AI based optimization of the spectrum and energy efficiency for Intelligent 6G

一年来，我主要进行了两个课题的研究。即：1）用于优化频谱和能量效率的联合动态频谱接入和复用技术。2）实际不确定无线环境中基于人工智能的自动通信参数决策。对于第一个主题，提出了一种针对动态非正交多址（NOMA）系统的基于量子退火的资源分配。具体来说，首先考虑动态频谱接入和 NOMA，制定优化目标问题。然后，通过穷举搜索和量子退火方法得出最佳传输参数，包括信道和传输功率。与同时考虑动态频谱接入和NOMA时的频谱效率与不考虑动态频谱接入和NOMA时的频谱效率相比，明确了频谱效率是可以提高的。对于第二个主题，提出了几种AI算法以分散的方式确定传输参数，包括信道、功率和扩频因子。人工智能算法包括基于激光混沌的多臂老虎机、拔河动力学和深度强化学习方法。此外，所提出的算法通过在实际不确定无线环境中进行的仿真和实验进行了验证。基于研究成果，已发表期刊论文1篇和相关国际论文3篇以上。此外，已提交期刊论文2篇。

During the past year, I have mainly conducted research on two topics. That is:1) Joint dynamic spectrum access and multiplexing techniques for optimizing the spectrum and energy efficiency.2) Automatic communication parameter decision based on AI in a practical uncertain wireless environment.For the first topic, a quantum annealing-based resource allocation was proposed for dynamic non-orthogonal multiple access (NOMA) systems. Specifically, an optimization objective problem is first formulated, considering the dynamic spectrum access and NOMA. Then, the optimal transmission parameters, including channel and transmission power, were derived through an exhaustive search and quantum annealing methods. Compared with the spectrum efficiency while jointly considering the dynamic spectrum access and NOMA to that without a joint consideration, it is clarified that the spectrum efficiency can be improved.For the second topic, several AI algorithms were proposed to determine the transmission parameters in a decentralized manner, including the channel, power, and spreading factor. The AI algorithms include laser chaos-based multi-armed bandit, Tug of War dynamics, and deep reinforcement learning methods. In addition, the proposed algorithms were verified by simulation and experiments conducted in a practical uncertain wireless environment.Based on the research results, 1 journal paper and more than 3 related international papers have been published. In addition, 2 journal papers have been submitted.