Lattice All-Pass Filter based Precoder Adaptation for MIMO Wireless Channels

TL;DR

This paper introduces a lattice all-pass filter-based precoder adaptation method for MIMO wireless channels, significantly reducing feedback requirements while maintaining high data rates in mmWave 5G systems.

Contribution

The paper proposes a novel matrix-lattice structure for time-domain precoder representation, enabling lower feedback and improved tracking compared to existing frequency domain methods.

Findings

Achieves up to 70% reduction in feedback burden.

Yields higher achievable rates than prior approaches.

Validated through extensive mmWave channel simulations.

Abstract

Modern 5G communication systems employ multiple-input multiple-output (MIMO) in conjunction with orthogonal frequency division multiplexing (OFDM) to enhance data rates, particularly for wideband millimetre wave (mmW) applications. Since these systems use a large number of subcarriers, feeding back the estimated precoder for even a subset of subcarriers from the receiver to the transmitter is prohibitive. Moreover, such frequency domain approaches also do not exploit the predominant line-of-sight component that is present in such channels to reduce feedback. In this work, we view the precoder in the time domain as a matrix all-pass filter, and model the discrete-time precoder filter using a matrix-lattice structure that aids in reducing the overall feedback while still maintaining the desired frequency-phase delay profile. This provides an efficient precoder representation across the subcarriers using fewer coefficients, and is amenable to tracking over time with much lower feedback than past approaches. Compared to frequency domain geodesic interpolation, Givens rotation based parameterisation, and the angle-delay domain approach that depends on approximate discrete-time representation, the proposed approach yields higher achievable rates with a much lower feedback burden. Via extensive simulations over mmW channel models, we confirm the effectiveness of our claims, and show that the proposed approach can reduce the feedback burden by up to 70%.