Near-field ultra-massive MIMO (U-MIMO) systems provide enhanced spatial resolution but present challenges for channel estimation, particularly when hybrid architectures are employed. Within this framework, dictionary-based channel estimation schemes are needed to achieve accurate reconstruction from a reduced set of measurements. However, existing near-field dictionaries generally provide full three-dimensional coverage, which is unnecessary when user equipments are primarily located on the ground. In this paper, we propose a novel near-field grid design tailored to this common scenario. Specifically, grid points lie on a reference plane located at an arbitrary height with respect to the U-MIMO system, equipped with a uniform planar array. Furthermore, a channel accuracy metric is used to improve codebook performance, and to remark the limitations of the traditional far-field angular sampling in the near field. Results show that, as long as user equipments are not far from the reference plane, the proposed grid outperforms state-of-the-art designs in both channel estimation accuracy and spectral efficiency.
Ultra-massive MIMO (U-MIMO) is envisioned as a key technology to meet the high traffic demands of future wireless communication systems [2]- [4]. By exploiting the short wavelengths at millimeter-wave (mmWave) or TeraHertz (THz) frequencies, U-MIMO systems densely pack many antennas into a compact area, enabling substantial gains in beam focusing and spatial multiplexing [5]. However, the conventional fully-digital architecture, where each antenna is connected to a dedicated radio frequency (RF) chain, becomes prohibitively power-intensive for U-MIMO systems [6]. Consequently, hybrid architectures, which limit the number of RF chains [7], are typically adopted instead.
Unlike sub-6 GHz communications, where user equipments (UEs) are typically located in the far field, the very large number of antennas operating at short wavelengths in U-MIMO systems, generally locates UEs in the radiating near field [8]. This shift requires the development of near-field dictionaries, where, differently from far-field scenarios, the range domain is crucial due to the limited beamforming depth [9]- [11].
A. Literature review Since near-field codebooks are built sampling both the angular and range domains, they are typically large. However, several compressed sensing (CS) algorithms whose overheads are independent of the codebook’s size, have been used for channel estimation with low pilot overhead. For instance, [12] exploits the angular-domain sparsity to estimate the channel using the classical orthogonal matching pursuit (OMP) algorithm. Moreover, [13] proposes a variant of the OMP, referred to as simultaneous-OMP (SOMP), which handles the colored noise introduced by hybrid architectures.
Notably, all above solutions exploit the channel sparsity in the angular domain, which is available in the far field as the wavefront is approximately planar. However, in the near field, as the curvature of the wavefront is not negligible, the energy spreads into multiple angles [14]. Consequently, the channel is significantly less sparse in the angular domain, which causes the traditional approaches based on the classical far-field dictionary to suffer a severe performance degradation. Accordingly, novel dictionary designs are needed to make the channel sparse in the near field. Recently, a polar-domain near-field design has been proposed in [14] for a uniform linear array (ULA). Here, the design is based on the dictionary column coherence, which is more challenging to control in the near field.
Since uniform planar arrays (UPAs) allow to pack more antennas within a limited area, the polar-domain dictionary in [14] has been extended to the near field of a uniform rectangular array in [15]. Furthermore, [16] generalizes this design to arbitrary antenna spacings and enables improved control of the column coherence. Additionally, a concentric-ring dictionary has been proposed in [17] for a uniform circular array, whose symmetry is leveraged to achieve uniform and extended near-field regions across all angular directions, thereby enabling more UEs to benefit from near-field beamforming. The boundary for effective near-field beamforming is investigated in [18], leading to the development of a novel polar codebook, tailored to the concept of effective-Rayleigh distance (ERD). Moreover, the combined use of the far-field and polar dictionaries has been recently proposed. In [19], a discrete Fourier transform (DFT)-based codebook is first employed to estimate the UE direction, and a polar codebook is subsequently used to estimate the corresponding distance. Following a similar approach, [20] splits the array into two subarrays: one is associated with a far-field dictionary for angular beam training, the other with a polar dictionary for range estimation.
State-of-the-art dictionaries for UPAs, such as [15]- [20], typically provide full spatial coverage, meaning that they are designed for arbitrary UE locations in the threedimensional space. However, this is unnecessary when UEs are distributed close to a planar surface, which commonly occurs in practice.
Our main contributions can be summarized as follows.
• First, we show that when the number of observations exceeds the number of antennas, a dictionary-based approach is unnecessary, as the LS estimator is generally accurate -especially in the near field, where the SNR is higher. However, with few observations, LS becomes unreliable, while sparse recovery methods such as P-SOMP can still yield accurate channel estimates by exploiting sparsity in a suitable dictionary domain. Since near-field channels are not sparse in the traditional far-field codebook domain, near-field dictionaries are required.
• Since state-of-the-art near-field dictionaries for UPA typically provide full three-dimensional coverage, we propose a novel near-field grid, specifically tailored to UEs distributed on a reference plane (RP), rather than throughout the entire three-dimensional space.
To further assess its pr
This content is AI-processed based on open access ArXiv data.