Approximate Gram-Matrix Interpolation for Wideband Massive MU-MIMO Systems

TL;DR

This paper introduces approximate algorithms for Gram-matrix computation in wideband massive MU-MIMO systems, reducing complexity while maintaining near-optimal performance and improving robustness against channel estimation errors.

Contribution

It proposes novel approximate interpolation algorithms that exploit subcarrier correlation and channel hardening to lower computational complexity in massive MU-MIMO systems.

Findings

Achieve near-optimal error-rate with fewer Gram-matrix computations

Significantly reduce computational complexity

Enhanced robustness to channel-estimation errors

Abstract

Numerous linear and non-linear data-detection and precoding algorithms for wideband massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems that rely on orthogonal frequency-division multiplexing (OFDM) or single-carrier frequency-division multiple access (SC-FDMA) require the computation of the Gram matrix for each active subcarrier. Computing the Gram matrix for each active subcarrier, however, results in excessively high computational complexity. In this paper, we propose novel, approximate algorithms that significantly reduce the complexity of Gram-matrix computation by simultaneously exploiting correlation across subcarriers and channel hardening. We show analytically that a small fraction of Gram-matrix computations in combination with approximate interpolation schemes are sufficient to achieve near-optimal error-rate performance at low computational complexity in massive MU-MIMO systems. We also demonstrate that the proposed methods exhibit improved robustness against channel-estimation errors compared to exact Gram-matrix interpolation algorithms that typically require high computational complexity.