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.

翻译：无线系统的优化算法在提升其性能与效率方面发挥着基础性作用。然而，现有文献中的传统优化算法复杂度通常随无线系统中发射天线数量和通信用户数的增加而呈指数级增长。因此，在大规模场景下，这些优化算法极高的复杂度使其难以实际应用，也阻碍了在优化条件下评估大规模无线系统的性能。为克服这一局限，本文提出采用一种基于无监督元学习的方法，以显著降低的复杂度直接执行非凸优化。为验证所提出的基于元学习的解决方案的有效性，本文针对以下三种新兴的6G技术中的和速率最大化问题展开研究：分层速率分割多址接入、集成感知与通信，以及超对角可重构智能表面。数值结果表明，所提出的基于元学习的优化框架能够成功优化系统性能，并揭示所研究的三种6G技术在大规模场景下运行中尚未被认知的特性。