Channel Estimation for Extremely Large-Scale MIMO: Far-Field or Near-Field?

By Mingyao Cui, Linglong Dai. IEEE Transactions on Communications, 2021

937 citations recorded by the source corpus.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is promising to meet the high rate requirements for future 6G. To realize efficient precoding, accurate channel state information is essential. Existing channel estimation algorithms with low pilot overhead heavily rely on the channel sparsity in the angular domain, which is achieved by the classical far-field planar-wavefront assumption. However, due to the non-negligible near-field spherical-wavefront property in XL-MIMO, this channel sparsity in the angular domain is not achievable. Therefore, existing far-field channel estimation schemes will suffer from severe performance loss. To address this problem, in this paper, we study the near-field channel estimation by exploiting the polar-domain sparsity. Specifically, unlike the classical angular-domain representation that only considers the angular information, we propose a polar-domain representation, which simultaneously accounts for both the angular and distance information. In this way, the near-field channel also exhibits sparsity in the polar domain, based on which, we propose on-grid and off-grid near-field XL-MIMO channel estimation schemes. Firstly, an on-grid polar-domain simultaneous orthogonal matching pursuit (P-SOMP) algorithm is proposed to efficiently estimate the near-field channel. Furthermore, an off-grid polar-domain simultaneous iterative gridless weighted (P-SIGW) algorithm is proposed to improve the estimation accuracy. Finally, simulations are provided to verify the effectiveness of our schemes.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e008380a3941c1e8b9f98270cd577ec84fca3387

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Citations in the corpus

An Overview of Signal Processing Techniques for RIS/IRS-Aided Wireless Systems (2021, neutral): “Specifically, the angle and distance are sampled in the polar domain using a uniform grid in [232].”
6G Wireless Communications: From Far-Field Beam Steering to Near-Field Beam Focusing (2022, background): “One elegant solution is the polar-domain near-field channel modeling scheme [7], which has strictly proved the non-uniform sampling criterion in the distance dimension for the first time.”
A Tutorial on Near-Field XL-MIMO Communications Toward 6G (2023, neutral): “Exhaustive search: Given the non-uniform polar-domain codebook [11], the most straightforward near-ﬁeld beam training method is performing a 2D exhaustive search over all possible beam codewords in the angular-distance domains, as illustrated in Fig.”
A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications (2023, methodology): “Among many existing works for XL-MIMO, two major LoS propagation channel modeling schemes are studied including Green’s function based channel modeling and the complex-valued channel response representation channel modeling.”
The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications (2024, neutral): “For , Cui and Dai [248] proposed a polar-domain representation of the channel that comprehensively captures near-field spherical wave characteristics.”
5G-Advanced Toward 6G: Past, Present, and Future (2023, background): “[56], [57], [58], more comprehensive studies and measurement campaigns are in need to thoroughly explore the associated channel models and beam/wavefront management strategies.”
Near-Field MIMO Communications for 6G: Fundamentals, Challenges, Potentials, and Future Directions (2023, methodology): “With the help of this non-uniform codebook, a polar-domain sparse channel representation and corresponding CS-based algorithms are proposed in [5] to accomplish accurate channel estimation in both near- and far-field areas.”
Near-Field Communications: A Tutorial Review (2023, background): “• Narrowband systems: Some recent efforts have been devoted to the design of narrowband near-field beam training [85]–[88].”