Scalable Long-Term Beamforming for Massive Multi-User MIMO

TL;DR

This paper introduces scalable long-term beamforming techniques for massive multi-user MIMO systems, optimizing capacity while reducing computational and channel estimation overhead.

Contribution

It presents an optimal projection matrix computation method and efficient matrix algorithms suitable for hardware implementation in large-scale MIMO systems.

Findings

Ray tracing simulations show minimal SINR loss compared to instantaneous MMSE beamforming.

Proposed methods significantly reduce overhead and computational complexity.

Matrix inverse square root optimization enhances capacity bounds.

Abstract

Fully digital massive MIMO systems with large numbers (1000+) of antennas offer dramatically increased capacity gains from spatial multiplexing and beamforming. Designing digital receivers that can scale to these array dimensions presents significant challenges regarding both channel estimation overhead and digital computation. In the massive MIMO setting, long-term beamforming is widely-used since it offers significant reductions in both computation and channel estimation overhead. Long-term beamforming operates by projecting the data onto a low-dimensional subspace that can be tracked at a relatively slow time-scale from the long-term channel parameters. In this setting, we show how to optimally compute the projection matrix to maximize a capacity upper-bound using a matrix inverse square root. Computationally efficient methods are then presented to perform the matrix computation. The methods can be realized with matrix-matrix multiplies, making them amenable to systolic array implementations in hardware. Error analysis bounds on the degradation in the SINR for users are derived. Ray tracing simulations in a realistic rural uplink setting show minimal loss relative to complete instantaneous MMSE beamforming while offering significant overhead and computational gains.