Networked integrated sensing and communication (ISAC) has emerged as a pivotal paradigm for next-generation wireless networks, where dedicated target monitoring terminals (TMTs) can be extensively leveraged for their low-cost flexible deployment and capability to facilitate bistatic and multistatic sensing. Nevertheless, the coordinated beamforming design for networked ISAC tailored for time-of-arrival (ToA)-based multi-TMT localization remains largely unexplored. To address this gap, we present a comprehensive study in this paper. Specifically, we first establish signal models for both communication and localization, and, for the first time, derive a closed-form Cramér-Rao lower bound (CRLB) to quantify the localization performance. Leveraging this CRLB, we formulate two optimization problems focusing on sensing-centric and communication-centric criteria, respectively, to thoroughly investigate the fundamental communication-localization trade-offs. For the sensing-centric problem, we develop a globally optimal algorithm based on semidefinite relaxation (SDR), applicable to scenarios where the number of BS antennas exceeds the total number of communication users. In parallel, for the communication-centric problem, we design a globally optimal algorithm for the single-BS case utilizing bisection search. To address the general cases of both problems, we propose a unified and efficient successive convex approximation (SCA)-based algorithm, which is further extended to multi-target scenarios. Finally, simulation results demonstrate the effectiveness of our proposed algorithms, reveal the intrinsic trade-offs between communication and localization, and further show that deploying more TMTs is more beneficial than deploying more BSs in networked ISAC systems.

In this paper, we focus on beamforming optimization for a multiuser and multi-target integrated sensing and communication (ISAC) system with non-ideal hardware at both the base station (BS) and the users. Specifically, by taking into account the impact of hardware impairments encountered in practice, we jointly optimize the transmit and receive beamforming at the ISAC BS to maximize the minimum radar output signal-to-interference-plus-noise ratio (SINR) for the multi-target sensing, subject to the constraints of multiuser communication requirements and transmit power limit. The formulated joint optimization is nonconovex and challenging to solve. To address this intricate optimization task, we start with a single-target scenario, for which we propose an optimal solution. In particular, we prove in theory that, even in the presence of general additive Gaussian distortions caused by transceiver hardware impairments, a matched filter (MF) radar receiver and a transmit beamforming determined through beampattern gain maximization criterion are optimal, which follow the same strategies as an ideal scenario with perfect hardware. Subsequently, for a general multi-target scenario, we derive a series of closed-form optimal radar receive beamforming. By substituting these solutions, we achieve an equivalent problem reformulation with respect to the transmit beamforming and propose an iterative algorithm to solve it. We also extend the optimization method to cases involving more realistic hardware impairments. Finally, we evaluate the effectiveness of the proposed algorithms and highlight their notable advantages compared to existing approaches through simulation results.

This paper investigates integrated localization and communication in a multi-cell system, and proposes a coordinated beamforming algorithm to enhance target localization accuracy while preserving communication performance. Within this integrated sensing and communication (ISAC) system, the CramérRao lower bound (CRLB) is adopted to quantify the accuracy of target localization, with its closed-form expression derived for the first time. It is shown that the nuisance parameters can be disregarded without impacting the CRLB of time of arrival (TOA)based target localization. Capitalizing on the derived CRLB, we formulate a nonconvex coordinated beamforming problem to minimize the CRLB while satisfying signal-to-interference-plusnoise ratio (SINR) constraints in communication. To facilitate the development of solution, we reformulate the original problem into a more tractable form and solve it through semi-definite programming (SDP). Notably, we show that the proposed algorithm can always obtain rank-one global optimal solutions under mild conditions. Finally, numerical results demonstrate the superiority of the proposed algorithm over benchmark algorithms and reveal the performance trade-off between localization accuracy and communication SINR.

Index modulation based frequency hopping spread spectrum (IM-FHSS) has shown the attractive anti-jamming capability. With the aid of jamming detection, IM-FHSS becomes efficient to defend various malicious jamming attacks, especially the reactive jamming. In this paper, we propose two jamming detection approaches for IM-FHSS to efficiently detect reactive jamming. Specifically, the pilot symbols for channel estimation are used in the first detection approach, where a single frame to calculate the optimal test statistic and make the decision. Second, the proposed approach II collects the pilot symbols from many data frames to detect the reactive jamming. Moreover, we theoretically derive the closed-form expressions of the optimal thresholds and the probabilities of detection, and provide the complexity comparison. Simulation results validate the correctness of our performance analysis and show that the proposed approaches outperform the prior detection schemes.

In this paper, we investigate a multiuser integrated sensing and communication (ISAC) system with hardware im-pairments at both the base station (BS) transceiver and the users. Specifically, by considering the impact of hardware impairments, we optimize the transmit and receive beamforming at the ISAC BS to maximize the radar output signal-to-interference-plus-noise ratio (SINR) for sensing, under both point and extended target scenarios, subject to the constraints of communication requirement and power limitation. For both scenarios, we first find closed-form optimal radar receive beamforming and then obtain equivalent reformulations with respect to the transmit beamforming. Subsequently, for the resulting problems, a globally optimal solution is obtained for the point target scenario and an iterative solution is proposed for the extended target scenario. Finally, the effectiveness of the proposed methods is evaluated via simulation results.

Reconfigurable intelligent surface (RIS) is a promising technique to improve the performance of future wireless communication systems at low energy consumption. To reap the potential benefits of RIS-aided beamforming, it is vital to enhance the accuracy of channel estimation. In this paper, we consider an RIS-aided multiuser system with non-ideal reflecting elements, each of which has a phase-dependent reflecting amplitude, and we aim to minimize the mean-squared error (MSE) of the channel estimation by jointly optimizing the training signals at the user equipments (UEs) and the reflection pattern at the RIS. As examples the least squares (LS) and linear minimum MSE (LMMSE) estimators are considered. The considered problems do not admit simple solution mainly due to the complicated constraints pertaining to the non-ideal RIS reflecting elements. As far as the LS criterion is concerned, we tackle this difficulty by first proving the optimality of orthogonal training symbols and then propose a majorization-minimization (MM)-based iterative method to design the reflection pattern, where a semi-closed form solution is obtained in each iteration. As for the LMMSE criterion, we address the joint training and reflection pattern optimization problem with an MM-based alternating algorithm, where a closed-form solution to the training symbols and a semi-closed form solution to the RIS reflecting coefficients are derived, respectively. Furthermore, an acceleration scheme is proposed to improve the convergence rate of the proposed MM algorithms. Finally, simulation results demonstrate the performance advantages of our proposed joint training and reflection pattern designs.

In this paper, we investigate the beamforming design for an integrated sensing and communication (ISAC) system involved full-duplex (FD) communications. Specifically, an FD ISAC base station (BS) performs target detection and communicates with multiple downlink users and uplink users reusing the same time and frequency resources. We jointly optimize the downlink dual-functional transmit signal and the uplink receive beamformers at the BS and the transmit power at the uplink users. The problem is formulated to minimize the total transmit power of the system while ensuring the communication and sensing requirements. The downlink and uplink transmissions are tightly coupled, making the joint optimization challenging. To solve this intractable problem, we first determine the receive beamformers in closed forms with respect to the BS transmit beamforming and the user transmit power and then suggest an iterative solution to the remaining problem. We demonstrate via numerical results that the optimized FD communication-based ISAC leads to power efficiency improvement compared to conventional ISAC with HD communication.

In this article, we consider an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) and relay communication network consisting of a base station (BS), a UAV, and multiple ground users. The UAV acts as both a wireless power transmission source and an uplink communication relay. Specifically, an entire transmission period of the considered system is divided into two stages. In the first stage, the UAV transfers the power to the ground users along a well-optimized flight trajectory and meanwhile, the users transmit data to the UAV using the harvested energy. Subsequently, in the second stage, the UAV flies to the vicinity of the BS and forwards the data to the BS. For the purpose of minimizing the energy consumed by the UAV, we jointly optimize the time durations of the two stages, the UAV’s transmit powers for WPT and data forwarding, as well as its flight trajectory, subject to the constraints of the Quality of Service (QoS), the information forwarding, the energy causality, and the mobility of the UAV. The involved optimization problem is nonconvex and highly intractable. To this end, we propose an efficient alternating algorithm to iteratively solve the two subproblems with respect to the time durations of the two stages and the UAV’s transmit powers and trajectory, respectively. The first subproblem has a closed-form optimal solution and the second subproblem is handled by addressing a surrogate convex problem based on the technique of successive convex approximation. Finally, the simulation results confirm the superiority of our proposed algorithm.

Beamforming design has been widely investigated for integrated sensing and communication (ISAC) systems with full-duplex (FD) sensing and half-duplex (HD) communication, where the base station (BS) transmits and receives radar sensing signals simultaneously while the integrated communication operates in either downlink or uplink. To achieve higher spectral efficiency, in this paper, we extend existing ISAC beamforming design to a general case by considering the FD capability for both radar and communication. Specifically, we consider an FD ISAC system, where the BS performs target detection and communicates with multiple downlink users and uplink users reusing the same time and frequency resources. We jointly optimize the downlink dual-functional transmit signal and the uplink receive beamformers at the BS and the transmit power at the uplink users. The problems are formulated under two criteria: power consumption minimization and sum rate maximization. The downlink and uplink transmissions are tightly coupled due to both the desired target echo and the undesired interference received at the BS, making the problems challenging. To handle these issues in both cases, we first determine the optimal receive beamformers in closed forms with respect to the BS transmit beamforming and the user transmit power. Subsequently, we invoke these results to obtain equivalent optimization problems and propose iterative algorithms to solve them. In addition, we consider a special case under the power minimization criterion and propose an alternative low complexity design. Numerical results demonstrate that the optimized FD communication-based ISAC brings tremendous improvements in terms of both power efficiency and spectral efficiency compared to the conventional ISAC with HD communication.

The trend of developing distributed multiple-input multiple-output (MIMO) cooperation has been growing for future wireless networks due to its potential of capacity improvement through network-level precoding. In massive MIMO applications, the overhead of channel state information (CSI) exchange among distributed transmitters is too large to make it possible in practical implementations. In this paper, we consider a cooperative multicell massive MIMO network with distributed regularized zero-forcing (RZF) precoding at each base station (BS), where a novel CSI exchange scheme is devised to reduce the interactive overhead. As a key finding of this work, we theoretically prove that it suffices to share the Gram matrix of local CSI among the cooperative BSs in order to achieve the same performance as a centralized cooperative MIMO network using the RZF precoding with global CSI sharing. The CSI exchange from each BS is thus reduced to a symmetric matrix that has a much smaller size than the full CSI and the amount of CSI exchange does NOT grow with the large number of antennas in massive MIMO. Specifically, based on the exchanged Gram matrices, we derive a decentralized RZF precoding design at each BS and develop both the optimal and suboptimal cooperative power allocation strategies, which achieve different performance and complexity tradeoffs. A virtual centralized power allocation is accomplished at each BS and the performance achieved by the proposed decentralized precoding is the same as the centralized benchmark scheme with full CSI exchange. These superiorities of the proposed schemes are verified through simulation results.