The growing adoption of mmWave frequency bands to realize the full potential of 5G, turns beamforming into a key enabler for current and next-generation wireless technologies. Many mmWave networks rely on beam selection with Grid-of-Beams (GoB) approach to handle user-beam association. In beam selection with GoB, users select the appropriate beam from a set of pre-defined beams and the overhead during the beam selection process is a common challenge in this area. In this paper, we propose an Advantage Actor Critic (A2C) learning-based framework to improve the GoB and the beam selection process, as well as optimize transmission power in a mmWave network. The proposed beam selection technique allows performance improvement while considering transmission power improves Energy Efficiency (EE) and ensures the coverage is maintained in the network. We further investigate how the proposed algorithm can be deployed in a Service Management and Orchestration (SMO) platform. Our simulations show that A2C-based joint optimization of beam selection and transmission power is more effective than using Equally Spaced Beams (ESB) and fixed power strategy, or optimization of beam selection and transmission power disjointly. Compared to the ESB and fixed transmission power strategy, the proposed approach achieves more than twice the average EE in the scenarios under test and is closer to the maximum theoretical EE.
翻译:越来越多地采用毫米Wave频带,以实现5G的全部潜力,使光束变成当前和下一代无线技术的关键促进器。许多毫米Wave网络依靠使用Beams网(GoB)的方法选择光束来处理用户-波束联系。在与GoB选择光束时,用户从一组预先定义的光束和波段选择过程中选择适当的光束,这是这方面的一个共同挑战。在本文件中,我们提议建立一个以A2C为基础的优劣动动动动感(A2C)学习基础框架,以改进GoB和Baam选择过程,以及优化毫米Wave网络的传输能力。拟议的光束选择技术可以提高性能,同时考虑传输能力提高能源效率并确保网络的覆盖面。我们进一步调查如何将拟议的算法部署在服务管理和预设(SMO)平台上。我们的模拟表明,基于A2C的联合选择和传输能力比使用NamBam选择和EVALE的最优化战略更为有效。在使用Nespace-SBeam和EOirimal 最精确的传输战略下,在更接近地实现E-ESBAirstirstal 和E-Siral 和E-Sirstimest 战略下,在更接近更接近更接近地进行最高级的传输战略。