Integrated sensing and communications (ISAC) is a key enabler for 6G applications such as drone surveillance, urban air mobility, and low-altitude logistics. However, the hybrid ISAC signal, which comprises deterministic pilots and random data payloads, poses challenges for target detection, since 1) these components jointly affect both the mean and variance of the received signal, and 2) the random data payloads are typically unknown to the sensing receiver in bistatic systems. To address these, we develop a generalized likelihood ratio test (GLRT)-based detector that exploits the known pilots and the statistical properties of the unknown payloads. Given the exact performance is analytically intractable, an asymptotic analysis of the false alarm probability is conducted. Simulation results validate the theoretical derivations and demonstrate the superiority of the proposed detector, which highlights the importance of tailored ISAC detection that fully leverages data payload resources.

In this paper, a decentralized iterative maximum likelihood (ML) direct position determination (DIM-DPD) algorithm is proposed based on the expectation maximization (EM) concept for user equipment (UE) localization in Wi-Fi systems. By innovatively treating the non-line-of-sight (NLoS) angles-of-arrival (AoAs) and observed times-of-arrival (ToAs) as nuisance parameters in the received signal model and parameter estimation procedure, the proposed DIM-DPD demonstrates its adaptability and localization efficiency in dense multipath indoor environments. In the proposed method, the position of the UE is incorporated in the vectors denoting the location differences between the UE and the access point (APs), referred to as the UE-AP location difference vectors. The set of UE-AP location difference vectors allows constructing the related UE-AP variables, serving as the latent variables in the EM iterations. Then, by taking advantage of the alternating projection technique, the nuisance parameters and UE-AP variables in the DIM-DPD algorithm are alternatively updated on separated APs in parallel. Furthermore, instead of traditional grid search, the UE position is updated with an efficient closed-form solution by aggregating the distributed estimated low-dimensional UE-AP variables. Thus, overall, the proposed DIM-DPD approach facilitates decentralized direct localization with implementation feasible processing complexity. The provided numerical simulation results demonstrate that the proposed DIM-DPD algorithm achieves high positioning accuracy, fast convergence, and a good balance between computational complexity and performance.

Target-mounted intelligent reflecting surfaces (IRS) introduce a novel degree of freedom (DoF) in controlling the target’s radar cross section (RCS), thereby enabling numerous advanced applications in wireless sensing and integrated sensing and communication (ISAC) systems. Nevertheless, a comprehensive analytical framework characterizing the impact of IRS reflection coefficients on wireless sensing performance remains largely unexplored in existing literature. To address this gap, this paper investigates sensing mutual information (SMI) in a general scenario where a sensing transmitter (TX) sends random signals to multiple targets each equipped with an IRS, and multiple sensing receivers (RXs) process the received echoes. We derive a closed-form tight upper bound on SMI and propose an efficient manifold optimization-based method to maximize it by jointly optimizing the transmit precoder and IRS reflection coefficients. Simulation results validate our analysis and demonstrate substantial enhancements in SMI achieved by the proposed method.

Sensing performance is typically evaluated by classical radar metrics, such as Cramér-Rao bound and signal-to-clutter-plus-noise ratio. The recent development of the integrated sensing and communication (ISAC) framework motivated the efforts to unify the performance metric for sensing and communication, where sensing mutual information (SMI) was proposed as a sensing performance metric withdeterministicsignals. However, the communication need in ISAC systems necessitates the transmission ofrandomsignals for sensing applications, whereas an explicit evaluation for the SMI with random signals is not yet available in the literature. This paper aims to fill the research gap and investigate the unification of sensing and communication performance metrics. For that purpose, we first derive the explicit expression for the SMI with random signals utilizing random matrix theory. On top of that, we further build up the connections between SMI and traditional sensing metrics, such as ergodic minimum mean square error (EMMSE), ergodic linear minimum mean square error (ELMMSE), and ergodic Bayesian Cram´er- Rao bound (EBCRB). Such connections open up the opportunity to unify sensing and communication performance metrics, which facilitates the analysis and design for ISAC systems. Finally, SMI is utilized to optimize the precoder for both sensing-only and ISAC applications. Simulation results validate the accuracy of the theoretical results and the effectiveness of the proposed precoding design.

Maneuvering target tracking is an important service of future wireless networks to assist innovative applications such as intelligent transportation. However, tracking maneuvering targets by cellular networks faces many challenges. In particular, the dense network and high-speed targets make the selection of the sensing nodes (SNs) and the associated power allocation very challenging. Existing methods demonstrated engaging performance, but with high computational complexity. In this paper, we propose a model-driven deep learning (DL)-based approach for SN selection. To this end, we first propose an iterative SN selection method by jointly exploiting the majorization-minimization (MM) framework and the alternating direction method of multipliers (ADMM). Then, we unfold the iterative algorithm as a deep neural network and prove its convergence. The proposed method achieves lower computational complexity, as the number of layers is less than the number of iterations required by the original algorithm, and each layer only involves simple matrix-vector additions/multiplications. Finally, we propose an efficient power allocation method based on fixed point (FP) water filling and solve the joint SN selection and power allocation problem under the alternative optimization framework. Simulation results show that the proposed method achieves better performance than conventional optimization-based algorithms with much lower computational complexity.

Sensing performance is typically evaluated by classical metrics, such as Cramer-Rao bound and signal- to-clutter-plus-noise ratio. The recent development of the integrated sensing and communication (ISAC) framework motivated the efforts to unify the metric for sensing and communication, where researchers have proposed to utilize mutual information (MI) to measure the sensing performance with deterministic signals. However, the need to communicate in ISAC systems necessitates the use of random signals for sensing applications and the closed-form evaluation for the sensing mutual information (SMI) with random signals is not yet available in the literature. This paper investigates the SMI and precoder design for sensing applications with random signals. For that purpose, we first derive the closed-form expression for the SMI with random signals by utilizing random matrix theory. The result reveals some interesting physical insights regarding the relation between the SMI with deterministic and random signals. The derived SMI is then utilized to optimize the precoder by leveraging a manifold-based optimization approach. The accuracy of the theoretical analysis and the effectiveness of the proposed precoder design method are validated by simulation results.

Maneuvering target tracking will be one of the es-sential applications for future perceptive mobile networks, where multiple sensing nodes (SNs) collaboratively track the same tar-get. However, the associated SN selection is a substantial challenge due to stringent latency requirements of sensing applications. In this paper, we propose a model-driven approach by unfolding conventional optimization-based methods to tackle this problem. To this end, we first propose an iterative selection method based on the majorization-minimization (MM) framework and the alternating direction method of multipliers (ADMM). Then, we unfold the MM-ADMM approach as a deep neural network (DNN) to reduce the computational complexity and improve the performance, by leveraging an enhanced surrogate function. Simulation results demonstrate that the unfolded DNN outper-forms conventional methods with much lower computational complexity.

Different from conventional radar, the cellular network structure integrated sensing and communication (ISAC) systems enables collaborative sensing by multiple sensing nodes, e.g., base stations (BSs). However, existing works normally assume designated BSs as the sensing nodes, and thus can't fully exploit the macro-diversity gain. In the paper, we propose a joint BS selection, user association, and beamforming design to tackle this problem. In particular, we minimize the total transmit power by the above-mentioned joint design, while guaranteeing the communication and sensing performance measured by the signal-to-interference-plus-noise ratio (SINR) for the communication users and the Cramer-Rae lower bound (CRLB) for location estimation, respectively. An alternating optimization (AO)-based algorithm is developed to solve the non-convex problem. Simulation results validate the effectiveness of the proposed algorithm and unveil the benefits brought by collaborative sensing and BS selection.

Sensing will become an important service of future wireless networks to assist innovative applications such as autonomous driving and environment monitoring. Perceptive mobile networks (PMNs) were proposed to incorporate sensing capability into current cellular networks. However, the interference management between sensing and communication, as well as the collaborative sensing by multiple sensing nodes (SNs), faces significant challenges. In this paper, we first propose a two-stage protocol to tackle the interference between two sub-systems, where the echoes created by communication signals, i.e., interference for sensing, are estimated in the clutter estimation (CE) stage and then utilized for interference management in the target sensing (TS) stage. Then, a networked sensing detector is derived to exploit the perspectives provided by multiple SNs for sensing the same target. The macro-diversity from multiple SNs, the array gain, and the higher angular resolution from multiple receive antennas of each SN are then investigated to reveal the benefit of networked sensing. Furthermore, we derive the sufficient condition for one SN’s contribution to be positive, based on which a SN selection algorithm is proposed. To reduce the communication workload, we propose a distributed model-driven deep-learning algorithm that utilizes partially-sampled data for CE. Simulation results demonstrate the benefits of networked sensing and validate the higher efficiency of the proposed CE algorithm than existing methods.

This article investigates regularized estimation of Kronecker-structured covariance matrices (CMs) for polarization radar in sea clutter scenarios where the data are assumed to follow the complex elliptically symmetric (CES) distributions with a Kronecker-structured CM. To obtain a well-conditioned estimate of the CM, we add penalty terms of Kullback–Leibler divergence to the negative log-likelihood function of the associated complex angular Gaussian (CAG) distribution. This is shown to be equivalent to regularizing Tyler’s fixed-point equations by shrinkage. A sufficient condition that the solution exists is discussed. An iterative algorithm is applied to solve the resulting fixed-point iterations, and its convergence is proven. In order to solve the critical problem of tuning the shrinkage factors, we then introduce two methods by exploiting oracle approximating shrinkage (OAS) and cross-validation (CV). The proposed estimator, referred to as the robust shrinkage Kronecker estimator (RSKE), is shown to achieve better performance compared with several existing methods when the training samples are limited. Simulations are conducted for validating the RSKE and demonstrating its high performance by using the IPIX 1998 real sea data.

Integrated sensing and communication (ISAC) creates a platform to exploit the synergy between two powerful functionalities that have been developing separately. However, the interference management and resource allocation between sensing and communication have not been fully studied. In this paper, we consider the design of perceptive mobile networks (PMNs) by adding sensing capability to current cellular networks. To avoid full-duplex operation, we propose the PMN with distributed target monitoring terminals (TMTs) where passive TMTs are deployed over wireless networks to locate the sensing target (ST). To manage the interference between sensing and communication, we jointly optimize the transmit and receive beamformers towards the communication user equipment (UEs) and the ST by alternating-optimization (AO) and prove its convergence. To reduce computation complexity and obtain physical insights, we further investigate the use of linear transceivers, including zero forcing and beam synthesis (B-syn). Our analysis revealed interesting physical insights: 1) instead of forming dedicated sensing signals, it is more efficient to redesign the communication signals for both communication and sensing purposes and “leak” communication energy for sensing; 2) the amount of energy leakage from one UE to the ST depends on their relative locations.

This paper considers the best channel selection for reduced-dimension space-time adaptive processing (STAP) in the presence of multiple targets. An algorithm based on semidefinite programming (SDP) is proposed to achieve the best channel selection by optimizing the worst case of the detection performance of the different targets. Compared with some existing algorithms which only consider the single-target case, the proposed algorithm can provide considerable performance improvements for detecting several different targets. Simulations are conducted for validating the proposed method and demonstrating their high performance.