Memristor-Based Meta-Learning for Fast mmWave Beam Prediction in Non-Stationary Environments

TL;DR

This paper introduces a memristor-based meta-learning framework that enables rapid and accurate mmWave beam prediction in dynamic environments, reducing dependence on large datasets and improving adaptability.

Contribution

The proposed M-ML framework leverages memristors to improve meta-learning for mmWave beamforming, enhancing real-time prediction and adaptability in non-stationary environments.

Findings

High prediction accuracy in new environments

Reduced need for large training datasets

Enhanced generalization and adaptability

Abstract

Traditional machine learning techniques have achieved great success in improving data-rate performance and reducing latency in millimeter wave (mmWave) communications. However, these methods still face two key challenges: (i) their reliance on large-scale paired data for model training and tuning which limits performance gains and makes beam predictions outdated, especially in multi-user mmWave systems with large antenna arrays, and (ii) meta-learning (ML)-based beamforming solutions are prone to overfitting when trained on a limited number of tasks. To address these issues, we propose a memristorbased meta-learning (M-ML) framework for predicting mmWave beam in real time. The M-ML framework generates optimal initialization parameters during the training phase, providing a strong starting point for adapting to unknown environments during the testing phase. By leveraging memory to store key data, M-ML ensures the predicted beamforming vectors are wellsuited to episodically dynamic channel distributions, even when testing and training environments do not align. Simulation results show that our approach delivers high prediction accuracy in new environments, without relying on large datasets. Moreover, MML enhances the model's generalization ability and adaptability.