Training for Channel Estimation in Nonlinear Multi-Antenna Transceivers

TL;DR

This paper investigates the amount of training needed for effective channel estimation in nonlinear, coarsely quantized multi-antenna wireless systems, providing bounds and insights into optimal training durations.

Contribution

It derives a computable lower bound on the achievable rate for training-based schemes in large-antenna nonlinear systems, revealing counterintuitive optimal training lengths.

Findings

Optimal training symbols may be fewer than transmitters in quantized systems.

Training time decreases as the number of receivers increases.

The derived bounds are tight at high SNR.

Abstract

Recent efforts to obtain high data rates in wireless systems have focused on what can be achieved in systems that have nonlinear or coarsely quantized transceiver architectures. Estimating the channel in such a system is challenging because the nonlinearities distort the channel estimation process. It is therefore of interest to determine how much training is needed to estimate the channel sufficiently well so that the channel estimate can be used during data communication. We provide a way to determine how much training is needed by deriving a lower bound on the achievable rate in a training-based scheme that can be computed and analyzed even when the number of antennas is very large. This lower bound can be tight, especially at high SNR. One conclusion is that the optimal number of training symbols may paradoxically be smaller than the number of transmitters for systems with coarsely-quantized transceivers. We show how the training time can be strongly dependent on the number of receivers, and give an example where doubling the number of receivers reduces the training time by about 37 percent.