Imaging is a crucial sensing function that finds wide applications in environmental reconstruction, autonomous driving, etc. However, existing radio imaging techniques require at least Gigahertz-level bandwidth for high-resolution range estimation, and cannot be applied in 6G integrated sensing and communication (ISAC) network with Megahertz-level bandwidth. In this paper, we propose two novel high-resolution radio imaging techniques that can work on the limited-bandwidth 6G signals - bandwidth-independent synthetic aperture radar (BI-SAR), where the aerial base station (BS) moves along a circular trajectory to image the static targets; as well as bandwidth-independent inverse synthetic aperture radar (BI-ISAR), where the BS is static and the targets spin along an axis. Different from conventional SAR and ISAR counterparts that rely on range estimation, our proposed schemes can simply combine the Doppler spectrogram observed over one spin period to perform imaging. We also reveal that the signal model under our BI-SAR and BI-ISAR schemes is related to that under computer tomography (CT), but with an anisotropic scattering function, instead of an isotropic attenuation function as in CT imaging. We design the iterative adaptive approach-based Doppler association (IAA-DA) algorithm to tackle the above issue. Both numerical examples and real-world experiments are provided to show the feasibility and the effectiveness of our proposed BI imaging schemes.

This paper considers multi-view imaging in a sixth-generation (6G) integrated sensing and communication network, which consists of a transmit base-station (TBS), multiple receive base-stations (RBSs) connected to a central processing unit (CPU), and multiple extended targets. Our goal is to devise an effective multi-view imaging technique that can jointly leverage the echo signals at all the RBSs to precisely construct the image of these targets. To achieve this goal, we propose a two-phase framework. In Phase I, each RBS recovers an individual image of all the targets from its own view, which is obtained via utilizing its received signals’ sample covariance matrix to detect the grids with non-zero effective scattering intensity in the region of interest. Moreover, the shape of each grid is adjusted to conform to target geometries. In Phase II, the CPU fuses the individual images of all the RBSs to construct a higher-quality image of all the targets. To this end, we first design an edge-preserving natural neighbor interpolation (EP-NNI) method and then formulate an optimization problem to fuse the interpolated results. Extensive numerical results show that the proposed scheme significantly enhances imaging performance, facilitating high-quality environment reconstruction for future 6G networks.

Due to discrepancies in clock oscillators between transmitter and receiver, bistatic sensing in integrated sensing and communications (ISAC) systems suffers from the clock asynchronism issue. Previous research has demonstrated that these offsets can be effectively mitigated through cross-antenna techniques. However, such techniques may not always be preferred due to the side effects of restricted spatial degrees of freedom or complicated signal models. In this paper, we introduce cross-frequency techniques to address the clock asynchrony issue for line-of-sight (LOS) dominant bistatic sensing. We begin by uncovering the rotational invariance properly inherent in a time-frequency-domain signal matrix that is typically constructed for bistatic sensing. We then propose two novel methods, i.e., cross-frequency cross-correlation and cross-frequency signal ratio, to effectively suppress clock asynchronism without compromising spatial sensing ability. Furthermore, we evaluate the performance of these methods using key metrics, including the maximum unambiguous delay, delay resolution, and analytical target SNRs. Extensive simulation and experimental results are provided, verifying the effectiveness of the proposed methods and their superiority versus efficiency over prior art in sensing accuracy.

Uplink sensing in integrated sensing and communications (ISAC) systems, such as Perceptive Mobile Networks, is challenging due to the clock asynchronism between transmitter and receiver. Existing solutions typically require the presence of a line-of-sight path and the knowledge of the transmitter’s location. In this paper, these requirements, we propose a novel uplink sensing scheme to relieve these requirements by introducing static anchor points for the first time. The scheme consists of two efficient algorithms. The first algorithm estimates the relative timing and carrier frequency offsets, with respect to a randomly selected reference snapshot. Its estimation performance is analyzed with closed-form bias and root mean squared error derived. The estimates from the first algorithm are then used to eliminate clock offsets, enabling the construction of angle-Doppler maps. Using the maps, the second algorithm is developed to identify anchor points and then locate the transmitter and dynamic targets. The impact of the locations of the transmitter and anchor points is also analytically illustrated. Extensive simulation results are provided, demonstrating the effectiveness of the proposed sensing scheme in practical 5G communication setups and its superiority over prior art in terms of noise resilience and asymptotic performances.

This paper proposes a robust adaptive beamforming method based on the l1norm sparse constraint. The solution of this algorithm involves introducing the l1norm to find better beamforming weight vectors. To control the sidelobe level, the power constraint in the Direction of Arrival (DOA) of the Signal of Interest (SOI) is relaxed. We reformulate the adaptive beamforming problem as a Quadratic Constraint Quadratic Programming (QCQP) with an l1norm constraint, achieving better sparsity. The QCQP problem is solved using semi-definite relaxation. The beam pattern generated by the proposed algorithm exhibits low sidelobes and excellent robustness. Simulation results validate the effectiveness of the proposed adaptive beamforming algorithm.

Bi-static sensing is crucial for exploring the potential of networked sensing capabilities in integrated sensing and communications (ISAC). However, it suffers from the challenging clock asynchronism issue. Channel state information (CSI) ratio-based sensing is an effective means to address the issue. Its performance bounds, particular for Doppler sensing, have not been fully understood yet. This work endeavors to fill the research gap. Focusing on a single dynamic path in high-SNR scenarios, we derive the closed-form Cramér-Rao bounds (CRB). Then, through analyzing the mutual interference between dynamic and static paths, we simplify the CRB results by deriving close approximations, further unveiling new insights of the impact of numerous physical parameters on Doppler sensing. Moreover, utilizing the new CRB and analyses, we propose novel waveform optimization strategies for noise- and interference-limited sensing scenarios, which are also empowered by closed-form and efficient solutions. Extensive simulation results are provided to validate the preciseness of the derived CRB results and analyses, with the aid of the maximum-likelihood estimator. The results also demonstrate the substantial enhanced Doppler sensing accuracy and the sensing capabilities for low-speed target achieved by the proposed waveform design.

The frequency diverse array (FDA) multiple-input multiple-output (MIMO) radar combined with long-baseline transmit array (LBTA-FDA-MIMO) is presented for high accuracy direction-of-arrival (DOA) estimation, and the transmit array is designed in the form of a four-antenna interferometer in this letter. However, the ambiguities of both angle and range exist at the same time. To deal with this problem, a joint-disambiguation method based on ESPRIT is proposed and the uniqueness theorem is proved. The main idea of this method is that the unambiguous-rough-estimation for angle is used to solve the ambiguities of fine-estimation expressions coupled with angle and range. Compared with FDA-MIMO radar, the LBTA-FDA-MIMO radar significantly improves the angle estimation accuracy, and interestingly, it also improves the ranging accuracy via simulations.