Towards 6G MIMO: Massive Spatial Multiplexing, Dense Arrays, and Interplay Between Electromagnetics and Processing

TL;DR

The paper addresses the ambitious goal of 6G UM-MIMO by showing that ultra-dense, large-aperture arrays must be modeled with physically consistent electromagnetics and circuit theory. It combines near-field beamforming, spatial degrees-of-freedom analysis, and advanced channel estimation with a linear EM framework based on impedance and Green’s functions to reveal fundamental limits and practical designs. It introduces EM-aware array models, polarization considerations, and several array architectures (CAP, DMA, fluid) while stressing the joint design of antennas and signal processing. The results indicate near-field propagation can dramatically increase spatial multiplexing capabilities and SSE if hardware and models capture mutual coupling, near-field effects, and polarization, informing feasible 6G deployments and guiding future field trials.

Abstract

The increasing demand for wireless data transfer has been the driving force behind the widespread adoption of Massive MIMO (multiple-input multiple-output) technology in 5G. The next-generation MIMO technology is now being developed to cater to the new data traffic and performance expectations generated by new user devices and services in the next decade. The evolution towards "ultra-massive MIMO (UM-MIMO)" is not only about adding more antennas but will also uncover new propagation and hardware phenomena that can only be treated by jointly utilizing insights from the communication, electromagnetic (EM), and circuit theory areas. This article offers a comprehensive overview of the key benefits of the UM-MIMO technology and the associated challenges. It explores massive multiplexing facilitated by radiative near-field effects, characterizes the spatial degrees-of-freedom, and practical channel estimation schemes tailored for massive arrays. Moreover, we provide a tutorial on EM theory and circuit theory, and how it is used to obtain physically consistent antenna and channel models. Subsequently, the article describes different ways to implement massive and dense antenna arrays, and how to co-design antennas with signal processing. The main open research challenges are identified at the end.

Figures (23)

• Figure 1: The EM field looks different depending on the distance from the transmitting aperture antenna. The wavefront is almost planar in the far-field, while the spherical curvature is clearly noticeable in the radiative near-field but not reactive effects such as inductive coupling and evanescent waves.
• Figure 2: The curvature of an impinging spherical wave creates a delay $\frac{\Delta}{c}$ between the center of the receiver and the edge. The delay turns into a phase-shift of $2\pi f_c\frac{\Delta}{c}=\frac{2\pi}{\lambda}\Delta$.
• Figure 3: The (normalized) real part of the electric field in \ref{['eq:electric-field']} is shown for an antenna array deployed in the $xy$-plane. The transmitter is located in a broadside direction in the radiative near-field, leading to spherical phase variations.
• Figure 4: Beamforming leads to a limited beamwidth regardless of whether the signal is focused on a receiver in the near-field or far-field. However, if the focus point is at a closer distance than $d_\textrm{F}/10$, the beamdepth will be finite. This is not the case when the focus point is beyond $d_\textrm{F}/10$, because then the beam continues until infinity.
• Figure 5: The average uplink SE per UE and sum SE in a setup with $5000$ antennas. The exact near-field propagation model leads to much higher values than in an identical setup where a mismatched far-field approximation is utilized.