A New Solution for MU-MISO Symbol-Level Precoding: Extrapolation and Deep Unfolding

TL;DR

This paper introduces a novel symbol-level extrapolation strategy and neural network unfolding for CI-based precoding in multi-antenna systems, significantly reducing computational complexity while maintaining performance.

Contribution

It proposes a new SLE strategy and neural network unfolding for CI precoding, improving efficiency and interpretability over traditional methods.

Findings

Significant reduction in computational complexity and time complexity.

Marginal performance loss compared to conventional methods.

Effective application to PSK and QAM modulations.

Abstract

Constructive interference (CI) precoding, which converts the harmful multi-user interference into beneficial signals, is a promising and efficient interference management scheme in multi-antenna communication systems. However, CI-based symbol-level precoding (SLP) experiences high computational complexity as the number of symbol slots increases within a transmission block, rendering it unaffordable in practical communication systems. In this paper, we propose a symbol-level extrapolation (SLE) strategy to extrapolate the precoding matrix by leveraging the relationship between different symbol slots within in a transmission block, during which the channel state information (CSI) remains constant, where we design a closed-form iterative algorithm based on SLE for both PSK and QAM modulation. In order to further reduce the computational complexity, a sub-optimal closed-form solution based on SLE is further developed for PSK and QAM, respectively. Moreover, we design an unsupervised SLE-based neural network (SLE-Net) to unfold the proposed iterative algorithm, which helps enhance the interpretability of the neural network. By carefully designing the loss function of the SLE-Net, the time-complexity of the network can be reduced effectively. Extensive simulation results illustrate that the proposed algorithms can dramatically reduce the computational complexity and time complexity with only marginal performance loss, compared with the conventional SLP design methods.