Leveraging Self-Supervised Learning for MIMO-OFDM Channel Representation and Generation

TL;DR

This paper introduces a self-supervised learning framework that captures MIMO-OFDM channel representations and generates channels from geolocation data, enhancing geolocation-based MIMO transmission performance.

Contribution

It proposes a novel self-supervised contrastive learning approach with Transformer models and a diffusion generator for channel representation and generation based on geolocation.

Findings

Effective channel representation from unlabeled data

Improved MIMO transmission performance using generated channels

Successful application on public ray-tracing dataset

Abstract

In communications theory, the capacity of multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems is fundamentally determined by wireless channels, which exhibit both diversity and correlation in spatial, frequency and temporal domains. It is further envisioned to exploit the inherent nature of channels, namely representation, to achieve geolocation-based MIMO transmission for 6G, exemplified by the fully-decoupled radio access network (FD-RAN). Accordingly, this paper first employs self-supervised learning to obtain channel representation from unlabeled channel, then proposes a channel generation assisted approach for determining MIMO precoding matrix solely based on geolocation. Specifically, we exploit the small-scale temporal domain variations of channels at a fixed geolocation, and design an ingenious pretext task tailored for contrastive learning. Then, a Transformer-based encoder is trained to output channel representations. We further develop a conditional diffusion generator to generate channel representations from geolocation. Finally, a Transformer-encoder-based decoder is utilized to reconstruct channels from generated representations, where the optimal channel is selected for calculating the precoding matrix for both single and dual BS transmission. We conduct experiments on a public ray-tracing channel dataset, and the extensive simulation results demonstrate the effectiveness of our channel representation method, and also showcase the performance improvement in geolocation-based MIMO transmission.