基于深度强化学习的多小区NOMA能效优化功率分配算法

在下行多小区非正交多址接入系统中，功率分配是决定系统性能的关键因素之一。由于多小区系统间的功率优化问题的非凸性，获得最优功率分配在求解上非常困难。为此提出了一种基于深度强化学习最大化能效的功率分配算法，将深度Q网络作为动作?状态值函数，将系统能效直接设置为奖励函数，优化信道功率分配，使系统能量效率最大化。仿真结果表明，该算法比加权最小均方误差、分式规划、最大功率和随机功率算法等能够获得更高的系统能量效率，在算法计算复杂度、收敛速度和稳定性方面也有较好表现。

Multi-Cell NOMA Energy Efficiency Optimization Power Allocation Algorithm Based on Deep Reinforcement Learning

In a downlink multi-cell non-orthogonal multiple access system, power allocation is one of the key factors to determine system performance. Due to the non-convexity of the power optimization problem among multi-cell systems, it is very difficult to obtain the optimal power allocation. The power allocation algorithm based on deep reinforcement learning is proposed to maximize energy efficiency in this paper, which is simple and efficient. The algorithm takes the deep Q network as the action-state value function, system energy efficiency is directly set as a reward function, which optimizes channel power allocation and maximizes system energy efficiency. The simulation results show that the algorithm of proposed scheme is more effective than the weighted minimum mean square error, fractional programming, maximum power and random power algorithms in achieving higher system energy efficiency. The scheme also has better performances in algorithm calculation complexity, convergence speed and stability.