Combining Reciprocity and CSI Feedback in MIMO Systems

TL;DR

This paper proposes a hybrid approach combining reciprocity and CSI feedback in Massive MIMO systems, demonstrating improved downlink precoding performance through optimized mapping strategies in a finite channel realization setting.

Contribution

It introduces a novel framework that integrates reciprocity and codebook-based feedback, along with algorithms for designing effective channel-to-signal mappings in Massive MIMO systems.

Findings

Reciprocity-based mapping outperforms traditional TDD and FDD methods under certain conditions.

Optimized sequence mappings improve downlink channel estimation accuracy.

The proposed algorithms effectively find good channel-to-signal mappings.

Abstract

Reciprocity-based time-division duplex (TDD) Massive MIMO (multiple-input multiple-output) systems utilize channel estimates obtained in the uplink to perform precoding in the downlink. However, this method has been criticized of breaking down, in the sense that the channel estimates are not good enough to spatially separate multiple user terminals, at low uplink reference signal signal-to-noise ratios, due to insufficient channel estimation quality. Instead, codebook-based downlink precoding has been advocated for as an alternative solution in order to bypass this problem. We analyze this problem by considering a "grid-of-beams world" with a finite number of possible downlink channel realizations. Assuming that the terminal accurately can detect the downlink channel, we show that in the case where reciprocity holds, carefully designing a mapping between the downlink channel and the uplink reference signals will perform better than both the conventional TDD Massive MIMO and frequency-division duplex (FDD) Massive MIMO approach. We derive elegant metrics for designing this mapping, and further, we propose algorithms that find good sequence mappings.