Deploying ultra-dense networks that operate on millimeter wave (mmWave) band is a promising way to address the tremendous growth on mobile data traffic. However, one key challenge of ultra-dense mmWave network (UDmmN) is beam management due to the high propagation delay, limited beam coverage as well as numerous beams and users. In this paper, a novel systematic beam control scheme is presented to tackle the beam management problem which is difficult due to the nonconvex objective function. We employ double deep Q-network (DDQN) under a federated learning (FL) framework to address the above optimization problem, and thereby fulfilling adaptive and intelligent beam management in UDmmN. In the proposed beam management scheme based on FL (BMFL), the non-rawdata aggregation can theoretically protect user privacy while reducing handoff cost. Moreover, we propose to adopt a data cleaning technique in the local model training for BMFL, with the aim to further strengthen the privacy protection of users while improving the learning convergence speed. Simulation results demonstrate the performance gain of our proposed scheme.
翻译:部署以毫米波(mmWave)波段运行的超常网络是解决移动数据流量巨大增长的一个很有希望的方法,然而,由于传播延迟、光束覆盖率有限以及许多光束和用户,超常-超常-毫米Wave网络(UDmmN)的光束管理面临一项关键挑战。本文提出了一个新型的系统波束控制计划,以解决由于非电流客观功能而难以解决的波束管理问题。我们在一个联合学习(FL)框架下采用双重深度Q网络(DDQN)来解决上述优化问题,从而在UDmmN实现适应性和智能的波束管理。在基于FL(BMFL)的拟议波束管理计划中,非光数据汇总可以在理论上保护用户隐私的同时降低手费。此外,我们提议在BMFL的本地模型培训中采用数据清理技术,目的是进一步加强用户的隐私保护,同时提高学习趋同速度。模拟结果显示了我们拟议计划的绩效。