Extremely large-scale arrays (XL-arrays) have emerged as a key enabler in achieving the unprecedented performance requirements of future wireless networks, leading to a significant increase in the range of the near-field region. This transition necessitates the spherical wavefront model for characterizing the wireless propagation rather than the far-field planar counterpart, thereby introducing extra degrees-of-freedom (DoFs) to wireless system design. In this paper, we explore the beam focusing-based physical layer security (PLS) in the near field, where multiple legitimate users and one eavesdropper are situated in the near-field region of the XL-array base station (BS). First, we consider a special case with one legitimate user and one eavesdropper to shed useful insights into near-field PLS. In particular, it is shown that 1) Artificial noise (AN) is crucial to near-fieldsecurity provisioning, transforming an insecure system to a secure one; 2) AN can yield numeroussecurity gains, which considerably enhances PLS in the near field as compared to the case without AN taken into account. Next, for the general case with multiple legitimate users, we propose an efficient low-complexity approach to design the beamforming with AN to guarantee near-field secure transmission. Specifically, the low-complexity approach is conceived starting by introducing the concept ofinterference domainto capture the inter-user interference level, followed by athree-step identification frameworkfor designing the beamforming. Finally, numerical results reveal that 1) the PLS enhancement in the near field is pronounced thanks to the additional spatial DoFs; 2) the proposed approach can achieve close performance to that of the computationally-extensive conventional method yet with a significantly lower computational complexity.

This paper investigates the energy-efficient hybrid beamforming design for a multi-functional integrated sensing, communications, and powering (ISCAP) system. In this system, a base station (BS) with a hybrid analog-digital (HAD) architecture sends unified wireless signals to communicate with multiple information receivers (IRs), sense multiple point targets, and wirelessly charge multiple energy receivers (ERs) at the same time. To facilitate the energy-efficient design, we present a novel HAD architecture for the BS transmitter, which allows dynamic on-off control of its radio frequency (RF) chains and analog phase shifters (PSs) through a switch network. We also consider a practical and comprehensive power consumption model for the BS, by taking into account the power-dependent non-linear power amplifier (PA) efficiency, and the on-off non-transmission power consumption model of RF chains and PSs. We jointly design the hybrid beamforming and dynamic on-off control at the BS, aiming to minimize its total power consumption, while guaranteeing the performance requirements on communication rates, sensing Cramér-Rao bound (CRB), and harvested power levels. The formulation also takes into consideration the per-antenna transmit power constraint and the constant modulus constraints for the analog beamformer at the BS. The resulting optimization problem for ISCAP is highly non-convex due to the binary on-off non-transmission power consumption of RF chains and PSs, the non-linear PA efficiency, and the coupling between analog and digital beamformers. To tackle this problem, we first approximate the binary on-off non-transmission power consumption into a continuous form, and accordingly propose an iterative algorithm to find a high-quality approximate solution with ensured convergence, by employing techniques from alternating optimization (AO), sequential convex approximation (SCA), and semi-definite relaxation (SDR). Then, based on the optimized beamforming weights, we develop an efficient method to determine the binary on-off control of RF chains and PSs, as well as the associated hybrid beamforming solution. Numerical results show that the proposed design achieves an improved energy efficiency for ISCAP than other benchmark schemes without joint design of hybrid beamforming and dynamic on-off control. This validates the benefit of dynamic on-off control in energy reduction, especially when the multi-functional performance requirements become less stringent.

Integrated sensing and communication (ISAC) will become an important feature in the future wireless communication networks. To achieve the high-rate communication and high-precision sensing, joint design of beamforming and power allocation is essential in the ISAC-assisted vehicular networks. This paper studies the downlink ISAC-assisted beamforming design and power allocation in the vehicle-to-infrastructure (V2I) communication networks to maximize the achievable sum-rate while guaranteeing the targeted sensing accuracy. The Cramer-Rao lower bounds (CRLBs) are introduced to characterize the estimation performance of the angle and distance between the target vehicle and the roadside unit (RSU). Due to the non-convex CRLBs sensing constraints, we propose an intelligent scheme based on deep reinforcement learning (DRL) to design the beamforming and allocate the power. In this scheme, the reward function is related to the communication sum-rate and CRLBs sensing constraints. Simulation results show that the proposed scheme significantly improves both communication and sensing performance compared to the benchmark schemes.

This paper studies the transmit energy beamforming in a multi-antenna wireless power transfer (WPT) system, in which an access point (AP) equipped with a uniform linear array (ULA) sends radio signals to wirelessly charge multiple single-antenna energy receivers (ERs). Different from conventional energy beamforming designs that require the AP to acquire the channel state information (CSI) via training and feedback, we propose a new training-free energy beamforming approach assisted by wireless radar sensing, which is implemented based on the following two-stage protocol. In the first stage, the AP performs wireless radar sensing to estimate the path gain and angle parameters of the ERs for constructing the corresponding CSI. In the second stage, the AP implements the transmit energy beamforming based on the constructed CSI to efficiently charge these ERs in a fair manner. Under this setup, first, we jointly optimize the sensing beamformers and duration in the first stage to minimize the sensing duration, while ensuring a given accuracy threshold for parameters estimation subject to the maximum transmit power constraint at the AP. Next, we optimize the energy beamformers in the second stage to maximize the minimum harvested energy by all ERs. In this approach, the estimation accuracy threshold for the first stage is properly designed to balance the resource allocation between the two stages for optimizing the ultimate energy harvesting performance. Finally, numerical results show that the proposed training-free energy beamforming design performs close to the performance upper bound with perfect CSI, and outperforms the benchmark schemes without such joint optimization and that with isotropic transmission.

This paper investigates the energy efficiency of a multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) system for Internet of things (IoT), in which one multi-antenna IoT transceiver transmits unified ISAC signals to a multi-antenna communication user (CU) and at the same time use the echo signals to estimate an extended target. We focus on one particular ISAC transmission block and take into account the practical on-off non-transmission power at the IoT transceiver. Under this setup, we minimize the energy consumption at the transceiver while ensuring a minimum average data rate requirement for communication and a maximum Cramér-Rao bound (CRB) requirement for target estimation, by jointly optimizing the transmit covariance matrix and the “on” duration for active transmission. We obtain the optimal solution to the rate-and-CRB-constrained energy minimization problem in a semi-closed form. Interestingly, the obtained optimal solution is shown to unify the spectrum-efficient and energy-efficient communications and sensing designs. In particular, for the special MIMO sensing case with rate constraint inactive, the optimal solution follows the isotropic transmission with shortest “on” duration, in which the IoT transceiver radiates the required sensing energy by using sufficiently high power over the shortest duration. For the general ISAC case, the optimal transmit covariance solution is of full rank and follows the eigenmode transmission based on the communication channel, while the optimal “on” duration is determined based on both the rate and CRB constraints. Numerical results show that the proposed ISAC design achieves significantly reduced energy consumption as compared to the benchmark schemes based on isotropic transmission, always-on transmission, and sensing or communications only designs, especially when the rate and CRB constraints become stringent.

This paper studies a multi-intelligent-reflecting-surface-(IRS)-enabled integrated sensing and communications (ISAC) system, in which multiple IRSs are installed to help the base station (BS) provide ISAC services at separate line-of-sight (LoS) blocked areas. We focus on the scenario with semi-passive uniform linear array (ULA) IRSs for sensing, in which each IRS is integrated with dedicated sensors for processing echo signals, and each IRS simultaneously serves one sensing target and multiple communication users (CUs) in its coverage area. We consider two cases with point and extended targets, in which each IRS aims to estimate the target direction-of-arrival (DoA) and the complete target response matrix, respectively. Under this setup, we first derive the closed-form Cramér-Rao bounds (CRBs) for parameter estimation under the two target models. Then, we assume that the BS sends combined information and dedicated sensing signals for ISAC, and accordingly consider two different types of CU receivers that can and cannot cancel the interference from dedicated sensing signals. Under this setup, we minimize the maximum CRB at all IRSs, via jointly optimizing the transmit beamformers at the BS and the reflective beamformers at the multiple IRSs, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints at individual CUs, the maximum transmit power constraint at the BS, and the unit-modulus constraints at the multiple IRSs. To tackle the highly non-convex SINR-constrained max-CRB minimization problems, we propose efficient algorithms based on alternating optimization and semi-definite relaxation, to obtain converged solutions. Finally, numerical results are provided to verify the benefits of our proposed designs over various benchmark schemes based on separate or heuristic beamforming designs.

This paper studies a multi-antenna networked integrated sensing and communications (ISAC) system, in which a set of multi-antenna base stations (BSs) employ the coordinated transmit beamforming to serve multiple single-antenna communication users (CUs) and concurrently perform joint target detection by exploiting the echo signals. To facilitate target sensing, the BSs transmit dedicated sensing signals combined with their information signals. We consider two types of CU receivers with and without the capability of canceling the interference from the dedicated sensing signals, respectively. We also investigate two scenarios with and without time synchronization among the BSs. For the scenario with synchronization, the BSs can exploit the target-reflected signals over both the direct links (BS-to-target-to-originated-BS links) and the cross-links (BS-to-target-to-other-BSs links) for joint detection, while in the unsynchronized scenario, the BSs can only utilize the target-reflected signals over the direct links. For each scenario under different types of CU receivers, we optimize the coordinated transmit beamforming at the BSs to maximize the minimum detection probability over a particular targeted area, while guaranteeing the required minimum signal-to-interference-plus-noise ratio (SINR) constraints at the CUs. These SINR-constrained detection probability maximization problems are recast as non-convex quadratically constrained quadratic programs (QCQPs), which are then optimally solved via the semi-definite relaxation (SDR) technique. Numerical results show that for each considered scenario, the proposed ISAC design achieves enhanced target detection probability compared with various benchmark schemes. In particular, enabling time synchronization and sensing signal cancellation at the BSs is always beneficial for further improving the joint detection and communication performance.

This paper investigates the performance tradeoff for a multi-antenna integrated sensing and communication (ISAC) system with simultaneous information multicasting and multi-target sensing, in which a multi-antenna base station (BS) sends the common information messages to a set of single-antenna communication users (CUs) and estimates the parameters of multiple sensing targets based on the echo signals concurrently. We consider two target sensing scenarios without and with prior target knowledge at the BS, in which the BS is interested in estimating the complete multi-target response matrix and the target reflection coefficients/angles, respectively. First, we consider the capacity-achieving transmission and characterize the fundamental tradeoff between the achievable rate and the multi-target estimation Cramér-Rao bound (CRB) accordingly. To this end, we design the optimal transmit signal covariance matrix at the BS to minimize the estimation CRB for each of the two scenarios, subject to the minimum multicast rate requirement and the maximum transmit power constraint. It is shown that the optimal covariance matrix consists of two parts for ISAC and dedicated sensing, respectively. Next, we consider the transmit beamforming designs, in which the BS sends one information beam together with multiple a-priori known dedicated sensing beams for effective ISAC and each CU can cancel the interference caused by the sensing signals. By exploiting the successive convex approximation (SCA) technique, we develop efficient algorithms to obtain the joint information and sensing beamforming solutions to the resultant rate-constrained CRB minimization problems. Finally, we provide numerical results to validate the CRB-rate (C-R) tradeoff achieved by our proposed designs, as compared to two benchmark schemes, namely the isotropic transmission and the joint beamforming without sensing interference cancellation. It is shown that the proposed optimal transmit covariance solution achieves much better C-R performance than the benchmark schemes and the proposed joint beamforming with sensing interference cancellation performs close to the optimal transmit covariance solution when the number of CUs is small. We also conduct simulations to show the practical estimation performance achieved by our proposed designs, by considering randomly generated information signals and practical estimators.

This paper studies a networked integrated sensing and communication (ISAC) system, where distributed base stations (BSs) implement coordinated transmit beamforming to communicate with their respective user and cooperatively perform multi-static target sensing. To fully reap the performance gains provided by the networked ISAC system, accurate channel state information (CSI) and time synchronization (TS) among distributed BSs are crucial. However, CSI errors and TS errors are inevitable in practice due to the imperfect channel training and the inaccurate synchronization. To reveal the effect of CSI errors on communication, a Gaussian distributed CSI error model is formulated based on the channel estimation process, and accordingly, the users’ achievable rates with CSI errors are derived. To characterize the effect of TS errors on multi-static sensing, the Cramér-Rao lower bound (CRLB) for estimating target position in the presence of TS errors is derived. It is shown that due to the existence of CSI errors and TS errors, additional terms are introduced in the achievable rate and CRLB formulas, degrading the communication and sensing performance, respectively. Based on the above derivations, we aim at maximizing the sum-rate of users by designing the coordinated transmit beamforming at the BSs, while guaranteeing the CRLB requirements for target sensing. In particular, we consider two cases with and without TS errors, for which the corresponding non-convex optimization problems are solved via a penalty-based algorithm and an alternating optimization algorithm, respectively. Simulation results show that the proposed algorithms significantly outperform benchmark schemes for both cases with and without CSI/TS errors, thus validating the robustness in ISAC performance optimization.

This paper studies a multi-intelligent-reflecting-surface (IRS)-enabled integrated sensing and communications (ISAC) system, in which multiple IRSs are installed to help a base station (BS) provide ISAC services at the line-of-sight (LoS) blocked areas. In particular, we consider the case with semi-passive uniform linear array (ULA) IRSs each integrated with dedicated sensors for receiving echo signals, in which each IRS simultaneously senses one point target and communicates with one communication user (CU) within its coverage area. Under this setup, we first derive the closed-form Cramér-Rae bound (CRB) for the targets' direction-of-arrival (DoA) estimation at the corresponding IRSs. Then, to achieve fair and optimal sensing performance, we minimize the maximum CRB for targets' DoA estimation at all IRSs, by jointly optimizing the transmit beamformers at the BS and the reflective beamformers at the IRSs, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints at individual CUs, the maximum transmit power constraint at the BS, and the unit-modulus constraints at the IRSs. To tackle the highly non-convex SINR-constrained max-CRB minimization problem, we propose an efficient algorithm based on alternating optimization and semi-definite relaxation, to obtain a converged solution. Finally, numerical results are provided to verify the effectiveness of our proposed design over various benchmark schemes based on separate or heuristic beamforming designs.

This paper studies an uplink rate splitting multiple access (RSMA) enabled integrated sensing and communication (ISAC) system, where a radar-communication (RadCom) base station (BS) receives the signals from communication users (CUs) while simultaneously sensing the target. By utilizing the rate splitting (RS) paradigm to split the CU with better channel condition into two virtual CUs, the inter-user interference is mitigated substantially and a higher sensing performance is achieved with determinate communication performance requirements. Under this consideration, we jointly optimize the transmit and receive beamforming to maximize the radar signal-to-interference-plus-noise ratio (SINR) while ensuring to the CUs’ rate requirements as well as the transmit power constraints of BS and CUs. However, due to the coupled variables and non-convex constraints, the formulated problem is non-convex and difficult to solve. To tackle this problem, we propose an effective alternating optimization (AO) algorithm, specifically, in each iteration, the optimal transmit radar waveform is derived via the rank-1 guaranteed semidefinite relaxation (SDR). Simulation results are provided to validate the performance of our proposed design and it is shown that RSMA is effective and robust for suppressing interference.

This paper studies a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) sends unified wireless signals to estimate an extended target and communicate with a multi-antenna communication user (CU) at the same time. We investigate the fundamental tradeoff between the estimation Cramér-Rao bound (CRB) for sensing and the data rate for communication, by characterizing the Pareto boundary of the achievable CRB-rate (C-R) region. Towards this end, we formulate a new MIMO rate maximization problem by optimizing the transmit covariance matrix at the BS, subject to a new form of maximum CRB constraint together with a maximum transmit power constraint. We derive the optimal transmit covariance solution in a semi-closed form, by first implementing the singular-value decomposition (SVD) to diagonalize the communication channel and then properly allocating the transmit power over these subchannels for communication and other orthogonal subchannels (if any) for dedicated sensing. It is shown that the optimal transmit covariance is of full rank, which unifies the conventional rate maximization design with water-filling power allocation and the CRB minimization design with isotropic transmission. Numerical results are provided to validate the performance achieved by our proposed optimal design, in comparison with other benchmark schemes.

In this paper, we investigate the effect of cooperative double Intelligent reflecting surfaces (IRS) in improving the energy efficiency (EE) of a multi-user multiple-input multiple-output (MIMO) system. Under the general channel setup with the co-existence of both double-IRS and single-IRS reflection links, we aim to maximize the total EE of the system by jointly designing the transmit beamforming at the base station (BS) and the reflecting beamforming at the IRSs. The proposed problem is subject to the transmit power constraint at the BS, the individual minimum signal-to-interference-plus-noise ratio (SINR) requirements of the users and the unit modulus constraints of the IRS phase shifts. Due to the non-convex constraints and the coupling between variables, we decompose the original problem into two subproblems and propose an effective alternating optimization (AO) algorithm based on the techniques of fractional programming (FP) and semi-definite relaxation (SDR) to solve it. Simulation results validate the significant advantages of the proposed algorithm over benchmark schemes, as well as the effectiveness of cooperative double-IRS deployment in improving the EE performance of the system.

In this work, we investigate an uplink unmanned aerial vehicles (UAVs)-enabled intelligent transportation system to collect data from traveling vehicles on a specific highway road. To ensure the freshness of information delivered from the traveling vehicles to UAV base stations, we use the new age of information (AoI) metric to characterize the information freshness and formulate the AoI minimization problem by optimizing the UAVs’ trajectories and the communication time of vehicles jointly. In order to handle the mixed-integer nonlinear problem, a multi-agent deep reinforcement learning scheme is proposed by applying independent flight direction and time slot action spaces, in which each UAV working as an independent agent adjusts to the dynamic environment quickly based on stored experience. The AoI-related reward function is proposed to select the beneficial action space to guarantee the information freshness. Numerical simulation results show the proposed scheme outperforms the benchmark schemes.

In this paper, we study a downlink unmanned aerial vehicle (UAV)-enabled simultaneous wireless information and power transfer (SWIPT) system with the aid of an intelligent reflecting surface (IRS). Considering the time switching (TS) scheme, the UAV is leveraged to charge a single battery limited smart device (SD) and for information transmission simultaneously. We aim to maximize the average harvested energy of the SD over a finite mission/communication period on the conditions of average data transmission requirement and other practical constraints, by jointly optimizing the UAV trajectory, TS factors, and the phase shifts of IRS. In existing literatures, the block coordinate descent and successive convex approximation (BCD-SCA) method is usually applied to obtain a local optimal solution, however, the solution is susceptible to the initial parameter settings. Therefore, in this paper, we obtain a more advantageous solution by using the Lagrange dual method. Numerical results show that under the same constraints, the proposed design scheme improves the average harvested energy compared with the BCD-SCA method, and complexity analysis indicates that our algorithm has lower computation complexity.

In this paper, we investigate an unmanned aerial vehicle (UAV)-enabled secure communication system, where ground nodes send confidential information to a legitimate UAV by time division multiple access in the presence of a smart UAV eavesdropper. It is a practical scenario that the UAV eavesdropper will make full use of its mobility for more effective eavesdropping and its trajectory can not be obtained in advance. Firstly, the problem of maximizing the sum secrecy rate by jointly optimizing the legitimate UAV trajectory, transmit power control and node scheduling is formulated from perspective of legitimate UAV. Next, due to the presence of smart eavesdropper and time-varying environment caused by uncontrollable mobility of UAV eavesdropper, we reformulated the original problem as a two-player zero-sum stochastic game (TZSG) problem. In order to solve the TZSG problem, considering competitive scenario and the mixed action space of the TZSG, we propose an algorithm based on multi-agent deep reinforcement learning to obtain a policy of legitimate communication link nodes and simulation results verify the proposed algorithm has superior performance than benchmark algorithm.

In this paper, we investigate a secure unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) system, where multiple ground users and a UAV collaborate to complete computing tasks in the presence of multiple eavesdroppers on the ground with exact locations. The UAV acts as a computing server and users can offload part of computing tasks to the air in order to relieve calculation pressure. The total energy consumption of the UAV is minimized by jointly optimizing the task allocation, the transmit power of each user equipment, and the trajectory of UAV, subject to the secrecy offloading rate constrains, the transmit power and UAV's trajectory constrains. To solve the difficult nonconvex optimization problem, an alternating algorithm based on the successive convex approximation is proposed to optimize the parameters iteratively. The simulation results demonstrate that the developed algorithm is effective and can decrease the energy consumption compared with other benchmark methods.

In this paper, we propose a novel deep learning-based channel estimation scheme in an orthogonal frequency division multiplexing (OFDM) system. The channel response with known pilot positions can be treated as a low-resolution image. Then, we explore a generative adversarial network (GAN) for channel super-resolution (SR) to estimate the whole channel state information (CSI). For previous deep learning-based channel estimators recovered by a single model, high-frequency details are missing and they fail to match the fidelity expected at the higher resolution. The scheme we proposed is more consistent with the real channel by adding a discriminator to recover more details of the channel. The simulation results show that our scheme is superior to other SR-based channel estimation methods and close to the linear minimum mean square error (LMMSE) performance.

Massive multiple-input multiple-output (MIMO) systems with a large number of base station (BS) antennas can provide significant spectral and energy efficiency. However, phase noise introduced by the impairment of oscillators can cause a severe performance loss in wireless communication systems. In this paper, we study the effect of phase noise on uplink MIMO systems with imperfect channel state information. We consider a setup that the BS employs different numbers of free- running oscillators. The asymptotic equivalent (AE) expressions of signal-to-interference-plus-noise ratio (SINR) for matched filter (MF) and minimum mean squared error (MMSE) receivers are derived by using random matrix theory, respectively. Based on the AE expressions, we show that the system performance degrades as the number of oscillators increases. Then, we demonstrates a phase noise impact comparison between the MF and MMSE receivers. The MF receiver shows greater robustness but a lower sum rate performance than the MMSE receiver under the effect of phase noise. Moreover, we investigate the relationship between the sum rate gain and the data transmission interval. Due to the phase noise, the length of the data transmission interval is limited. The derived AE expressions provide valuable insights on the effect of various parameters on the system performance and the optimum choice of the data transmission interval length.