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

过去一年，我主要就两个课题进行研究。即：1）联合动态频谱接入和复用技术，优化频谱和能量效率; 2）实际不确定无线环境下基于人工智能的通信参数自动决策。具体而言，首先制定了优化目标问题，考虑动态频谱接入和NOMA。然后，通过穷举搜索和量子退火方法，得到了最优的传输参数，包括信道和传输功率。通过将动态频谱接入和NOMA联合考虑时的频谱利用率与未联合考虑时的频谱利用率进行比较，明确了联合考虑时频谱利用率可以得到提高。针对第二个问题，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。AI算法包括基于激光混沌的多臂强盗，拔河动力学和深度强化学习方法。在实际的不确定无线环境中，对所提出的算法进行了仿真和实验验证，研究成果已发表1篇期刊论文和3篇以上相关国际论文。此外，还提交了2篇期刊论文。

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.