With the deployment of large antenna arrays at high-frequency bands, future wireless communication systems are likely to operate in the radiative near-field. Unlike far-field beam steering, near-field beams can be focused on a spatial region with a finite depth, enabling spatial multiplexing in the range dimension. Moreover, in the line-of-sight MIMO near-field, multiple spatial degrees of freedom (DoF) are accessible, akin to a scattering-rich environment. In this paper, we derive the beamdepth for a generalized uniform rectangular array (URA) and investigate how the array geometry influences near-field beamdepth and its limits. We define the effective beamfocusing Rayleigh distance (EBRD), to present a near-field boundary with respect to beamfocusing and spatial multiplexing gains for the generalized URA. Our results demonstrate that under a fixed element count constraint, the array geometry has a strong impact on beamdepth, whereas this effect diminishes under a fixed aperture length constraint. Moreover, compared to uniform square arrays, elongated configurations such as uniform linear arrays (ULAs) yield narrower beamdepth and extend the effective near-field region defined by the EBRD. Building on these insights, we design a polar codebook for compressed-sensing-based channel estimation that leverages our findings. Simulation results show that the proposed polar codebook achieves a 2 dB NMSE improvement over state-of-the-art methods. Additionally, we present an analytical expression to quantify the effective spatial DoF in the near-field, revealing that they are also constrained by the EBRD. Notably, the maximum spatial DoF is achieved with a ULA configuration, outperforming a square URA in this regard.

Ultra-massive multiple-input multiple-output (UM-MIMO) technology is a key enabler for 6G networks, offering exceptional high data rates in millimeter-wave (mmWave) and Terahertz (THz) frequency bands. The deployment of large antenna arrays at high frequencies transitions wireless communication into the radiative near-field, where precise beam alignment becomes essential for accurate channel estimation. Unlike far-field systems, which rely on angular domain only, near-field necessitates beam search across both angle and distance dimensions, leading to substantially higher training overhead. To address this challenge, we propose a discrete Fourier transform (DFT) based beam alignment to mitigate the training overhead. We highlight that the reduced path loss at shorter distances can compensate for the beamforming losses typically associated with using far-field codebooks in near-field scenarios. Additionally, far-field beamforming in the near-field exhibits angular spread, with its width determined by the user's range and angle. Leveraging this relationship, we develop a correlation interferometry (CI) algorithm, termed CI-DFT, to efficiently estimate user angle and range parameters. Simulation results demonstrate that the proposed scheme achieves performance close to exhaustive search in terms of achievable rate while significantly reducing the training overhead by 87.5%.

With the deployment of large antenna arrays at high-frequency bands, future wireless communication systems are likely to operate in the radiative near-field. Unlike far-field beam steering, near-field beams can be focused within a spatial region of finite depth, enabling spatial multiplexing in both the angular and range dimensions. This paper derives the beamdepth for a generalized uniform rectangular array (URA) and investigates how array geometry influences the near-field beamdepth and the limits where near-field beamfocusing is achievable. To characterize the near-field boundary in terms of beamfocusing and spatial multiplexing gains, we define the effective beamfocusing Rayleigh distance (EBRD) for a generalized URA. Our analysis reveals that while a square URA achieves the narrowest beamdepth, the EBRD is maximized for a wide or tall URA. However, despite its narrow beamdepth, a square URA may experience a reduction in multiuser sum rate due to its severely constrained EBRD. Simulation results confirm that a wide or tall URA achieves a sum rate of 3.5× more than that of a square URA, benefiting from the extended EBRD and improved spatial multiplexing capabilities.

Ultra massive multiple-input-multiple-output (UM-MIMO) technology has emerged as a promising candidate for 6G networks, offering ultra-high spectral efficiency in wireless systems. The transition to large antenna arrays specially at high-frequency bands is fundamentally transforming wireless communication from the traditional far-field to the near-field realm. This transition poses a distinct challenge in channel estimation due to the associated pilot overhead from large antenna arrays and the absence of angular sparsity in near-field spherical wavefronts. However, polar-domain sparsity remains achievable, advocating the use of polar codebooks over traditional angle-based ones. Nevertheless, the size of the polar codebook presents a significant challenge, necessitating sampling of distance and angle points across the entire near-field. In this work, we investigate the near-field channel estimation techniques while identifying the boundaries of polar domain sparsity with minimal pilot overhead. We propose a novel polar codebook, which leverages our findings from sparsity analysis and exploits the beam-focusing properties of the near-field. Unlike existing work, the proposed polar codebook design is agnostic to user range information and has considerably reduced dimensions. Capitalizing on this new polar codebook, we introduce the beam focused simultaneous orthogonal matching pursuit (BF-SOMP) algorithm for efficient near-field channel estimation. To further improve the channel estimation accuracy, we then present a refinement procedure that iterates over off-grid angle and range samples to enhance the estimation accuracy. Simulation results demonstrate that the proposed polar codebook based algorithms outperform contemporary methods in terms of improved normalized mean square error (NMSE) and reduced computational complexity. When compared to existing channel estimation methods, the proposed algorithms achieve an NMSE improvement of 6 − 7 dB at low and high SNR values with 32 pilots, while utilizing a codebook nearly half the size of existing ones.

Ultra massive multiple-input-multiple-output (UM-MIMO) has emerged as a prospective capacity-enhancing technology for next generation (NG) networks. To harness its full potential, accurate channel estimation with low pilot overhead becomes paramount. The combination of large antenna arrays and high frequency bands transitions the wireless communication from the far-field to the near-field realm. This transition presents a unique challenge, as channel sparsity in the angular domain becomes unattainable in the presence of near-field spherical wavefronts. Nonetheless, polar-domain sparsity is achievable, allowing existing near-field channel estimation methods to use polar codebooks over the classical angle-based codebooks. However, a major challenge in utilizing polar domain sparsity is the size of the polar codebook, demanding sampling of both distance and angle points across the entire near-field. In this work, we investigate near-field channel response in the beam space domain to redefine the boundaries of polar domain sparsity. We propose a novel polar codebook leveraging our results of sparsity analysis and beamfocusing property of near-field. Unlike existing work, the proposed polar codebook design is agnostic to user range information and has considerably reduced dimensions. Exploiting the new polar codebook, we present the beam focused simultaneous orthogonal matching pursuit (BF-SOMP) algorithm for efficient near-field channel estimation. Simulation results demonstrate that the proposed algorithm surpasses contemporary methods in terms of normalized mean square error (NMSE) and reduced computational complexity.