Realistic Channel Models Pre-training

TL;DR

This paper introduces a neural network-based realistic wireless channel model that combines deterministic accuracy with stochastic uniformity, using multi-domain features and self-supervised pre-training for versatile downstream applications.

Contribution

It presents a novel multi-domain channel embedding with self-attention, enabling a universal pre-trained model adaptable to various channel modeling tasks.

Findings

Achieves deterministic-like accuracy and stochastic-like uniformity.

Pre-trained model can be fine-tuned with user data for specific tasks.

Pre-trained model effectively captures wireless channel characteristics.

Abstract

In this paper, we propose a neural-network-based realistic channel model with both the similar accuracy as deterministic channel models and uniformity as stochastic channel models. To facilitate this realistic channel modeling, a multi-domain channel embedding method combined with self-attention mechanism is proposed to extract channel features from multiple domains simultaneously. This 'one model to fit them all' solution employs available wireless channel data as the only data set for self-supervised pre-training. With the permission of users, network operators or other organizations can make use of some available user specific data to fine-tune this pre-trained realistic channel model for applications on channel-related downstream tasks. Moreover, even without fine-tuning, we show that the pre-trained realistic channel model itself is a great tool with its understanding of wireless channel.