The prior works on near-field target localization have mostly assumed ideal hardware model and thus suffer two limitations in practice. First, extremely large-scale arrays (XL-arrays) usually face a variety of hardware impairments (HIs) that may introduce unknown phase/amplitude errors. Second, the existing block coordinate descent (BCD) methods for the joint HIs indicator, angle, and range estimation may suffer considerable estimation errors when the target is very close to the XL-array. To address the above issues, we propose in this paper a new three-phase HI-aware near-field localization method, by jointly detecting faulty antennas and estimating the locations of targets. Specifically, we first determine the faulty antennas by using compressed sensing (CS) methods and improve the detection accuracy using estimated coarse targets’ locations. Then, an effective phase calibration method is proposed to correct phase errors induced by detected faulty antennas. Subsequently, an efficient near-field localization method is devised to accurately estimate the locations of targets based on fully XL-array with phase-calibration. Numerical results demonstrate that our proposed method significantly reduces the localization errors as compared to various benchmark schemes, especially when there is a high faulty-antenna probability.

Prior works on near-field beam training mostly assume dedicated polar-domain codebooks and on-grid range estimation, however, this may incur large training overhead and deteriorated estimation accuracy. In this paper, we propose a new and efficient beam training scheme with off-grid range esti-mation based on conventional discrete Fourier transform (DFT) codebook, which greatly reduces the beam training overhead. In particular, we first analyze the received beam pattern at the user when far-field beamforming vectors are used for beam scanning, and reveal an interesting result that this beam pattern contains useful user angle and range information. Then, an efficient scheme was proposed to jointly estimate the user angle and range using DFT codebook. This scheme estimates the user angle based on a defined angular support and resolves the user range by leveraging an approximated angular support width. Finally, numerical simulations show that our proposed scheme significantly reduces the near-field beam training overhead and improves the range estimation accuracy compared with various benchmark schemes.

Integrated sensing and communication (ISAC) is envisioned as a key pillar for enabling the upcoming sixth generation (6G) communication systems, requiring not only reliable communication functionalities but also highly accurate environmental sensing capabilities. In this paper, we design a novel networked ISAC framework to explore the collaboration among multiple users for environmental sensing. Specifically, multiple users can serve as powerful sensors, capturing back scattered signals from a target at various angles to facilitate reliable computational imaging. Centralized sensing approaches are extremely sensitive to the capability of the leader node because it requires the leader node to process the signals sent by all the users. To this end, we propose a two-step distributed cooperative sensing algorithm that allows low-dimensional intermediate estimate exchange among neighboring users, thus eliminating the reliance on the centralized leader node and improving the robustness of sensing. This way, multiple users can cooperatively sense a target by exploiting the block-wise environment sparsity and the interference cancellation technique. Furthermore, we analyze the mean square error of the proposed distributed algorithm as a networked sensing performance metric and propose a beamforming design for the proposed network ISAC scheme to maximize the networked sensing accuracy and communication performance subject to a transmit power constraint. Simulation results validate the effectiveness of the proposed algorithm compared with the state-of-the-art algorithms.

Prior works on near-field beam training have mostly assumed dedicated polar-domain codebook and on-grid range estimation, which, however, may suffer long training overhead, high codebook storage requirement, and degraded estimation accuracy. To address these issues, we propose in this paper new and efficient beam training schemes with off-grid range estimation by using conventional discrete Fourier transform (DFT) codebook. Specifically, we first analyze the received beam pattern at the user when far-field beamforming vectors are used for beam scanning, and show an interesting result that this beam pattern contains useful user angle and range information. Then, we propose two efficient schemes to jointly estimate the user angle and range with the DFT codebook. The first scheme estimates the user angle based on a defined angular support and resolves the user range by leveraging an approximated angular support width, while the second scheme estimates the user range by minimizing a power ratio mean square error (MSE) to improve the range estimation accuracy. Finally, numerical simulations show that our proposed schemes greatly reduce the near-field beam training overhead and improve the range estimation accuracy as compared to various benchmark schemes.