The demand for accurate sensing in the complex environment, such as urban areas and indoor spaces, is critical for future wireless networks, where scattered signals become essential for sensing occluded targets under non-line-of-sight (NLOS) conditions. To reduce the demand for beam-sweeping and geometric assumptions, we propose a bistatic NLOS sensing technique that fully exploits the scattered signals. By modeling the scattering channel responses and leveraging sparsity-driven compressed sensing, our method achieves robust environmental reconstruction to estimate target positions, shapes, and orientations. The proposed algorithm is applicable for estimating the parameters of first-order and second-order scattering targets. Experimental results demonstrate its superiority in occluded target sensing and environmental mapping, thus offering an efficient solution for NLOS sensing in complex scenarios.

High-resolution mmWave radar imaging plays an important role in applications such as autonomous driving. Beam-based imaging methods often require scanning the scene of interest with a sufficiently small angular stepsize to achieve high-resolution, thus leading to a large computational and storage burden. Compressive sensing (CS) is a promising strategy to reconstruct high-dimensional yet sparse signals from low-dimensional measurements with random sampling. Therefore in this paper, we conduct random space sampling by adding random phase shifts on the transmit antennas of a frequency modulated continuous wave (FMCW)-based radar system. We prove that the sensing model can be formulated as a CS problem and solved by Expectation-Maximization Gaussian-Mixture Approximate Message Passing (EMGMAMP)-based approaches. Simulation results show that the model has excellent imaging performance even with very few sensing measurements. To further improve the imaging quality, we consider a multi-view sensing scenario in which sensing results from different positions are fused by proper occlusion processing and coordinate transformation. Finally, appropriate evaluation metrics are proposed for target sensing results to validate the effectiveness of the proposed sensing model and algorithm.

Initial random access is a crucial process in wireless communication networks, which sets up reliable connections between multiple active users and the base station (BS). In this procedure, useful information, including beam pair, timing advance (TA), and channel state information (CSI), can be naturally obtained to achieve environment sensing and user positioning. Environment sensing is highly related to user positioning as it requires user-specific CSI, and more importantly, the multi-view observations from different user locations potentially benefit the fusion of the overall environment information. This makes joint environment sensing and user positioning of great significance. Moreover, initial random access provides observations from different users, avoiding the limited and insufficient observation of a single pair of transceivers, which helps to realize environment sensing in large scenarios. Therefore, in this paper, we propose a joint initial random access, environment sensing, and user positioning framework, exploiting direction, time-delay, reflection, and scattering information brought by beam pair, TA, and CSI. Furthermore, we illustrate the remarkable sensing and positioning performance of the proposed scheme in both light-of-sight (LoS) and non-light-of-sight (NLoS) scenarios.

Future communication networks will integrate the sensing functions, requiring the system to utilize limited radio resources to simultaneously achieve high-throughput communication and high-precision sensing. Compressive sensing technology is of great potential for such applications. In this paper, we design an efficient multiple-input multiple-output (MIMO) - orthogonal frequency division multiplexing (OFDM) integrated sensing and communication (ISAC) system based on compressive sensing. Specifically, we obtain the delay-Doppler and angle of departure (AoD) / angle of arrival (AoA) measurements by exploiting the sparse and random time-frequency resource allocation patterns and array structures. Our approach leverages the sparsity of environmental information and employs the Kronecker method to construct a compressive measurement matrix with Vandermonde structure. Our method provides high-resolution performance comparable to Nyquist sampling, while significantly reducing the usage of time-frequency resource units and the number of antennas, without introducing excessive additional hardware links for sensing and computation. Numerical simulations demonstrate the feasibility of the method, and our results indicate that compressive sensing recovery algorithms outperform traditional method with a lower probability of recovery errors and better robustness.

The integration of sensing and communication (ISAC) is an essential function of future wireless systems. Due to its large available bandwidth, millimeter-wave (mmWave) ISAC systems are able to achieve high sensing accuracy. In this paper, we consider the multiple base-station (BS) collaborative sensing problem in a multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) mmWave communication system. Our aim is to sense a remote target shape with the collected signals which consist of both the reflection and scattering signals. We first characterize the mmWave's scattering and reflection effects based on the Lambertian scattering model. Then we apply the periodogram technique to obtain rough scattering point detection, and further incorporate the subspace method to achieve more precise scattering and reflection point detection. Based on these, a reconstruction algorithm based on Hough Transform and principal component analysis (PCA) is designed for a single convex polygon target scenario. To improve the accuracy and completeness of the reconstruction results, we propose a method to further fuse the scattering and reflection points. Extensive simulation results validate the effectiveness of the proposed algorithms.

Millimeter-wave (mmWave) imaging over wireless networks is one of the potential technologies in the design of integrated sensing and communication (ISAC) systems. To achieve complete and accurate sensing of the large-scale complex environment, multiple views from different user equipments (UEs) and base stations (BSs) in a wireless network should be fully and cooperatively exploited. In this paper, based on the uplink channels of the wireless cellular network, we propose a multi-view mmWave imaging architecture. In the proposed architecture, a single BS centrally or multiple BSs jointly process the transmitted data of UEs. Taking into account the complex physical propagation characteristics of mmWave in the environment, especially the occlusion effect, we exploit the multi-view sensing of the environment from various UEs and BSs. To solve the multi-view sensing problem for the considered model, we propose a generalized-approximate-message-passing-based multi-view sparse vector reconstruction (GAMP-MVSVR) algorithm to obtain the imaging results. In the proposed algorithm, a multi-layer factor graph is proposed to describe the data receiving and sending relationship, as well as the occlusion effect of mmWave propagation. The sum-product algorithm (SPA) is used to iteratively solve the imaging result. Specifically in each iteration, the occlusion relationship between the target object in the environment is recalculated according to the proposed occlusion detection rule, and in turn, used to estimate the scattering coefficients of the target objects. Simulation results verify the effectiveness of the proposed algorithm.

In this paper, we consider the problem of integrated sensing and communication (ISAC) system design over wireless networks. Specifically, the mobile station (MS) in the ISAC system sends uplink communication signal beams to the base station (BS), and the BS accomplishes the environment sensing by processing the propagation gain from received beams. Since the uniform discretization of the environment scenario has inaccurate descriptions of the BS/MS position and object occlusion relationship, we propose a non-uniform pixel discretization method. According to the location of the transceiver and the direction of beams, we discretize the environment into layered non-uniform pixels, which reflect the occlusion relationship between objects in the environment. Based on the sparse features of environmental scatterers, we propose an environment sensing algorithm based on compressed sensing and approximate message passing. The proposed algorithm achieves accurate environment sensing by iteratively removing occlusion interference. At the same time, with the continuous sweeping of the transmitting and receiving beams, the environment sensing results gradually improve. Finally, simulation results demonstrate the effectiveness of the proposed ISAC system design and algorithm.

Integrated sensing and communication (ISAC) systems aim to obtain the environment information using the wireless communication signals. However, most existing methods for ISAC systems require additional communication or hardware overheads, which pose a significant challenge for the resource-constrained wireless communication system. To address this issue, we propose a novel sidelobe-enhanced beam sweeping scheme, which leverages the extra information provided by the sidelobe compared to the traditional beam-based sensing algorithm. The proposed scheme takes into account the complete beam pattern including sidelobes, exploiting the different characteristics in each direction to simultaneously utilize multiple spatial angles. By effectively exploiting the sidelobes rather than treating them as interference, the proposed scheme can achieve comprehensive environmental information acquisition with high efficiency. Simulation results demonstrate that the proposed algorithm yields remarkable performance improvement.

Frequency modulated continuous wave (FMCW) radar can precisely detect moving objects utilizing the Doppler information. However, only exploiting the Doppler information in one frame can usually lead to object false detection when static background has large radar cross section or the moving objective occludes some static background. In this paper, we investigate the moving objective detection problem with FMCW radar through utilizing the Doppler information in multiple frames to increase objective detection accuracy. To solve this problem, an online tensor robust principal component analysis (RPCA) algorithm is proposed with low hardware and computation complexity. The proposed algorithm can maintain the intrinsic tensor data structure. Experimental results show that the proposed algorithm can accurately detect the static background and moving object even for the case of occlusion or static object with large RCS.

The widely applied orthogonal frequency division multiplexing (OFDM) system naturally contains oversampled cyclic prefixes (CP) in the generation process, which provide a wealth of environmental information and higher distance resolution for integrated sensing and communication (ISAC) but is underutilized. Therefore, we develop a compressed sensing (CS) model with oversampled CP, reaching the higher distance resolution limit corresponding to the sample rate of the analog-to-digital converter (ADC) than the fixed signal bandwidth. Since the measurement matrix formed by shifting adjacent oversampled CP pairs is ill-conditioned, we proposed random modulation and random extraction from multiple oversampled CP to increase the validity of the observations. To exploit the channel fading characteristics and sparsity of scattering points, we propose an element-by-element demodulator based on the orthogonal approximate message passing (OAMP) algorithm, called the element-wise OAMP (E-OAMP) algorithm. The simulation results validate the outstanding performance of the proposed algorithm over traditional CS algorithms.

This paper investigates an interesting wireless sensing problem which aims to focus on a specific target from the complicated background by exploiting the signals of a wireless communication system. In the considered context, multiple users send pilot signals to the base station (BS), which consistently collects and processes the received signals and gradually figures out the target within the focused area. This is by no means easy since all the background scatterers within the environment may produce severe interference to the received signals, which incurs possible divergence and large sensing error, especially when only limited wireless resource is available for sensing. To solve this issue, an iterative focusing algorithm is proposed, in which a rough sensing of the overall environment is performed to obtain an initial blurred imaging result. Then based on the initial environment sensing result, the proposed algorithm iteratively and gradually removes the background scatterers to obtain more and more accurate sensing results of the target object with limited system resource overhead. Simulation results verify the convergence and effectiveness of the proposed algorithm.