Joint Bi-Directional Training of Nonlinear Precoders and Receivers in Cellular Networks

TL;DR

This paper proposes a joint bi-directional training method for nonlinear precoders and receivers in cellular networks, improving interference management and performance without explicit channel estimation.

Contribution

It introduces a bi-directional training approach for nonlinear precoders and receivers that bypasses explicit channel estimation, enhancing cellular network performance.

Findings

Nonlinear filters outperform linear filters with limited iterations.

Bi-directional training effectively updates filters without channel state information.

Substantial performance gains demonstrated through numerical simulations.

Abstract

Joint optimization of nonlinear precoders and receive filters is studied for both the uplink and downlink in a cellular system. For the uplink, the base transceiver station (BTS) receiver implements successive interference cancellation, and for the downlink, the BTS station pre-compensates for the interference with Tomlinson-Harashima precoding (THP). Convergence of alternating optimization of receivers and transmitters in a single cell is established when filters are updated according to a minimum mean squared error (MMSE) criterion, subject to appropriate power constraints. Adaptive algorithms are then introduced for updating the precoders and receivers in the absence of channel state information, assuming time-division duplex transmissions with channel reciprocity. Instead of estimating the channels, the filters are directly estimated according to a least squares criterion via bi-directional training: Uplink pilots are used to update the feedforward and feedback filters, which are then used as interference pre-compensation filters for downlink training of the mobile receivers. Numerical results show that nonlinear filters can provide substantial gains relative to linear filters with limited forward-backward iterations.