Enabling Covariance-Based Feedback in Massive MIMO: A User Classification Approach

TL;DR

This paper introduces a hybrid feedback scheme for massive MIMO systems that classifies users based on statistical and instantaneous channel information, improving sum rate performance under feedback constraints.

Contribution

It proposes a novel user classification-based feedback scheme leveraging statistical separability, enhancing feedback efficiency in massive MIMO systems.

Findings

Scheme achieves higher sum rates under feedback constraints

User classification improves feedback efficiency

Hybrid approach outperforms traditional feedback methods

Abstract

In this paper, we propose a novel channel feedback scheme for frequency division duplexing massive multi-input multi-output systems. The concept uses the notion of user statistical separability which was hinted in several prior works in the massive antenna regime but not fully exploited so far. We here propose a hybrid statistical-instantaneous feedback scheme based on a user classification mechanism where the classification metric derives from a rate bound analysis. According to classification results, a user either operates on a statistical feedback mode or instantaneous mode. Our results illustrate the sum rate advantages of our scheme under a global feedback overhead constraint.