Harnessing Tensor Structures -- Multi-Mode Reservoir Computing and Its Application in Massive MIMO

TL;DR

This paper introduces multi-mode reservoir computing using tensor structures for efficient neural network processing, with a focus on symbol detection in massive MIMO-OFDM systems, demonstrating robustness and adaptability in wireless communications.

Contribution

The paper proposes a novel multi-mode reservoir computing framework utilizing tensor data formats and an ALS-based learning algorithm, advancing neural network efficiency and robustness in MIMO-OFDM systems.

Findings

Less complex than traditional RC with similar performance

Effective in symbol detection with limited training data

Robust against channel errors and hardware non-linearity

Abstract

In this paper, we introduce a new neural network (NN) structure, multi-mode reservoir computing (Multi-Mode RC). It inherits the dynamic mechanism of RC and processes the forward path and loss optimization of the NN using tensor as the underlying data format. Multi-Mode RC exhibits less complexity compared with conventional RC structures (e.g. single-mode RC) with comparable generalization performance. Furthermore, we introduce an alternating least square-based learning algorithm for Multi-Mode RC as well as conduct the associated theoretical analysis. The result can be utilized to guide the configuration of NN parameters to sufficiently circumvent over-fitting issues. As a key application, we consider the symbol detection task in multiple-input-multiple-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) systems with massive MIMO employed at the base stations (BSs). Thanks to the tensor structure of massive MIMO-OFDM signals, our online learning-based symbol detection method generalizes well in terms of bit error rate even using a limited online training set. Evaluation results suggest that the Multi-Mode RC-based learning framework can efficiently and effectively combat practical constraints of wireless systems (i.e. channel state information (CSI) errors and hardware non-linearity) to enable robust and adaptive learning-based communications over the air.