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

TL;DR

The paper addresses enabling 6G performance with XL-MIMO by systematically examining four hardware designs (ULA-based, UPA-based with patch or point antennas, and CAP-based), their EM characteristics, and near-field channel models. It surveys LoS, NLoS, and hybrid channel modeling approaches, including Green's function and complex-valued representations, together with distance boundaries that define near-field vs. far-field regimes. It then reviews low-complexity and ML-enabled signal processing, including channel estimation and beamforming, and discusses ML and distributed learning for scalable XL-MIMO operations. The survey also highlights diverse XL-MIMO applications—PLS, UAV communications, ISAC, IoT, edge computing, and massive connectivity—and outlines future directions such as AI-aided resource allocation, energy efficiency, semantic communications, testbeds, and security. Overall, the work provides a comprehensive, EM-grounded roadmap for implementing XL-MIMO in practical 6G networks, balancing theoretical modeling with pragmatic processing and deployment considerations.

Abstract

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.

Figures (10)

• Figure 1: The organization structure of the survey.
• Figure 2: Hardware design characteristics for the general XL-MIMO schemes and their relationships among each other.
• Figure 3: The EM wave characteristics for the near-field and far-field regions, which are bounded by the Rayleigh distance. The spherical wave and planar wave characteristics should be considered in the near-field and far-field regions, respectively.
• Figure 4: The diagram for the spatial stationary mMIMO and spatial non-stationary XL-MIMO.
• Figure 5: Three EM regions and their respective distance boundaries. In the reactive near-field region, the evanescent wave is the strongest, where the power is concentrated in the surroundings of the source. In the radiating near-field region, the spherical wave propagation characteristics should be paid attention to. In the far-field region, the planar wave can be assumed due to the large propagation distance.