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Deep Learning for Near-Field XL-MIMO Transceiver Design: Principles and Techniques

Wentao Yu, Yifan Ma, Hengtao He, Shenghui Song, Jun Zhang, Khaled B. Letaief

TL;DR

This paper tackles the challenge of designing scalable, robust transceivers for near-field XL-MIMO in 6G by introducing two DL-based frameworks: Neural Calibration (NC) for non-iterative algorithms and Fixed Point Networks (FPNs) for iterative algorithms. NC uses a permutation-equivariant neural architecture to calibrate inputs before a differentiable basis solver, delivering scalable performance that generalizes across varying numbers of users and antennas. FPNs reformulate iterative algorithms as contraction mappings, enabling fixed-point convergence with $O(1)$ training complexity and improved generalization via the Banach fixed point theorem. Case studies on beam focusing and channel estimation demonstrate substantial performance and speed gains over traditional methods and existing DL approaches, signaling practical potential for real-time near-field XL-MIMO transceivers. The paper also outlines open directions, including unsupervised learning, architecture co-design, stronger generalization, and problem-specific theoretical guarantees to advance robust DL-based transceiver design.

Abstract

Massive multiple-input multiple-output (MIMO) has been a critical enabling technology in 5th generation (5G) wireless networks. With the advent of 6G, a natural evolution is to employ even more antennas, potentially an order of magnitude more, to meet the ever-increasing demand for spectral efficiency. This is beyond a mere quantitative scale-up. The enlarged array aperture brings a paradigm shift towards near-field communications, departing from traditional far-field approaches. However, designing advanced transceiver algorithms for near-field systems is extremely challenging because of the enormous system scale, the complicated channel characteristics, and the uncertainties in the propagation environments. Hence, it is important to develop scalable, low-complexity, and robust algorithms that can efficiently characterize and leverage the properties of the near-field channel. In this article, we discuss the principles and advocate two general frameworks to design deep learning-based near-field transceivers covering both iterative and non-iterative algorithms. Case studies on channel estimation and beam focusing are presented to provide a hands-on tutorial. Finally, we discuss open issues and shed light on future directions.

Deep Learning for Near-Field XL-MIMO Transceiver Design: Principles and Techniques

TL;DR

This paper tackles the challenge of designing scalable, robust transceivers for near-field XL-MIMO in 6G by introducing two DL-based frameworks: Neural Calibration (NC) for non-iterative algorithms and Fixed Point Networks (FPNs) for iterative algorithms. NC uses a permutation-equivariant neural architecture to calibrate inputs before a differentiable basis solver, delivering scalable performance that generalizes across varying numbers of users and antennas. FPNs reformulate iterative algorithms as contraction mappings, enabling fixed-point convergence with training complexity and improved generalization via the Banach fixed point theorem. Case studies on beam focusing and channel estimation demonstrate substantial performance and speed gains over traditional methods and existing DL approaches, signaling practical potential for real-time near-field XL-MIMO transceivers. The paper also outlines open directions, including unsupervised learning, architecture co-design, stronger generalization, and problem-specific theoretical guarantees to advance robust DL-based transceiver design.

Abstract

Massive multiple-input multiple-output (MIMO) has been a critical enabling technology in 5th generation (5G) wireless networks. With the advent of 6G, a natural evolution is to employ even more antennas, potentially an order of magnitude more, to meet the ever-increasing demand for spectral efficiency. This is beyond a mere quantitative scale-up. The enlarged array aperture brings a paradigm shift towards near-field communications, departing from traditional far-field approaches. However, designing advanced transceiver algorithms for near-field systems is extremely challenging because of the enormous system scale, the complicated channel characteristics, and the uncertainties in the propagation environments. Hence, it is important to develop scalable, low-complexity, and robust algorithms that can efficiently characterize and leverage the properties of the near-field channel. In this article, we discuss the principles and advocate two general frameworks to design deep learning-based near-field transceivers covering both iterative and non-iterative algorithms. Case studies on channel estimation and beam focusing are presented to provide a hands-on tutorial. Finally, we discuss open issues and shed light on future directions.
Paper Structure (26 sections, 3 figures, 2 tables)

This paper contains 26 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Research Challenges and the Roles of DL
  3. Research Challenges
  4. Complicated Channel Characteristics
  5. Enormous System Scale
  6. Uncertainties in Propagation Environments
  7. The Roles and Requirements of DL
  8. Scalability and Complexity
  9. Generalization and Adaptation
  10. Theoretical Guarantees
  11. Algorithmic Frameworks and Enabling Techniques
  12. Algorithmic Frameworks for Non-Iterative Algorithms
  13. Existing Black-Box Neural Networks (NNs)
  14. Proposed Neural Calibration (NC)
  15. Algorithmic Frameworks for Iterative Algorithms
  16. ...and 11 more sections

Figures (3)

  • Figure 1: A typical XL-MIMO system involving near-field propagation.
  • Figure 2: (a) Sum rate versus the number of near-field users when the number of antennas is 1024. (b) Sum rate versus the number of antennas when the number of served users is 100. Settings: 14 GHz carrier frequency, 1 GHz system bandwidth, a fully-digital uniform planar array, -60 dBm noise power, 10 dB transmit power. A standard near-field channel model is adopted 2023Cui.
  • Figure 3: NMSE versus the CPU runtime. Settings: 300 GHz carrier frequency, a 1024-antenna non-uniform array-of-subarray, 5 dB SNR, a one-bit hybrid analog-digital combiner with 50% compression ratio. ISTA-Net+ is the SoA DUN method for channel estimation. The hybrid-field channel model in 2023Yu-JSTSP is adopted in the simulations.