Quantized Multimode Precoding in Spatially Correlated Multi-Antenna Channels

TL;DR

This paper proposes a systematic design of quantized multimode precoders for multi-antenna channels with spatial correlation, leveraging statistical information and low-rate feedback to significantly improve throughput over fixed codebooks.

Contribution

It introduces a novel codebook skewing method based on spatial correlation, enhancing precoder adaptability and performance in realistic multi-antenna channel conditions.

Findings

Skewed codebooks outperform fixed i.i.d. codebooks in throughput.

Low-complexity maps effectively adapt precoders to spatial correlation.

Performance approaches that of perfect CSI with statistical power allocation.

Abstract

Multimode precoding, where the number of independent data-streams is adapted optimally, can be used to maximize the achievable throughput in multi-antenna communication systems. Motivated by standardization efforts embraced by the industry, the focus of this work is on systematic precoder design with realistic assumptions on the spatial correlation, channel state information (CSI) at the transmitter and the receiver, and implementation complexity. For spatial correlation of the channel matrix, we assume a general channel model, based on physical principles, that has been verified by many recent measurement campaigns. We also assume a coherent receiver and knowledge of the spatial statistics at the transmitter along with the presence of an ideal, low-rate feedback link from the receiver to the transmitter. The reverse link is used for codebook-index feedback and the goal of this work is to construct precoder codebooks, adaptable in response to the statistical information, such that the achievable throughput is significantly enhanced over that of a fixed, non-adaptive, i.i.d. codebook design. We illustrate how a codebook of semiunitary precoder matrices localized around some fixed center on the Grassmann manifold can be skewed in response to the spatial correlation via low-complexity maps that can rotate and scale submanifolds on the Grassmann manifold. The skewed codebook in combination with a lowcomplexity statistical power allocation scheme is then shown to bridge the gap in performance between a perfect CSI benchmark and an i.i.d. codebook design.