Rang Liu

Integrated sensing and communication (ISAC) can substantially improve spectral, hardware, and energy efficiency by unifying radar sensing and data communications. In wideband and scattering-rich environments, clutter often dominates weak target reflections and becomes a fundamental bottleneck for reliable sensing. Practical ISAC clutter includes “cold” clutter arising from environmental backscatter of the probing waveform and “hot” clutter induced by external interference and reflections from the environment whose statistics can vary rapidly over time. In this article, we develop a unified wideband multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) signal model that captures both clutter types across the space, time, and frequency domains. Building on this model, we review clutter characterization at multiple levels, including amplitude statistics, robust spherically invariant random vector (SIRV) modeling, and structured covariance representations suitable for limited-snapshot regimes. We then summarize receiver-side suppression methods in the temporal and spatial domains, together with extensions to space–time adaptive processing (STAP) and space–frequency–time adaptive processing (SFTAP), and we provide guidance on selecting techniques under different waveform and interference conditions. To move beyond reactive suppression, we discuss clutter-aware transceiver co-design that couples beamforming and waveform optimization with practical communication quality-of-service (QoS) constraints to enable proactive clutter avoidance. We conclude with open challenges and research directions toward environment-adaptive and clutter-resilient ISAC for the next-generation networks.

Cell-free integrated sensing and communication (ISAC) systems have emerged as a promising paradigm for sixth-generation (6G) networks, enabling simultaneous high-rate data transmission and high-precision radar sensing through cooperative distributed access points (APs). Fully exploiting these capabilities requires a unified design that bridges system-level optimization with multi-target parameter estimation. This paper proposes an end-to-end graph learning approach to close this gap, modeling the entire cell-free ISAC network as a heterogeneous graph to jointly design the AP mode selection, user association, precoding, and echo signal processing for multi-target position and velocity estimation. In particular, we propose two novel heterogeneous graph learning frameworks: a dynamic graph learning framework and a lightweight mirror-based graph attention network (mirror-GAT) framework. The dynamic graph learning framework employs structural and temporal attention mechanisms integrated with a three-dimensional convolutional neural network (3D-CNN), enabling superior performance and robustness in cell-free ISAC environments. Conversely, the mirror-GAT framework significantly reduces computational complexity and signaling overhead through a bi-level iterative structure with shared adjacency. Simulation results validate that both proposed graph-learning-based frameworks achieve significant improvements in multi-target position and velocity estimation accuracy compared to conventional heuristic and optimization-based designs. Particularly, the mirror-GAT framework demonstrates substantial reductions in computational time and signaling overhead, underscoring its suitability for practical deployments.

Multiple-input multiple-output (MIMO) technology has emerged as a key technology for integrated sensing and communication (ISAC) systems, significantly improving spectral efficiency and sensing performance. This paper introduces cost-effective massive MIMO-ISAC architectures employing one-bit digital-to-analog and analog-to-digital converters (DACs/ADCs). While one-bit quantization facilitates the deployment of large-scale antenna arrays and enhances spatial resolution, it also introduces nonlinear distortion that necessitates sophisticated signal processing for effective compensation. To overcome these challenges, we propose novel MIMO-ISAC transceiver designs that integrate symbol-level precoding (SLP) to preserve communication quality, alongside radar waveform synthesis and space-time adaptive processing (STAP) techniques for robust clutter suppression. Specifically, for target detection, we formulate the maximization of the signal-to-quantization-plus-clutter-plus-noise ratio (SQCNR) as a joint optimization problem involving the transmit waveform and receive filtering, which is efficiently solved through the majorization-minimization (MM) and alternating direction method of multipliers (ADMM) algorithms. For target parameter estimation, we design transmit waveforms aimed at minimizing beam pattern sidelobes, thereby enhancing spatial selectivity. Additionally, we develop a MUSIC-based joint angle-Doppler estimation approach, exploiting Doppler-induced frequency shifts to effectively distinguish moving targets from stationary clutter without requiring prior clutter information. Extensive simulation results confirm the benefits of adopting one-bit DACs/ADCs in MIMO-ISAC systems. Notably, compared to conventional high-resolution systems using 8 antennas, our proposed one-bit architecture with 32 antennas achieves approximately 10 dB higher SQCNR and reduces angular estimation error by up to 85%. These results underscore the significant potential of one-bit quantization architectures as practical, high-performance solutions for future cost-effective MIMO-ISAC implementations.

Symbol-level precoding (SLP) is a promising solution for addressing the inherent interference problem in dual-functional radar-communication (DFRC) signal designs. This paper considers an SLP-DFRC signal design problem which optimizes the radar performance under communication performance constraints. We show that a common phase modulation applied to the transmit signals from an antenna array does not affect the performance of different radar sensing metrics, including beampattern similarity, signal-to-interference-plus-noise ratio (SINR), and Cramér-Rao lower bound (CRLB). We refer to this as symmetric-rotation invariance, upon which we develop low-complexity yet efficient DFRC signal design algorithms. More specifically, we propose a symmetric non-convexity (SNC)-based DFRC algorithm that relies on the non-convexity of the radar sensing metrics to identify a set of radar-only solutions. Based on these solutions, we further exploit the symmetry property of the radar sensing metrics to efficiently design the DFRC signal. We show that the proposed SNC-based algorithm is versatile in the sense that it can be applied to the DFRC signal optimization of all three sensing metrics mentioned above (beampattern, SINR, and CRLB). In addition, since the radar sensing metrics are independent of the communication channel and data symbols, the set of radar-only solutions can be constructed offline, thereby reducing the computational complexity. We also develop an accelerated SNC-based algorithm that further reduces the complexity. Finally, we numerically demonstrate the superiority of the proposed algorithms compared to existing methods in terms of sensing and communication performance as well as computational requirements.

Polarization diversity offers a cost- and space-efficient solution to enhance the performance of integrated sensing and communication systems. Polarimetric sensing exploits the signal’s polarity to extract details about the target such as shape, pose, and material composition. From a communication perspective, polarization diversity can enhance the reliability and throughput of communication channels. This paper proposes an integrated polarimetric sensing and communication (IPSAC) system that jointly conducts polarimetric sensing and communications. We study the use of single-port polarization-reconfigurable antennas to adapt to channel depolarization effects, without the need for separate RF chains for each polarization. We address two core sensing tasks in IPSAC systems, target parameter estimation and target detection. For parameter estimation, we consider the problem of minimizing the mean square error (MSE) of the target depolarization parameter estimate, which is a critical task for various polarimetric radar applications such as rainfall forecasting, vegetation identification, and target classification. To address this nonconvex problem, we apply semi-definite relaxation (SDR) and majorization-minimization (MM) optimization techniques. Next, we consider a design that maximizes the target signal-to-interference-plus-noise ratio (SINR) leveraging prior knowledge of the target and clutter depolarization statistics to enhance the target detection performance. To tackle this problem, we modify the solution developed for MSE minimization subject to the same quality-of-service (QoS) constraints. Extensive simulations show that the proposed polarization reconfiguration method substantially improves the depolarization parameter MSE. Furthermore, the proposed method considerably boosts the target SINR due to polarization diversity, particularly in cluttered environments.

Integrated sensing and communication (ISAC) has emerged as a key feature for sixth-generation (6G) networks, providing an opportunity to meet the dual demands of communication and sensing. Existing ISAC research primarily focuses on baseband optimization at individual access points, with limited attention to the roles of electromagnetic (EM) shaping and network-wide coordination. The intricate interdependencies between these domains remain insufficiently explored, leaving their full potential for enhancing ISAC performance largely untapped. To bridge this gap, we consider multi-domain ISAC optimization integrating EM shaping, baseband processing, and network cooperation strategies that facilitate efficient resource management and system-level design. We analyze the fundamental trade-offs between these domains and offer insights into domain-specific and cross-domain strategies contributing to ISAC performance and efficiency. We then conduct a case study demonstrating the effectiveness of joint multi-domain optimization. Finally, we discuss key challenges and future research directions to connect theoretical advancements and practical ISAC deployments. This work paves the way for intelligent and scalable ISAC architectures, providing critical insights for their seamless integration into next-generation wireless networks.

This paper focuses on precoding design in multi-antenna systems with improper Gaussian interference (IGI), characterized by correlated real and imaginary parts. We first study block level precoding (BLP) and symbol level precoding (SLP) assuming the receivers apply a pre-whitening filter to decorrelate and normalize the IGI. We then shift to the scenario where the base station (BS) incorporates the IGI statistics in the SLP design, which allows the receivers to employ a standard detection algorithm without pre-whitenting. Finally we address the case where the channel and statistics of the IGI are unknown, and we formulate robust BLP and SLP designs that minimize the worst case performance in such settings. Interestingly, we show that for BLP, the worst-case IGI is in fact proper, while for SLP the worst case occurs when the interference signal is maximally improper, with fully correlated real and imaginary parts. Numerical results reveal the superior performance of SLP in terms of symbol error rate (SER) and energy efficiency (EE), especially for the case where there is uncertainty in the non-circularity of the jammer.