Since the beam squint and near-field effects both inherently exist in upper-6 GHz (U6G) extremely large-scale multiple-input multiple-output (XL-MIMO) systems, wideband near-field channel estimation faces severe challenges, such as higher computational complexity, and higher pilot overhead particularly at hybrid architectures with fewer radio frequency (RF) chains. To precisely reduce the complexity and number of pilots, theparametric symmetry of wideband near-field channelsis explored, such that the channel parameters, including angle, distance, and range, can be decoupled based on the delay variations observed by different antennas. Based on this, adistributed parametric symmetry-based (DPS) algorithm, applicable to U6G XL-MIMO, is proposed. The delays observed by different subarrays are estimated and extrapolated across the local processing units (LPUs) firstly, and then, the channel parameters are decoupled and estimated at the central processing unit (CPU), by only linearly combining the delays from different LPUs. The path gains are calculated at different LPUs, respectively, to reconstruct the channel with low complexity. Since the proposed algorithm does not rely on scanning the polar-domain dictionary, onlya single pilotis required even with hybrid architectures. Furthermore, the computational complexity, multiple-path resolution, Cramér–Rao lower bound (CRLB) and lower bound (LB) of the estimates in hybrid architectures and the DPS algorithm, respectively, are analyzed, to evaluate the realizable potential of the proposed algorithm. The simulation results prove that the proposed algorithm has a higher estimation accuracy, while requiring less complexity and pilots.

In this paper, a wideband extremely large-scale multiple-input multiple-output (XL-MIMO) system is considered, and an efficient wideband near-field channel estimation algorithm is proposed. Due to the non-negligible near-field effect and beam squint effect in wideband XL-MIMO systems, the spatial domain sparsity of near-field channel matrix is broken, and thus traditional estimation algorithms become inapplicable. Meanwhile, the large number of antennas and wide bandwidth will lead to greatly high computational complexity. To decrease the computational complexity, the parametric symmetry of wideband XL-MIMO array is studied, and the extra freedom of beam squint effect in near-field region is utilized. Then, the parametric symmetry-based multiple-antenna joint (PSMJ) channel estimation algorithm is proposed to estimate the wideband near-field channel with low computational complexity. Simulation results proves that the proposed PSMJ algorithm has superior performance with lower complexity for wideband XL-MIMO systems.

To reduce the computational complexity of near-field channel estimation for extremely large-scale multiple-input multiple-output (XL-MIMO) systems, the concept ofvirtual cell subarraysin subarray hybrid precoding architectures, is firstly given. Multiple subarrays with strong correlation can be flexibly and dynamically combined, to jointly extract relevant features of the channels. Based on this, an undersampling matching and oversampling refinement pursuit (UMORP) algorithm is proposed, which can detect the channel parameters of cell subarrays through an undersampling dictionary constructed by analog phase shifters. This approach facilitates the estimation of the channel of a single cell subarray with much fewer pilots and lower hardware capability requirement. Then, a multiple-path decoupled spatial extrapolation (MPDSE) scheme is proposed for fully dimensional channel reconstruction, which orthogonally decouples multiple paths from the received signal of a single cell subarray firstly, and then utilize the spatial correlation between adjacent cell subarrays to extrapolate the channels with low cost. Moreover, to further reduce the computational complexity in XL-MIMO systems, a spatial multiple cell subarrays joint extrapolation (SMCJE) scheme is also proposed. Based on the spatial geometry among cell subarrays, only several cell suabrrays are utilized to jointly estimate the distances and reconstruct the fully dimensional near-field channel, achieving a low level of computational complexity. Our simulation results verify that the proposed schemes perform better in XL-MIMO systems, while requiring fewer pilots and exhibiting much lower computational complexity.

In the near-field region of an extremely large-scale multiple-input multiple-output (XL MIMO) system, channel reconstruction is typically addressed through sparse parameter estimation based on compressed sensing (CS) algorithms after converting the received pilot signals into the transformed domain. However, the exhaustive search on the codebook in CS algorithms consumes significant computational resources and running time, particularly when a large number of antennas are equipped at the base station (BS). To overcome this challenge, we propose a novel scheme to replace the high-cost exhaustive search procedure. We visualize the sparse channel matrix in the transformed domain as a channel image and design the channel keypoint detection network (CKNet) to locate the user and scatterers in high speed. Subsequently, we use a small-scale newtonized orthogonal matching pursuit (NOMP) based refiner to further enhance the precision. Our method is applicable to both the Cartesian domain and the Polar domain. Additionally, to deal with scenarios with a flexible number of propagation paths, we further design FlexibleCKNet to predict both locations and confidence scores. Our experimental results validate that the CKNet and FlexibleCKNet-empowered channel reconstruction scheme can significantly reduce the computational complexity while maintaining high accuracy in both user and scatterer localization and channel reconstruction tasks.

In this paper, an efficient near-field channel estimation algorithm, and a novel cascade channel reconstruction scheme, with significantly reduced pilot overhead and computational complexity, are proposed for hybrid extra large-scale reconfigurable intelligent surface (XL-RIS)-aided multi-input multi-output (MIMO) systems. A unique hybrid XL-RIS architecture is devised, in which the elements at the designed central subarray, and many specially selected discrete elements, are active, while others are passive. Meanwhile, a damped Newtonized orthogonal matching pursuit algorithm combining the planar and spherical wave models (DNOMP-CPSW) is proposed, in which the angle and distance parameters of multipaths are estimated through the received signals of the central subarray and the discrete active elements respectively, and the near-field channel can be reconstructed accurately with low pilot overhead and computational complexity. Moreover, to decrease the cost of cascade channel reconstruction in the considered system, a separate channel estimation scheme based on the decoupling operation (SCEDO) is proposed, which estimates the two separate channels with only 3 pilots and reduced computational complexity, and then reconstruct the cascade channel. Furthermore, the phase shift strategy of the XL-RIS with 2-bits quantization is devised, which can increase the energy of the received signal and maintain the set order to estimate multipaths in different stages of the SCEDO scheme, to improve the accuracy of the estimates, and enhance the reliability of the separate channel estimation. Simulation results verify that the proposed DNOMP-CPSW algorithm and the hybrid XL-RIS phase shift strategy can enhance the performance of the considered system. Compared with other schemes, the SCEDO scheme can reconstruct the cascade channel efficiently, with much reduced pilot overhead and computational complexity.

In this paper, an efficient near-field channel reconstruction and user equipment (UE) localization scheme is proposed for extremely large antenna array (ELAA) systems using a subarray hybrid precoding architecture. Considering the non-negligible signal amplitude and phase variations across the different receive antennas, a more realistic channel model is adopted. The channel environment, with an approximate smooth ground surface, is modeled. In fact, the channel can be divided into a line-of-sight (LoS) path, a reflection path and some non-LoS (NLoS) paths. Based on the sparsity of the channel in the spatial domain, the damped Newtonized orthogonal matching pursuit (DNOMP) algorithm is also proposed to accurately estimate the multipaths, and reconstruct the channel. Then, a UE localization algorithm is proposed, which can detect the existence of the LoS path and locate the UE. A joint localization algorithm is also devised to further increase the positioning reliability. Simulation results verify that the DNOMP algorithm can reconstruct the channel with better NMSE performance than other schemes. The localization algorithm can locate the UE with low error whenever the LoS path exists or not, with an accuracy close enough to the position error bound (PEB), while the joint localization algorithm can further enhance the positioning reliability.