Realistic Channel Models Pre-training
This work addresses the need for improved channel modeling in wireless communications, offering a practical tool for network operators and organizations, but it appears incremental as it builds on existing channel modeling approaches.
The paper tackles the problem of creating a realistic channel model that combines the accuracy of deterministic models with the uniformity of stochastic models, achieving similar accuracy to deterministic models while maintaining uniformity. It introduces a multi-domain channel embedding method with self-attention for feature extraction and uses self-supervised pre-training on wireless channel data, showing that the pre-trained model is effective even without fine-tuning.
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.