mlsanityData to 7 Oct 2026

A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications

arXiv 2307.07340 ↗First posted 14 Jul 2023cs.IT eess.SP math.ITcited by 23

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Abstract (arXiv)

Extremely large-scale multiple-input-multiple-output (XL-MIMO), which offers vast spatial degrees of freedom, has emerged as a potentially pivotal enabling technology for the sixth generation (6G) of wireless mobile networks. With its growing significance, both opportunities and challenges are concurrently manifesting. This paper presents a comprehensive survey of research on XL-MIMO wireless systems. In particular, we introduce four XL-MIMO hardware architectures: uniform linear array (ULA)-based XL-MIMO, uniform planar array (UPA)-based XL-MIMO utilizing either patch antennas or point antennas, and continuous aperture (CAP)-based XL-MIMO. We comprehensively analyze and discuss their characteristics and interrelationships. Following this, we introduce several electromagnetic characteristics and general distance boundaries in XL-MIMO. Given the distinct electromagnetic properties of near-field communications, we present a range of channel models to demonstrate the benefits of XL-MIMO. We further discuss and summarize signal processing schemes for XL-MIMO. It is worth noting that the low-complexity signal processing schemes and deep learning empowered signal processing schemes are reviewed and highlighted to promote the practical implementation of XL-MIMO. Furthermore, we explore the interplay between XL-MIMO and other emergent 6G technologies. Finally, we outline several compelling research directions for future XL-MIMO wireless communication systems.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jul 2023.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 9; 2026: 8.

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  1. 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimizationcited by 56
  2. 2024Generative Diffusion Models for High Dimensional Channel Estimationcited by 26
  3. 2025RadioDiff-k2: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Constructioncited by 19
  4. 2024DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approachcited by 17
  5. 2025RadioDiff-3D: A 3D×3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communicationcited by 16
  6. 2025Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Informationcited by 4
  7. 2025Environment-Aware Channel Inference via Cross-Modal Flow: From Multimodal Sensing to Wireless Channelscited by 3
  8. 2024Large Language Model Empowered Next-Generation MIMO Networks: Fundamentals, Challenges, and Visionscited by 2
  9. 2026RadioDiff-FS: Physics-Informed Manifold Alignment in Few-Shot Diffusion Models for High-Fidelity Radio Map Constructioncited by 2
  10. 2024Maximum-Likelihood Estimation Based on Diffusion Model For Wireless Communicationscited by 1
  11. 2025Near Field Localization via AI-Aided Subspace Methodscited by 1
  12. 2024Rapid and Power-Aware Learned Optimization for Modular Receive Beamformingcited by 0
11 more
  1. 2025UrbanMIMOMap: A Ray-Traced MIMO CSI Dataset with Precoding-Aware Maps and Benchmarkscited by 0
  2. 2025Cross-field SNR Analysis and Tensor Channel Estimation for Multi-UAV Near-field Communicationscited by 0
  3. 2025Conditional Diffusion Model-Enabled Scenario-Specific Neural Receivers for Superimposed Pilot Schemescited by 0
  4. 2025LLM4XCE: Large Language Models for Extremely Large-Scale Massive MIMO Channel Estimationcited by 0
  5. 2026Structure-Informed Estimation for Pilot-Limited MIMO Channels via Tensor Decompositioncited by 0
  6. 2026Structure-Aware Multimodal LLM Framework for Trustworthy Near-Field Beam Predictioncited by 0
  7. 2026A Wireless World Model for AI-Native 6G Networkscited by 0
  8. 2026Mixture-of-Experts Diffusion Models for Adaptive Massive MIMO Channel Estimation via Variational Bayesian Inferencecited by 0
  9. 2026A Unified Two-Stage Generative Diffusion Framework for Channel Estimation and Port Selection in Multiuser MIMO-FAScited by 0
  10. 2026Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completioncited by 0
  11. 2026BeamRMX: Radiation-Pattern-Driven Learning for Generalizable Beam Radio Map Prediction and Beam Managementcited by 0