End-User-Centric Collaborative MIMO: Performance Analysis and Proof of Concept

TL;DR

UE-CoMIMO presents a practical framework for extending UE antenna capabilities by coordinating nearby devices to form a virtual array, effectively enriching the channel via an end-to-end path $\mathbf{H}=\mathbf{H}_1\mathbf{H}_2$. The authors develop a theoretical SE analysis based on the singular values of the augmented channel, showing that $\mathbf{H}_2$ induces a rank-$N_2$ modification to $\mathbf{H}_1$ and can further boost performance through phase-shifter design. They propose two relay structures with distinct hardware and complexity tradeoffs, derive optimization problems (P1, P2-1, P2-2), and introduce a practical blind greedy algorithm (BG) to configure phases without full CSI. Extensive simulations, including indoor and outdoor ray-tracing, demonstrate that UE-CoMIMO can significantly improve spectral efficiency, rank, and throughput over conventional Pr-UE MIMO, especially when carefully optimized. The Proof-of-Concept experiments validate real-world feasibility in a 5G environment, highlighting the potential of UE-CoMIMO for practical deployments and paving the way for multi-Co-UE extensions and sensing-enabled enhancements.

Abstract

The trend toward using increasingly large arrays of antenna elements continues. However, fitting more antennas into the limited space available on user equipment (UE) within the currently popular Frequency Range 1 spectrum presents a significant challenge. This limitation constrains the capacity-scaling gains for end users, even when networks support a higher number of antennas. To address this issue, we explore a user-centric collaborative MIMO approach, termed UE-CoMIMO, which leverages several fixed or portable devices within a personal area to form a virtually expanded antenna array. This paper develops a comprehensive mathematical framework to analyze the performance of UE-CoMIMO. Our analytical results demonstrate that UE-CoMIMO can significantly enhance the system's effective channel response within the current communication system without requiring extensive modifications. Further performance improvements can be achieved by optimizing the phase shifters on the expanded antenna arrays at the collaborative devices. These findings are corroborated by ray-tracing simulations. Beyond the simulations, we implemented these collaborative devices and successfully conducted over-the-air validation in a real 5G environment, showcasing the practical potential of UE-CoMIMO. Several practical perspectives are discussed, highlighting the feasibility and benefits of this approach in real-world scenarios.

Figures (11)

• Figure 1: A UE-CoMIMO System.
• Figure 2: Graph of the secular equation with $\sigma_{i}^2(\qH_1) = (1,2,3,4)$ for (a) $\| \qh' \|_2^2 = 1$ and (b) $\| \qh' \|_2^2 = 4$.
• Figure 3: Illustration of two structures employing PSs at Co-UE.
• Figure 4: Histograms of the SE achieved by joint ES and separate ES in three different positions: (a) $(1,1,1),\lambda_{\rm H}$, (b) $(5,5,5),\lambda_{\rm H}$, and (c) $(10,10,10),\lambda_{\rm H}$. (d)--(f) depict the corresponding trajectories of the BG algorithm. The achieved SEs are marked by $\times$, $\circ$, $\triangleright$, and $\ast$ for the iterations $[1 : N_{\rm c}]$, $[N_{\rm c}+1 : 2N_{\rm c}]$, $[2N_{\rm c}+1 : 3N_{\rm c}]$, and $[3N_{\rm c}+1 : 4N_{\rm c}]$.
• Figure 5: SEs of various algorithms, particularly for Structure 2, in three different positions: (a) $(1,1,1)\,\lambda_{\rm H}$, (b) $(5,5,5)\,\lambda_{\rm H}$, and (c) $(10,10,10)\,\lambda_{\rm H}$. The algorithms include the optimal beamforming, the SDR for the optimal (continuous) phases, ES for discrete phases, and the BG algorithm for discrete phases under $Q = 32$, $16$, and $2$ levels.