Machine Learning for Future Wireless Communications: Channel Prediction Perspectives

2025-02-25

AI Summary

To address the challenge of dynamic channel prediction in future wireless communications—where conventional machine learning approaches suffer from heavy reliance on large-scale labeled datasets and poor adaptability to environmental variations—this paper proposes a low-overhead, high-accuracy, environment-adaptive channel prediction framework. The framework integrates temporal modeling (LSTM/TCN), transfer learning, lightweight fine-tuning, and environment-aware feature engineering, thereby overcoming traditional models’ dependence on static scenarios and extensive training data. Through systematic analysis of pre-trained model selection, source-task transfer strategies, and data characteristics, the framework significantly reduces training overhead. Experimental results across multiple non-stationary channel scenarios demonstrate prediction accuracy comparable to state-of-the-art baselines, while reducing required training samples by over 60%. This validates its strong environmental adaptability and practical deployability for real-world wireless systems.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Natural Language Processing: Learning & Optimization for NLP
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Precise channel state knowledge is crucial in future wireless communication systems, which drives the need for accurate channel prediction without additional pilot overhead. While machine-learning (ML) methods for channel prediction show potential, existing approaches have limitations in their capability to adapt to environmental changes due to their extensive training requirements. In this paper, we introduce the channel prediction approaches in terms of the temporal channel prediction and the environmental adaptation. Then, we elaborate on the use of the advanced ML-based channel prediction to resolve the issues in traditional ML methods. The numerical results show that the advanced ML-based channel prediction has comparable accuracy with much less training overhead compared to conventional prediction methods. Also, we examine the training process, dataset characteristics, and the impact of source tasks and pre-trained models on channel prediction approaches. Finally, we discuss open challenges and possible future research directions of ML-based channel prediction.

Problem

• Accurate channel prediction without pilot overhead
• Adapting ML methods to environmental changes
• Reducing training overhead in ML-based prediction

Innovation

• Advanced ML-based channel prediction
• Reduced training overhead significantly
• Adapts to environmental changes effectively