The fifth generation of wireless communications (5G) promises massive increases in traffic volume and data rates, as well as improved reliability in voice calls. Jointly optimizing beamforming, power control, and interference coordination in a 5G wireless network to enhance the communication performance to end users poses a significant challenge. In this paper, we formulate the joint design of beamforming, power control, and interference coordination as a non-convex optimization problem to maximize the signal to interference plus noise ratio (SINR) and solve this problem using deep reinforcement learning. By using the greedy nature of deep Q-learning to estimate future rewards of actions and using the reported coordinates of the users served by the network, we propose an algorithm for voice bearers and data bearers in sub-6 GHz and millimeter wave (mmWave) frequency bands, respectively. The algorithm improves the performance measured by SINR and sum-rate capacity. In realistic cellular environments, the simulation results show that our algorithm outperforms the link adaptation industry standards for sub-6 GHz voice bearers. For data bearers in the mmWave frequency band, our algorithm approaches the maximum sum rate capacity, but with less than 4% of the required run time.

中文翻译：

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5G 网络的深度强化学习：联合波束成形、功率控制和干扰协调

第五代无线通信 (5G) 有望大幅增加流量和数据速率，并提高语音通话的可靠性。在 5G 无线网络中联合优化波束赋形、功率控制和干扰协调以提高终端用户的通信性能是一项重大挑战。在本文中，我们将波束成形、功率控制和干扰协调的联合设计制定为一个非凸优化问题，以最大化信干噪比 (SINR)，并使用深度强化学习解决这个问题。通过使用深度 Q 学习的贪婪特性来估计未来的行动奖励，并使用网络服务的用户的报告坐标，我们分别针对低于 6 GHz 和毫米波 (mmWave) 频带中的语音承载和数据承载提出了一种算法。该算法提高了由 SINR 和总速率容量衡量的性能。在现实蜂窝环境中，仿真结果表明，我们的算法优于低于 6 GHz 语音承载的链路自适应行业标准。对于毫米波频段中的数据承载，我们的算法接近最大总速率容量，但所需运行时间不到 4%。