Meta-Learning Based Optimization for Large Scale Wireless Systems

TL;DR

This paper introduces an unsupervised meta-learning approach to optimize large-scale wireless systems efficiently, overcoming the exponential complexity of traditional methods, and demonstrates its effectiveness on emerging 6G technologies.

Contribution

It proposes a novel meta-learning based optimization method that significantly reduces complexity for large-scale wireless systems, enabling performance assessment and optimization in regimes previously infeasible.

Findings

Successfully optimized sum-rate for H-RSMA, ISAC, and BD-RIS technologies.

Demonstrated reduced complexity compared to conventional algorithms.

Revealed new operational insights in large-scale wireless systems.

Abstract

Optimization algorithms for wireless systems play a fundamental role in improving their performance and efficiency. However, it is known that the complexity of conventional optimization algorithms in the literature often exponentially increases with the number of transmit antennas and communication users in the wireless system. Therefore, in the large scale regime, the astronomically large complexity of these optimization algorithms prohibits their use and prevents assessing large scale wireless systems performance under optimized conditions. To overcome this limitation, this work proposes instead the use of an unsupervised meta-learning based approach to directly perform non-convex optimization at significantly reduced complexity. To demonstrate the effectiveness of the proposed meta-learning based solution, the sum-rate (SR) maximization problem for the following three emerging 6G technologies is contemplated: hierarchical rate-splitting multiple access (H-RSMA), integrated sensing and communication (ISAC), and beyond-diagonal reconfigurable intelligent surfaces (BD-RIS). Through numerical results, it is demonstrated that the proposed meta-learning based optimization framework is able to successfully optimize the performance and also reveal unknown aspects of the operation in the large scale regime for the considered three 6G technologies.