# Capacity of a Multiple-Antenna Fading Channel with a Quantized Precoding Matrix

## TL;DR

This paper analyzes the capacity of MIMO channels with quantized precoding, demonstrating that Random Vector Quantization (RVQ) is asymptotically optimal and providing insights into feedback requirements for different receiver types.

## Contribution

It derives the large system capacity of quantized beamforming with RVQ and compares its performance with other schemes, extending analysis to arbitrary-rank precoding matrices.

## Key findings

- RVQ is asymptotically optimal for beamforming.
- Linear MMSE receivers require slightly more feedback than optimal receivers.
- Matched filter receivers need significantly more feedback.

## Abstract

Given a multiple-input multiple-output (MIMO) channel, feedback from the receiver can be used to specify a transmit precoding matrix, which selectively activates the strongest channel modes. Here we analyze the performance of Random Vector Quantization (RVQ), in which the precoding matrix is selected from a random codebook containing independent, isotropically distributed entries. We assume that channel elements are i.i.d. and known to the receiver, which relays the optimal (rate-maximizing) precoder codebook index to the transmitter using B bits. We first derive the large system capacity of beamforming (rank-one precoding matrix) as a function of B, where large system refers to the limit as B and the number of transmit and receive antennas all go to infinity with fixed ratios. With beamforming RVQ is asymptotically optimal, i.e., no other quantization scheme can achieve a larger asymptotic rate. The performance of RVQ is also compared with that of a simpler reduced-rank scalar quantization scheme in which the beamformer is constrained to lie in a random subspace. We subsequently consider a precoding matrix with arbitrary rank, and approximate the asymptotic RVQ performance with optimal and linear receivers (matched filter and Minimum Mean Squared Error (MMSE)). Numerical examples show that these approximations accurately predict the performance of finite-size systems of interest. Given a target spectral efficiency, numerical examples show that the amount of feedback required by the linear MMSE receiver is only slightly more than that required by the optimal receiver, whereas the matched filter can require significantly more feedback.