Near-Field Channel Estimation for Extremely Large-Scale Terahertz Communications

Future Terahertz communications exhibit significant potential inaccommodating ultra-high-rate services. Employing extremely large-scale arrayantennas is a key approach to realize this potential, as they can harnesssubstantial beamforming gains to overcome the severe path loss and leverage theelectromagnetic advantages in the near field. This paper proposes novelestimation methods designed to enhance efficiency in Terahertz widely-spacedmulti-subarray (WSMS) systems. Initially, we introduce three sparse channelrepresentation methods: polar-domain representation (PD-R),multi-angular-domain representation (MAD-R), and two-dimensionalpolar-angular-domain representation (2D-PAD-R). Each method is meticulouslydeveloped for near-field WSMS channels, capitalizing on their sparsitycharacteristics. Building on this, we propose four estimation frameworks usingthe sparse recovery theory: polar-domain estimation (PD-E),multi-angular-domain estimation (MAD-E), two-stage polar-angular-domainestimation (TS-PAD-E), and two-dimensional polar-angular-domain estimation(2D-PAD-E). Particularly, 2D-PAD-E, integrating a 2D dictionary process, andTS-PAD-E, with its sequential approach to angle and distance estimation, standout as particularly effective for near-field angle-distance estimation,enabling decoupled calculation of these parameters. Overall, these frameworksprovide versatile and efficient solutions for WSMS channel estimation,balancing low complexity with high-performance outcomes. Additionally, theyrepresent a fresh perspective on near-field signal processing.