interference term due to incomplete interference cancellation is considered, and the nonconvex robust MSE minimization problem is solved by a proposed inexact block coordinate descent algorithm. Numerical results are presented to show the effectiveness of our proposed schemes.

Future ultrareliable low-latency tasks in the Internet of Things require finite blocklength transmissions. The spectral efficiency of finite blocklength transmissions, by incorporating nonorthogonal multiple access (NOMA), can be significantly improved. In conventional NOMA systems, the successive interference cancelation (SIC) is employed for signal decoding, which is optimal for sufficient long blocklength transmissions. However, for finite blocklength transmissions, the joint decoding instead of SIC is optimal. Considering the joint decoding, we study the decoding error probability and power allocation factor optimization problem, which aims at maximizing the effective throughput at the central user under the minimum-required effective throughput constraint at the cell-edge user. We put forward a 2-D search method to find the globally optimal solution and a low-complexity alternating optimization method to find the locally optimal solution. It is illustrated that our proposed joint decoding scheme has the higher effective throughput than the conventional SIC scheme.

This paper investigates the physical layer security issue in an amplify-and-forward (AF) multi-input multi-output (MIMO) two-way relay assisted cognitive radio (CR) nonorthogonal multiple access (NOMA) network, where the simultaneous wireless information and power transfer (SWIPT) technology is employed to improve network energy efficiency. We consider the scenario that a pair of primary users and two pairs of secondary users (SUs) exchange information via a MIMO two-way relay, where the edge SU of each SU pair is untrusted and tries to wiretap the central SU’s information. For ensuring security, we aim to maximize the sum achievable secrecy rate (SASR) by jointly optimizing the power allocation at all users, power splitting factor and relay beamforming subject to the quality of service (QoS), energy harvesting and transmit power constraints. The formulated optimization problem is highly nonconvex due to coupling variables, thus it is challenging to solve. An effective path-following (PF)-based algorithm is proposed, which is proven to converge to a stationary point. Theoretical and simulation results show that the proposed PF-based algorithm has lower complexity than the state-of-art algorithm. To further reduce complexity, we proposed a zero-forcing (ZF)-based scheme. Numerical simulations show that the proposed PF-based algorithm achieves the same SASR as the state-of-art algorithm with moderate complexity, while the proposed ZF-based scheme strikes a good balance between performance and complexity.

Orthogonal time frequency space (OTFS) modulations are robust to time-varying frequency-selective fading channels. OTFS modulations operate in the delay-Doppler domain whereas two-dimensional (2D) orthogonal frequency division multiplexing (OFDM) modulations operate in the time-frequency domain. For 2D OFDM modulations in time-varying frequency-selective fading channels, we investigate the pilot pattern design problem, which minimizes the mean square error (MSE) of channel estimation. The MSE lower bound (LB) is theoretically derived to provide the design criterion. Based on the criterion, we show that the LB achieving design can be found by exhaustive 2D pilot location search. Exhaustive 2D search has high computational complexity. To reduce the complexity, sufficient conditions on the existence of LB achieving design are provided to decouple the 2D problem into two one-dimensional (1D) problems. For the 1D problem, we propose the LB achieving design when the number of possible pilot locations is divisible by the number of pilots. Simulation results illustrate that our proposed LB achieving design is superior to the random pilot pattern design and the bilinear interpolation channel estimation method.

In this paper, we investigate the secure beamforming design in multiple-input multiple-output (MIMO) nonorthogonal multiple access (NOMA) en-abled Internet of Things (IoT) networks, where a controller transmits confidential messages to multiple actuators and a cooperative controller acts as a jammer to prevent a potential eavesdropper from wiretapping the information. Our goal is to maximize achievable secrecy sum rate subject to the successful successive interference cancellation constraint and transmit power constraints of the controller and jammer. When channel state information (CSI) is perfect, we design the secure beamforming by developing an iterative optimization algorithm based on minimum mean square error (MMSE) method. When perfect CSI is not available, we model the channel errors as deterministically-bounded, and propose a robust secure beamforming design algorithm based on weighted MMSE and cutting-set method. The effectiveness of the proposed algorithms is verified by the simulation results.

Considering reconfigurable intelligent surfaces (RISs), we study a multi-cluster multiple-input-single-output (MISO) non-orthogonal multiple access (NOMA) downlink communication network. In the network, RISs assist the communication from the base station (BS) to all users by passive beamforming. Our goal is to minimize the total transmit power by jointly optimizing the active beamforming matrices at the BS and the reflection coefficient vector at the RISs. Because of the constraints on the RIS reflection amplitudes and phase shifts, the formulated quadratically constrained quadratic problem is highly non-convex. For the aforementioned problem, the conventional semidefinite programming (SDP) based algorithm has prohibitively high computational complexity and deteriorating performance. Here, we propose an effective second-order cone programming (SOCP)-alternating direction method of multipliers (ADMM) based algorithm to obtain the locally optimal solution. To reduce the computational complexity, we also propose a low-complexity zero-forcing based suboptimal algorithm. It is shown through simulation results that our proposed SOCP-ADMM based algorithm achieves significant performance gain over the conventional SDP based algorithm. Furthermore, when the target transmission rates of central and cell-edge users are 0.5 bps/Hz, our proposed NOMA RIS-aided system with 32 RIS elements has about 2.5 dB performance gain over the conventional massive multiple-input-multiple-output system with 64 transmit antennas.

Incorporating short-packet communications with non-orthogonal multiple access (NOMA) networks is able to achieve both low communication delay and high spectral efficiency. In this article, a wireless NOMA downlink short-packet communication system is studied. The system includes a base station, an entrusted central user, and an untrusted cell-edge user. The untrusted cell-edge user may eavesdrop the signals from the base station to the central user. We theoretically derive the average secure block error rate (BLER) of the central user in flat Rayleigh fading channels by utilizing the linear approximations on BLER and secure BLER. We also present the asymptotic average secure BLER at high signal-to-noise ratio (SNR). From numerical results, it is shown that our derived analytical average secure BLER matches the simulated one. Numerical results also illustrate that at high SNR, error floors occur. Furthermore, at high SNR, it is found that when the average secure BLER is not one, the power allocation factors for the central and cell-edge users have almost no impact on the average secure BLER.

Incorporating wireless caching in non-orthogonal multiple access (NOMA) networks is a promising technique to reduce the delivery latency and improve the quality of service. In this paper, we study a cache content placement optimization problem in a cellular NOMA downlink wireless caching network. Our goal is to minimize the average transmit power under the cache capacity constraints. The optimization problem is a non-linear integer programming, which is non-deterministic polynomial-time hard. To efficiently solve the problem, an alternating upper plane method based on quadratic knapsack problem (QKP) is proposed. To deal with the general situation that the sizes of files and cache capacities are not integers, an alternating method based on semidefinite relaxation is also proposed. Finally, a constrained concave-convex procedure-based iterative method is proposed to further reduce the computational complexity. Simulation results show that our proposed methods are superior to the schemes which cache the most popular files until the cache is full.

In this article, we study the secure beamforming design for a two-way cognitive radio (CR) Internet of Things (IoT) network aided with the simultaneous wireless information and power transfer (SWIPT). Located at the center of secondary network, the IoT controller helps to provide relay assistance and cooperative physical layer security (PLS) for two primary users (PUs) against an eavesdropper, while transmitting information and power to the other IoT devices (IoDs) with primary spectrum. To enhance the information security, we aim to maximize the secrecy sum rate (SSR) for PUs by jointly designing the beamforming matrix and vectors at the central controller. To efficiently solve the nonconvex problem, we first propose the branch-reduce-and-bound (BRB)-based algorithm to obtain an upper bound for the SSR and offer a feasible solution by Gaussian randomization, which demands two-level iteration and thus has high complexity. To strike a balance between the complexity and the performance, we then propose iterative algorithm based on constrained-convex-concave programming (CCCP) and a zero forcing (ZF)-based noniterative algorithm, the latter of which with lowest complexity is suitable for the central controller with limited-power supply. The simulation results are provided to demonstrate the effectiveness of our proposed optimization algorithms in comparison to the traditional schemes.

In this paper, we investigate an unmanned aerial vehicle (UAV) enabled wireless powered communication network, where the UAV with constant power supply first charges all users by transmitting wireless energy to all users simultaneously, and after that all users send their own information to the UAV. Our target is the joint optimization of the time allocation as well as the position of the UAV to make the uplink sum achievable rate for all users as large as possible. To solve this non-convex problem, we first derive the analytic optimal solution of the time allocation, which is expressed as the function of UAV position. The original problem, after substituting the derived optimal time allocation, is reformulated as a new optimization problem whose optimization variable is only UAV position. We propose a sequential unconstrained convex minimization-based algorithm to obtain the globally optimal solution. The simulation results demonstrate that the performance of our proposed algorithm matches with that obtained by two-dimensional exhaustive search. To decrease the complexity, a Dinkelbach-based algorithm to obtain the locally optimal solution is also proposed. The simulation results show that the performances of our proposed two algorithms are superior to the schemes without time allocation optimization and/or position optimization.

In[1], the title of the paper should be: “Joint Position and Time Allocation Optimization of UAV Enabled Wireless Powered Communication Networks.”

Considering that a legitimate full-duplex monitor aims to intercept the suspicious wireless transmission, we study the robust jamming design problem where imperfect channel state information is assumed. We model channel estimation errors as deterministically bounded and define the successful monitoring event as that the signal-to-interference-plus-noise ratio (SINR) at the legitimate monitor is higher than the SINR at the suspicious receiver. To increase the worst-case probability of successful monitoring, we propose a robust proactive monitoring via jamming scheme. From simulation results, our proposed robust scheme is superior to the nonrobust one.

Cooperative non-orthogonal multiple access (NOMA) systems inherit advantages of the NOMA protocol and the cooperative relay. In this paper, we propose cooperative NOMA systems in multiple-input-multiple-output channels. The whole transmission is divided into two phases. In the first phase, the base station broadcasts signals using the NOMA protocol to a central user and a cell-edge user. In the second phase, the central user helps the base station cooperatively relay signals intended for the cell-edge user. Our objective is to maximize achievable rate from the base station to the cell-edge user under transmit power constraints and achievable rate constraint from the base station to the central user. The difficulty of this problem is the joint beamforming of the base station and the central user in the second phase. We propose a constrained convex-concave procedure (CCCP)-based algorithm. To reduce computational complexity, we also propose a closed-form search-based suboptimal algorithm. Simulation results demonstrate that our proposed cooperative NOMA system with CCCP-based algorithm outperforms the conventional NOMA scheme. When achievable rate constraint to the central user is low, our proposed cooperative NOMA system with the closed-form search-based suboptimal algorithm outperforms the NOMA scheme.

To legitimately monitor suspicious wireless communications is an important issue. In this letter, we propose a proactive monitoring via jamming scheme over an amplify-and-forward relay network, which consists of a legitimate monitor, a suspicious source, a suspicious relay, and a suspicious destination. Our objective is to design the optimal jamming power to maximize average monitoring rate at the legitimate monitor. For the optimization problem, we propose to find the true optimal jamming power by a bisection search method. Furthermore, we propose an approximate optimal jamming scheme whose closed-form expression on average monitoring rate is theoretically derived. Simulation results demonstrate that our proposed true optimal jamming scheme outperforms passive monitoring and proactive monitoring via constant-power jamming. Furthermore, our proposed approximate optimal jamming scheme achieves almost the same average monitoring rate as true optimal one.

In this letter, we consider a cellular downlink multiple-input-multiple-output nonorthogonal multiple access (NOMA) secure transmission network, which consists of a base station, a central user, and a cell-edge user. The base station and two users are all equipped with multiple antennas. The central user is an entrusted user and the cell-edge user is a potential eavesdropper. We focus on secure beamforming optimization problem, which maximizes achievable secrecy rate of the central user subject to transmit power constraint at the base station and transmission rate requirement at the cell-edge user. The optimization problem is nonconvex. We employ majorization-minimization method to iteratively optimize a sequence of valid surrogate functions for the nonconvex optimization problem. Furthermore, in each iteration, we derive the semi-closed form solution to optimize the valid surrogate functions. Simulation results demonstrate that our proposed NOMA scheme outperforms the zero-forcing-based NOMA scheme and conventional orthogonal multiple access scheme.

Nonorthogonal multiple access (NOMA) is expected to be a promising technique for future wireless networks. In this letter, we investigate the secrecy sum rate optimization problem for a downlink multiple-input-multiple-output NOMA system that consists of a base station, multiple legitimate users, and an eavesdropper. Our objective is to maximize achievable secrecy sum rate subject to successful successive interference cancellation constraints and transmit power constraint. The formulated optimization problem is nonconvex. Motivated by the relationship between mutual information rate and minimum mean square error, we propose to transform the secrecy sum rate optimization problem into a biconvex problem. The biconvex problem is solved by alternating optimization method where in each iteration, we solve a second-order cone programming. Simulation results demonstrate that our proposed NOMA scheme outperforms conventional orthogonal multiple access scheme.

Non-orthogonal multiple access (NOMA) is a promising technology in future communication systems. In this paper, we propose a buffer-aided NOMA relaying system, which consists of a source, a relay, and two destinations. In the relaying system, the relay helps the source transmit packets to two destinations simultaneously using NOMA scheme. We theoretically derive outage probabilities of source-to-relay link and relay-to-destinations links considering two scenarios that the relay does and does not know the channel state information (CSI) from itself to two destinations. When the relay knows CSI, the obtained outage probability of relay-to-destinations links involves integration operation. Thus, we derive an upper bound and two lower bounds. Simulation results demonstrate that two lower bounds approach exact outage probability at low and high signal-to-noise ratios, respectively. We also propose a relay decision scheme for the buffer-aided NOMA relaying system. Based on the obtained system outage probability, we theoretically derive the diversity order. It is found that no matter whether the relay knows CSI or not, the diversity order of 2 can be achieved when the buffer size is larger than or equal to 3.

Transceiver with Tomlinson-Harashima (TH) precoding outperforms the linear minimum mean-square-error (MSE) architecture in terms of minimum achievable MSE. In this paper, we investigate transceiver design optimization problem for nonregenerative multiple-input multiple-output cognitive relay networks (CRNs) with TH precoding. In the CRN, a secondary user (SU) source, an SU relay and an SU destination employ a TH precoder, a relay precoder, and a linear equalizer, respectively. For scenario in which SUs know perfect channel state information (CSI) from SUs to primary users, we propose an alternating optimization (AO)-based suboptimal algorithm. Given TH precoder and relay precoder, we derive a closed-form optimal solution of linear equalizer. Given relay precoder, TH precoder can be found by convex optimization. Given TH precoder, we transform nonconvex relay precoder design problem into a difference of convex programming and propose a constrained concave convex procedure-based iterative algorithm to find its local optimum. For scenario in which SUs know imperfect CSI, the channel uncertainties are modeled by worst case model. We derive equivalent worst case interference power constraints and extend the proposed AO-based suboptimal algorithm to cope with the worst case interference power constraints. Simulation results demonstrate that the proposed transceiver design with TH precoding outperforms linear transceiver designs.

In this paper, we investigate information and energy cooperation in cognitive non-regenerative two-way relay networks, where the multi-antenna secondary user (SU) transmitter harvests energy from primary users' (PUs) signals by the power splitting (PS) scheme to forward PUs' signals and transmit its own signals. Our objective is to design beamforming matrix and vector at the SU transmitter and PS factor to maximize achievable rate at SUs while maintaining achievable rate requirements at PUs subject to transmit power constraint at SU transmitter. We consider two scenarios that the SU transmitter knows perfect and imperfect channel state information of all links. For the former, we propose an optimal solution based on Charnes-Cooper transformation and 1-D search. We also propose a low-complexity suboptimal solution based on the zero-forcing (ZF) scheme and the algebraic norm-maximizing scheme. For the latter, channel uncertainties are modeled by a worst-case model. We propose to employ a ZF scheme and transform the problem into a semidefinite programming with the help of S-procedure and rank-one relaxation. The rank-one feasible solution is proposed to be recovered by the Gaussian randomization method. Simulation results demonstrate that our proposed designs outperform the beamforming scheme without energy cooperation.

For conventional subcarrier pairing scheme in cooperative orthogonal frequency division multiplexing decode‐and‐forward multi‐relay networks, to avoid interference, each subcarrier pair (SP) is assigned to a single relay. Over a specific subcarrier, the destination receives signals transmitted from the relay. In this study, to better exploit the degrees of spatial freedom, the authors propose to assign each SP to multiple relays. Thus, over a specific subcarrier, the destination receives signals transmitted from multiple relays. Under the total network power constraint, to maximise the sum transmission rate, they propose a joint resource allocation scheme, in which they jointly optimised the four types of resources: assisting relays selection, transmission mode selection, subcarrier pairing and power allocation. They further propose a suboptimal algorithm which can significantly reduce the computational complexity of the aforementioned optimal allocation scheme with sacrificing little on the performance. It is shown from simulation results that the author's proposed schemes have significant performance improvement over the resource allocation schemes in the literature.

Cognitive radio is a potential technique to solve the spectrum shortage problem in wireless communications. Integrating wireless relaying into the cognitive radio networks can further improve the spectrum efficiency. In this study, a multiple‐input multiple‐output cognitive relay network is considered, where the primary network (PN) consists of one transmitter‐receiver pair and the secondary network consists of multiple active source‐destination pairs and non‐regenerative relays. All nodes in the networks are deployed with multiple antennas. How to avoid interference to the PN is an important task in the secondary network design. The precoders of the secondary sources and the receivers of the secondary destinations are designed in a well‐known zero‐forcing way. The secondary relays forward signals for the secondary source‐destination pairs by beamforming. With interference cancellation constraints and individual transmit power constraints, a relay beamforming scheme is proposed to maximise the total signal‐to‐interference‐plus‐noise ratio (SINR) at the secondary destinations. Then a maximising minimum SINR relay beamforming scheme is further proposed to provide max–min fairness among the secondary source‐destination pairs. The beamforming problems are formulated into the quadratically constrained quadratic fractional programming problems, and are solved by the semi‐definite relaxation technique. The performances of these beamforming schemes have been verified by computer simulations.

For wireless networks which aim at high speed communication and computation, we propose a parallel analog function computation scheme via wireless multiple-access multiple-input-multiple-output channels. We consider that each sensor node only has imperfect channel state information from itself to the fusion center. Modeling the channel uncertainties by the worst-case model, the robust transceiver design problem for parallel analog function computation is formulated as a non-convex optimization problem which minimizes the worst-case mean-square-error subject to individual transmit power constraints. We then propose an alternating optimization algorithm to solve the problem. Simulation results demonstrate that the proposed robust scheme outperforms the non-robust one.

Employing energy harvesting (EH) at the relay with simultaneous wireless information and power transfer is promising to prolong lifetime of energy-constrained relay networks. In this letter, considering multicarrier decode-and-forward (DF) relay network with time-switching (TS) based relaying, we investigate the optimization problem which jointly designs TS ratios of EH and information-decoding at the relay, TS ratio of signal forwarding from relay to destination as well as power allocation (PA) over all subcarriers at source and relay. Our objective is to maximize end-to-end achievable rate of DF relay networks subject to transmit power constraint at source and EH constraint at relay. We propose to decouple the optimization problem into a convex problem and a quasi-convex problem. For the quasi-convex problem, we propose to solve it by bisection search. Simulation results demonstrate that our proposed joint TS and PA optimization scheme outperforms the scheme with fixed TS ratio of EH.

In this letter, considering the simultaneous wireless information and power transfer scheme, we study the robust artificial noise (AN)-aided secure transmission design in multiple-input-single-output channels where the channel uncertainties are modeled by worst-case model. Our objective is to maximize the worst-case secrecy rate with respect to both the worst-case channel uncertainties and the worst-case eavesdropper among multiple eavesdroppers, under the transmit power constraint and the worst-case energy harvesting constraint. The optimal solution to the problem can be found by two-dimensional (2-D) search. Since the 2-D search algorithm has high computational complexity, we propose to neglect the correlation of the channel uncertainties from the transmitter to the information-decoding receiver and reformulate the problem as a sequence of convex semidefinite programming (SDP) which is solved efficiently by SDP based one-dimensional line search method. It is shown through computer simulations that the proposed robust AN-aided secure transmission schemes have significant performance gain over the non-robust AN-aided secure transmission scheme and the robust secure transmission scheme without the aid of AN.

Considering simultaneous wireless information and power transfer (SWIPT), we investigate cooperative-jamming (CJ) aided robust secure transmission design in multiple-input-single-output channels, where a cooperative jammer introduces jamming interferences and assists a source to supply wireless power for both an energy receiver and a legitimate destination. The destination employs a power splitting (PS) scheme to split the received signals for both information decoding and energy harvesting (EH). Compared with conventional transmission without SWIPT, the transmission with SWIPT should satisfy additional worst-case EH constraints. Furthermore, the PS scheme introduces an additional multiplicative optimization variable, i.e., the PS factor. Our objective is to maximize worst-case secrecy rate under transmit power constraints and worst-case EH constraints. We propose to decouple the problem into three optimization problems and employ alternating optimization algorithm to obtain the locally optimal solution. For the optimization of transmit covariance matrices and PS factor, we propose to employ the S-procedure and its extension to reformulate it as a convex semidefinite programming. It is shown through the simulation results that our proposed CJ aided robust secure transmission scheme outperforms the robust direct transmission scheme without CJ and the CJ aided non-robust scheme.

For amplify-and-forward multi-antenna relay networks with energy harvesting (EH) constraint, we study the optimal relay beamforming problem which maximizes the achievable rate from source to information-decoding receiver subject to the transmit power constraint at relay and the EH constraint at EH receiver. Because of the EH constraint, the beamforming problem is not convex. We propose the optimal beamforming scheme by converting the beamforming problem into a convex semidefinite programming with the rank-one relaxation and Charnes-Cooper transformation. We also propose a suboptimal closed-form beamforming scheme. It is shown from simulations that when the maximum allowable relay transmit power to noise power ratio is high, the performance of proposed suboptimal scheme approaches that of the optimal scheme.

In cognitive relay networks, the interferences from secondary users (SUs) and relays to primary users are constrained to be lower than a threshold. The interference constraints are difficult to satisfy when the channel state information (CSI) is imperfect. In this paper, we propose a robust beamforming scheme for the multi-antenna non-regenerative cognitive relay network where the multi-antenna relay with imperfect CSIs helps the communication of single-antenna SU. Our objective is to design a robust beamforming scheme which maximizes the system capacity subject to transmit power constraint and interference constraints. The bounded channel uncertainties are modeled using the worst-case model. The robust beamforming problem, neglecting the correlation of channel uncertainties, is reformulated as a convex semidefinite programming (SDP) by rank-one relaxation. This convex SDP is related with the worst-case relay transmit power minimization problem, which is further reformulated as a convex SDP, whose rank-one solution is proved to exist. Thus, we propose the suboptimal solution to the robust beamforming problem which is found effectively by solving two convex SDPs. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.

Simultaneous wireless information and power transfer (SWIPT) is able to prolong the lifetime of energy constrained wireless networks. In this paper, we consider the relay beamforming design problem for SWIPT scheme in a non-regenerative two-way multi-antenna relay network. Our objective is to maximize the sum rate of two-way relay network under the transmit power constraint at relay and the energy harvesting (EH) constraint at EH receiver. For the non-convex EH-constrained relay beamforming optimization problem, we propose an iterative algorithm to find the global optimal solution based on semidefinite programming and rank-one decomposition theorem. To reduce computational complexity of global optimal solution, we transform the EH-constrained optimization problem to a difference of convex programming and propose a constrained concave convex procedure based iterative algorithm to find a local optimum. To further reduce the complexity, we propose a suboptimal solution based on the generalized eigenvectors method. When the case of multi-antenna sources is considered, we propose the alternating optimization based iterative algorithms. It is shown from simulations that considering the EH constraint, our proposed schemes outperform conventional relay beamforming schemes in the literature.

The antenna subarray formation (ASF) is a promising technique for multiple-input multiple-output (MIMO) receiver. For MIMO cognitive radio systems, we propose a joint beamforming and ASF scheme in this letter which maximizes the cognitive achievable capacity subject to the peak transmit power constraint at the secondary transmitter, peak interference power constraint at the primary receiver, and the limited number of nonzero elements in the ASF matrix. To solve the joint optimization problem, we propose a relax-and-recover scheme. Simulation results have shown that the proposed scheme outperforms the conventional antenna selection scheme.

Impulse radio localization is an ideal technology for indoor localization. In this paper, we derive the Cram'er-Rao lower bounds (CRLBs) of impulse radio localization with antenna array reception. Previous works on CRLBs of localization with antenna array reception require an important assumption that the multipath components which arrive at the different antenna array elements are independent. However, this assumption may not be valid when the antenna array elements get close to one another. Without this assumption, we derive the CRLBs of impulse radio antenna array localization. Simulation results show that the derived CRLBs give a good indication of the actual standard deviations of the time of arrival and angle of arrival estimation.

A novel signal structure is designed for a high data rate (HDR) Prerake UWB system, which uses orthogonal codes and chip-interleaving to suppress the inter-chip interference (ICI). On one hand, high data rate is achieved via superposition of symbols. On the other hand, the symbols are coded using the Walsh-Hadamard codes and interleaved on the chip level, which largely eliminates the ICI. The BER performance of the system is analyzed under imperfect channel estimation. Numerical results show that the HDR Prerake UWB system using the Walsh-Hadamard codes and chip-interleaving outperforms the conventional HDR Prerake UWB system using the random DS codes without ICI suppression under both perfect and imperfect channel estimation. Effect of the length of Walsh-Hadamard codes on the system performance is also discussed in detail.

The differentially-encoded, di-symbol time-division multiuser impulse radio (d2TD-IR) with delay-sum autocorrelation receivers is a low complexity, high efficiency short range wireless communication technology for infrastructure networks. The d2TD-IR system is designed with the assumption that the users are perfectly synchronized. In this letter, we propose a recursive algorithm of combined synchronization and power control. Computer simulation results show that the proposed algorithm has significant performance improvement over the algorithm, in which synchronization and power control are performed separately.

Transmitted-reference impulse radio (TR-IR) is a low complexity UWB system which is suitable for highly dispersive multipath channel. However, TR-IR requires a wideband analog delay line which has high complexity and high energy consumption. In this letter, we propose a digital eigen-based (EB) receiver which first converts the analog signal to digital signal and then demodulates the received signal in digital domain. Computer simulation results show that the proposed receiver can outperform the conventional autocorrelation receiver. We also proposed a monobit digital EB (MEB) receiver as a low complexity alternative to the EB receiver. The bit-error-rate performance of MEB receiver have also been studied theoretically to show the quantization effect in this letter.

Transmitted-reference impulse radio with autocorrelation receiver (TR-IR/AcR) generally requires wideband analog delay line (WADL) with length of several tens nanoseconds. To maximally reduce the length, we propose a TR-IR system based on selective combining, which intelligently selects integration regions for symbol detection. It was shown that when the WADL with a total length of 8 ns is employed, the proposed system has a signal-to-noise ratio gain of 0.5 dB, 2.3 dB, 2,.3 dB and 3.7 dB over the TR-IR/AcR at bit-error-rate of 10-5for ultra-wideband channel models CM 1 ~ 4, respectively.

For transmitted-reference impulse radio (TR-IR), the conventional antenna array reception will provide array gain and thus improve system bit-error-rate (BER) performance. In this paper, we propose a delay-sum antenna array autocorrelation receiver for the TR-IR systems. Simulation results show that by exploiting spatial correlations between antenna array elements, an extra system performance improvement of 1.3 dB and 2.1 dB can be achieved by the proposed receiver at BER=10-5for the antenna array with 2 and 4 elements, respectively, in the proposed ultra-wideband channel model.

Resource allocation schemes for traditional MIMO system have been widely studied. However, when the MIMO technique is applied to relaying systems, where several separated terminals form a virtual receive array (VAA) and relay the signal from the transmitter to the receiver, these issues, which have a close relationship with the system performance, should be solved before a MIMO relaying system is deployed on a large scale. The channel capacity for a 2-hop MIMO relaying system is derived from the perspective of information theory. An optimal resource allocation under an aggregate power constraint between relaying nodes is proposed in closed form for the case with two relaying nodes. Numerical results indicate that a MIMO relaying system can achieve a higher channel capacity than traditional MIMO does and that the positions of the relaying nodes affect the system performance directly.