This paper considers the resource allocation problem in the sixth generation (6G) wireless interference channel (IC) under link outage constraints. Since the optimization problem is non-convex in nature, existing approaches to find the optimal power allocation are computationally intensive. When it comes to internet of things (IoT) and massive machine-type communications (mMTC) in the emerging 6G, the computational complexity becomes practically infeasible. Recently, deep reinforcement learning has shown promising outcome in solving non-convex optimization problems with reduced complexity. In this paper, we utilize a deep Q-learning (DQL) approach which interacts with the wireless environment and learns the optimal power allocation of a wireless IC while maximizing overall sum-rate of the system and maintaining reliability requirement of each link. We have used two separate deep Q-networks to remove the inherent instability in learning process. Simulation results demonstrate that the proposed DQL approach outperforms existing geometric programming based solutions.

This paper considers the grid formation of an unmanned aerial vehicle (UAV) swarm for maximizing the secrecy rate in the presence of an unknown eavesdropper. In particular, the UAV swarm performs coordinated beamforming onto the null space of the legitimate channel to jam the eavesdropper located at an unknown location. By nulling the channel between the legitimate receiver and the UAV swarm, we obtain an optimal trajectory and jamming power allocation for each UAV enabling wideband single ray beamforming to improve the secrecy rate. Results obtained demonstrate the effectiveness of the proposed UAV-aided jamming scheme as well as the optimal number of UAVs in the swarm necessary to observe a saturation effect in the secrecy rate. We also show the optimal radius of the unknown but constrained location of the eavesdropper.

This paper considers machine learning for physical layer security design for communication in a challenging wireless environment. The radio environment is assumed to be programmable with the aid of a meta material-based intelligent reflecting surface (IRS) allowing customisable path loss, multi-path fading and interference effects. In particular, the fine-grained reflections from the IRS elements are exploited to create channel advantage for maximizing the secrecy rate at a legitimate receiver. A deep learning (DL) technique has been developed to tune the reflections of the IRS elements in real-time. Simulation results demonstrate that the DL approach yields comparable performance to the conventional approaches while significantly reducing the computational complexity.

This paper focuses on optimal transmit power allocation to maximize the overall system throughput in a vehicle-to-everything (V2X) communication system. We propose two methods for solving the power allocation problem namely the weighted minimum mean square error (WMMSE) algorithm and the deep learning-based method. In the WMMSE algorithm, we solve the problem using block coordinate descent (BCD) method. Then we adopt supervised learning technique for the deep neural network (DNN) based approach considering the power allocation from the WMMSE algorithm as the target output. We exploit an efficient implementation of the mini-batch gradient descent algorithm for training the DNN. Extensive simulation results demonstrate that the DNN algorithm can provide very good approximation of the iterative WMMSE algorithm yet reducing the computational overhead significantly.

For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenges to obtaining real-time channel measurements. Deep learning (DL) techniques, in this context, offer learning ability and optimization capability that can approximate many kinds of functions. In this paper, we develop a DL-based channel prediction method to estimate channel responses for V2I communications. We have demonstrated how fast neural networks can learn V2I channel properties and the changing trend. The network is trained with a series of channel responses and known pilots, which then speculates the next channel response based on the acquired knowledge. The predicted channel is then used to evaluate the system performance.

In this paper, we consider a multiuser single-cell transmission facilitated by a full duplex (FD) base station (BS). We formulate two multi-objective optimization problems (MOOPs) via the weighted Tchebycheff method to jointly minimize the two desirable system design objectives namely the total downlink and uplink transmit power. In the first MOOP, multiuser interference is suppressed while in the second MOOP, multiuser interference is rather exploited. In order to solve the non-convex MOOPs, we propose a two-step iterative algorithm to optimize jointly the receive beamformer, transmit beamformer and uplink transmit power, respectively. Simulation results show the proposed scheme achieves a scalable performance-complexity trade-off that allows performance improvements compared to existing solutions.

In this paper, we consider a multi-user full duplex (FD) mobile edge computing (MEC) system, where a FD base station (BS) integrated with a MEC server, simultaneously transmits information signals to downlink users through the downlink and receive computation tasks for execution by the MEC server from mobile devices in the uplink. We study the trade-off between the offloading energy and latency by minimize the total offloading energy and latency via a weighted multi-objective optimization problem. First, we propose a design strategy based on the traditional interference suppression in the downlink, and then, we proposed a second design based on downlink interference exploitation. We employ Lagrangian methods to solve the two non-convex designs via an iterative algorithm. Simulation results not only show the trade-off between the offloading energy and latency, but also show the substantial gains achieved by the proposed FD schemes compared with the baseline half duplex schemes.

This paper studies interference exploitation techniques for secure beamforming design in simultaneous wireless information and power transfer in multiple-input single-output systems. In particular, multiuser interference (MUI) and artificially generated noise (AN) signals are designed as constructive to the information receivers (IRs) yet kept disruptive to potential eavesdropping by the energy receivers. The objective is to improve the received signal-to-interference and noise ratio (SINR) at the IRs by exploiting the MUI and AN power in an attempt to minimize the total transmit power. We first propose second-order cone programming-based solutions for the perfect channel state information (CSI) case by defining strong upper and lower bounds on the energy harvesting (EH) constraints. We then provide semidefinite programming-based solutions for the problems. In addition, we also solve the worst case harvested energy maximization problem under the proposed bounds. Finally, robust beamforming approaches based on the above are derived for the case of imperfect CSI. Our results demonstrate that the proposed constructive interference precoding schemes yield huge saving in transmit power over conventional interference management schemes. Most importantly, they show that, while the statistical constraints of conventional approaches may lead to instantaneous SINR as well as EH outages, the instantaneous constraints of our approaches guarantee both constraints at every symbol period.

Conventionally, interference and noise are treated as catastrophic elements in wireless communications. However, it has been shown recently that exploiting known interference constructively can contribute to signal detection ability at the receiving end. This paper exploits this concept to design artificial noise (AN) beamformers constructive to the intended receiver (IR) yet keeping AN disruptive to possible eavesdroppers (Eves). The scenario considered here is a multiple-input single-output wiretap channel with multiple Eves. This paper starts from AN design without any knowledge of Eve's CSI, builds with solutions with statistical CSI up to full CSI. Both perfect and imperfect channel information have been considered, in particular, with different extent of Eves' channel responses. The main objective is to improve the receive signal-to-interference and noise ratio at IR through exploitation of AN power in an attempt to minimize the total transmit power, while hindering detection at the Eves. Numerical simulations demonstrate that the proposed constructive AN precoding approach yields superior performance over conventional AN schemes in terms of transmit power. Critically, they show that, while the statistical constraints of conventional approaches may lead to instantaneous IR outages and security breaches from the Eves, the instantaneous constraints of our approach guarantee both IR performance and secrecy at every symbol period.

This paper considers a multiuser full-duplex (FD) wireless communication system, where an FD radio base station (BS) serves multiple single-antenna half-duplex uplink and downlink users simultaneously. Unlike conventional interference mitigation approaches, we propose using knowledge of the data symbols and the channel state information (CSI) at the FD radio BS to exploit the multi-user interference constructively rather than to suppress it. We propose a multi-objective optimization problem (MOOP) via the weighted Tchebycheff method to study the tradeoff between the two desirable system design objectives, namely the total downlink transmit power and the total uplink transmit power, at the same time ensuring the required quality-of-service (QoS) for all users. In the proposed MOOP, we adapt the QoS constraints for the downlink users to accommodate constructive interference for both generic phase shift keying modulated signals and for quadrature amplitude modulated signals. We also extended our work to a robust design to study the system with imperfect uplink and downlink CSI. The simulation results and analysis show that significant power savings can be obtained. More importantly, however, the MOOP approach here allows for the power saved to be traded off for both uplink and downlink power savings, leading to an overall energy efficiency improvement in the wireless link.

By taking a variety of realistic hardware imperfections into consideration, we propose an optimal power allocation (OPA) strategy to maximize the instantaneous secrecy rate of a cooperative wireless network comprised of a source, a destination, and an untrusted amplify-and-forward relay. We assume that either the source or the destination is equipped with a large-scale multiple antennas' system, while the rest are equipped with a single antenna. To prevent the untrusted relay from intercepting the source message, the destination sends an intended jamming noise to the relay, which is referred to as destination-based cooperative jamming. Given this system model, novel closed-form expressions are presented in the high signal-to-noise ratio regime for the ergodic secrecy rate and the secrecy outage probability. We further improve the secrecy performance of the system by optimizing the associated hardware design. The results reveal that by beneficially distributing the tolerable hardware imperfections across the transmission and reception radio-frequency front ends of each node, the system's secrecy rate may be improved. The engineering insight is that equally sharing the total imperfections at the relay between the transmitter and the receiver provides the best secrecy performance. Numerical results illustrate that the proposed OPA together with the most appropriate hardware design significantly increases the secrecy rate.

In this paper, we consider the power minimization problem in a multi-user full-duplex communication system by employing a multi-objective optimization problem via the weighted Tchebycheff method. We propose to exploit the multi-user interference by using the knowledge of the data symbols and channel state information at the full-duplex base station. Simulation results show that significant power savings can be obtained, which leads to substantial reduction of the self- interference power in the full-duplex systems.

Recent advances in interference exploitation showed that exploiting knowledge of interference constructively can improve the receive signal-to-interference and noise ratio (SINR) at the destination. This paper exploits this concept to design artificial noise (AN) beamformers constructive to the intended receiver (IR) yet keeping AN disruptive to possible eavesdroppers (Eves). A multiple-input single-output (MISO) wiretap channel with multiple eavesdroppers scenario has been investigated taking both perfect and imperfect channel information into consideration. The main objective is to improve the receive SINR at the IR through exploitation of AN power in an attempt to minimize the total transmit power, while confusing the Eves.

This paper considers simultaneous wireless information and power transfer in a multiple-input single-output downlink system consisting of one multi-antenna transmitter, one single-antenna information receiver, multiple multi-antenna eavesdroppers (Eves), and multiple single-antenna energy-harvesting receivers (ERs). The main objective is to keep the probability of the legitimate user's achievable secrecy rate outage as well as the ERs' harvested energy outage caused by channel state information uncertainties below some prescribed thresholds. As is well known, the secrecy rate outage constraints present a significant analytical and computational challenge. Incorporating the energy harvesting outage constraints only intensifies that challenge. In this paper, we address this challenging issue using convex restriction approaches which are then proved to yield rank-one optimal beamforming solutions. Numerical results reveal the effectiveness of the proposed schemes.

This paper brings the novel idea of paying the utility to the winning agents in terms of some physical entity in cooperative communications. Our setting is a secret two-way communication channel where two transmitters exchange information in the presence of an eavesdropper. The relays are selected from a set of interested parties, such that the secrecy sum rate is maximized. In return, the selected relay nodes' energy harvesting requirements will be fulfilled up to a certain threshold through their own payoff so that they have the natural incentive to be selected and involved in the communication. However, relays may exaggerate their private information in order to improve their chance to be selected. Our objective is to develop a mechanism for relay selection that enforces them to reveal the truth since otherwise they may be penalized. We also propose a joint cooperative relay beamforming and transmit power optimization scheme based on an alternating optimization approach. Note that the problem is highly non-convex, since the objective function appears as a product of three correlated Rayleigh quotients. While a common practice in the existing literature is to optimize the relay beamforming vector for given transmit power via rank relaxation, we propose a second-order cone programming-based approach in this paper, which requires a significantly lower computational task. The performance of the incentive control mechanism and the optimization algorithm has been evaluated through numerical simulations.

This paper considers the problem of maximizing the sum-rate for simultaneous wireless information and power transfer (SWIPT) in a full-duplex bi-directional communication system subject to energy harvesting and transmit power constraints at both nodes. We investigate the optimum design of the receive power splitters and transmit powers for SWIPT in full-duplex mode. Exploiting rate-split method, an iterative algorithm is derived to solve the non-convex problem. The effectiveness of the proposed algorithm is justified through numerical simulations.

This paper considers simultaneous wireless information and power transfer (SWIPT) in multiple-input single-output (MISO) downlink systems in which a multi-antenna transmitter sends a secret message to a single-antenna information receiver (IR) and power to multiple single-antenna energy receivers (ERs) in presence of external eavesdroppers. We aim to maximize the harvested energy by the ERs while maintaining the signal-to-interference and noise ratio (SINR) threshold at the IR and keeping the message secure from possible eavesdropping by the malicious attackers by interfering their reception. Both scenarios of perfect and imperfect channel state information (CSI) at the transmitter are studied. Using semidefinite relaxation (SDR) techniques, we show that a rank-one optimal beamforming solution can always be constructed for the IR.

This paper considers a multiple-input single-output downlink system consisting of one multiantenna transmitter, one single-antenna information receiver (IR), and multiple single-antenna energy-harvesting receivers (ERs) for simultaneous wireless information and power transfer. The design is to keep the message secret to the ERs while maximizing the information rate at the IR and meeting the energy harvesting constraints at the ERs. Technically, our objective is to optimize the information-bearing beam and artificial noise energy beam for maximizing the secrecy rate of the IR subject to individual harvested energy constraints of the ERs for the case where the ERs can collude to perform joint decoding in an attempt to illicitly decode the secret message to the IR. As a by-product, we also solve the total power minimization problem subject to secrecy rate and energy harvesting constraints. Both scenarios of perfect and imperfect channel state information (CSI) at the transmitter are addressed. For the imperfect CSI case, we study both eavesdroppers' channel covariance-based and worst case-based designs. Using semidefinite relaxation (SDR) techniques, we show that there always exists a rank-one optimal transmit covariance solution for the IR. Furthermore, if the SDR results in a higher rank solution, we propose an efficient algorithm to always construct an equivalent rank-one optimal solution. Computer simulations are carried out to demonstrate the performance of the proposed algorithms.

In this paper, we consider a dual-hop multicasting multiple-input multiple-output (MIMO) relay system where multiple transmitters multicast their own messages to a group of receivers with the aid of a relay node, and all nodes are equipped with multiple antennas. We aim at minimizing the maximal MSE of the signal waveform estimation among all receivers subjecting to power constraints at the transmitters and the relay node. We propose a low complexity solution for the problem under some mild approximation. In particular, we show that under (moderately) high signal-to-noise ratio (SNR) assumption, the min-max optimization problem can be solved using the semidefinite programming (SDP) technique. Numerical simulations demonstrate the effectiveness of the proposed algorithm.

In this paper, we consider a multicasting multiple-input multiple-output (MIMO) relay system where multiple transmitters multicast their own messages to a group of receivers over multiple hops, and all nodes are equipped with multiple antennas. The joint transmit and relay precoding design problem has been investigated for multicasting multiple data streams based on min-max mean-squared error (MSE) criterion. We aim at minimizing the maximal MSE of the signal waveform estimation among all receivers subjecting to power constraints at the transmitters and all the relay nodes. This problem is highly nonconvex with matrix variables and the exactly optimal solution is very hard to obtain. We develop an iterative algorithm to jointly optimize the transmitter, relay, and receiver matrices by solving convex subproblems. By exploiting the optimal structure of the relay precoding matrices, we then propose a low complexity solution for the problem under some mild approximation. In particular, we show that under (moderately) high signal-to-noise ratio assumption, the min-max optimization problem can be solved using the semidefinite programming technique. Numerical simulations demonstrate the effectiveness of the proposed algorithms.

In this paper, we consider a two-hop multicasting multiple-input multiple-output (MIMO) relay system where one transmitter multicasts common message to multiple receivers with the aid of a relay node, and all nodes are equipped with multiple antennas. Joint transmit and relay precoding design problems are investigated for multicasting multiple data streams based on two design criteria. In the first scheme, we aim at minimizing the maximal mean-squared error (MSE) of the signal waveform estimation among all receivers subjecting to power constraints at the transmitter and the relay node. This problem is highly nonconvex with matrix variables and the exactly optimal solution is very hard to obtain. We develop an iterative algorithm to jointly optimize the transmitter, relay, and receiver matrices through solving convex subproblems. By exploiting the optimal structure of the relay precoding matrix, we then propose a low complexity solution which decouples the optimization of the transmitter and relay matrices under the (moderately) high first-hop signal-to-noise ratio (SNR) assumption. In the second scheme, we propose a total transmission power minimization strategy subjecting to quality-of-service (QoS) constraints. By using the optimal structure of the relay precoding matrix and the (moderately) high first-hop SNR assumption, we show that this problem can be solved using the semidefinite programming (SDP) technique. Numerical simulations demonstrate the effectiveness of the proposed algorithms. Interestingly, we show that for the special case of single data stream multicasting, the relay precoding matrix optimization problem can be equivalently converted to the transmit beamforming problem for single-hop multicasting systems.

The optimal source precoding matrix and relay amplifying matrix have been developed in recent works on multiple-input multiple-output (MIMO) relay communication systems assuming that the instantaneous channel state information (CSI) is available. However, in practical relay communication systems, the instantaneous CSI is unknown, and therefore, has to be estimated at the destination node. In this paper, we develop a novel channel estimation algorithm for two-hop MIMO relay systems using the parallel factor (PARAFAC) analysis. The proposed algorithm provides the destination node with full knowledge of all channel matrices involved in the communication. Compared with existing approaches, the proposed algorithm requires less number of training data blocks, yields smaller channel estimation error, and is applicable for both one-way and two-way MIMO relay systems with single or multiple relay nodes. Numerical examples demonstrate the effectiveness of the PARAFAC-based channel estimation algorithm.

In this paper, we consider an interference multiple-input multiple-output (MIMO) relay system where multiple source nodes communicate with their desired destination nodes with the aid of distributed relay nodes. An iterative algorithm is developed to minimize the total source and relay transmit power such that a minimum signal-to-interference-plus-noise ratio (SINR) threshold is maintained at each receiver. The proposed algorithm exploits the network beamforming technique at the relay nodes and the receive beamforming technique at the destination nodes to mitigate the interferences from the unintended sources in conjunction with transmit power control. In particular, we apply the semidefinite relaxation technique to transform the relay transmission power minimization problem into a semidefinite programming (SDP) problem which can be efficiently solved by interior point-based methods. Numerical simulations are performed to demonstrate the effectiveness of the proposed iterative algorithm.

The optimal source precoding matrix and relay amplifying matrix have been developed in recent works on multiple-input multiple-output (MIMO) relay communication systems assuming that the instantaneous channel state information (CSI) is available. However, in practical relay communication systems, the instantaneous CSI is unknown, and therefore, has to be estimated at the destination node. In this paper, we develop a novel channel estimation algorithm for two-hop MIMO relay systems using the parallel factor (PARAFAC) analysis. The proposed algorithm provides the destination node with full knowledge of all channel matrices involved in the communication. Compared with existing approaches, the proposed algorithm requires less number of training data blocks, and is applicable for both one-way and two-way MIMO relay systems with single or multiple relay nodes. Numerical examples demonstrate the effectiveness of the PARAFAC-based channel estimation algorithm.

In this paper, we develop the optimal transmit beamforming vector and the relay amplifying factors for a multiple-input multiple-output (MIMO) relay communication system with distributed relay nodes. Using the optimal beamforming vector, an iterative joint source and relay beamforming algorithm is developed to minimize the mean-squared error (MSE) of the signal waveform estimation. Numerical simulations are carried out to demonstrate the performance of the proposed joint source and relay beamforming algorithm.

For two-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay systems, the uplink-downlink duality has been recently investigated. In this paper, we establish the duality between uplink and downlink multi-hop AF-MIMO relay channels with any number of hops and any number of antennas at each node, which is a further generalization of several previously established results. We show that in the downlink relay system, signal-to-interference-noise ratios (SINRs) identical to those in the uplink relay system, and vice versa, can be achieved by two approaches. First, with the same total network transmission power constraint, one simply applies Hermitian transposed uplink relay amplifying matrices at relay nodes in the downlink system. Second, with transmission power constraint at each node of the relay network, one can use scaled and Hermitian transposed uplink relay amplifying matrices in the downlink system, with scaling factors obtained by switching power constraints at different nodes of the uplink system. As an application of the uplink-downlink duality, we propose an optimal design of the source precoding matrix and relay amplifying matrices for multi-hop MIMO relay system with a dirty paper coding (DPC) transmitter at the source node.