arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.11009eess.SPcs.NI
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移动毫米波网络中基于学习的切换

Learning-based Handover in Mobile Millimeter-wave Networks

Sara Khosravi, Hossein S. Ghadikolaei, Marina Petrova

摘要:

毫米波(mmWave)通信被认为是未来蜂窝和无线网络中超高数据速率的关键使能器。毫米波系统中基站(BS)和用户之间通过波束成形实现的定向通信的需要增加了信道估计的复杂度。此外,为了提供更好的覆盖,需要BS的密集部署,这导致频繁的切换和增加的关联开销。在本文中,我们提出了一种方法,共同解决波束成形和切换问题。我们的解决方案需要一个有效的波束形成方法与最少数量的导频和基于学习的切换方法支持移动场景。我们使用强化学习算法来学习在移动用户的不同位置的备份BS的最佳选择。我们表明,我们的方法提供了高速率和可靠性,在所有位置的用户的轨迹与最小数量的brawers。基于几何毫米波信道模型和真实建筑物地图数据的室外环境仿真结果表明,我们提出的解决方案在可实现的瞬时速率和轨迹速率方面具有优越的性能。

英文摘要:

Millimeter-wave (mmWave) communication is considered as a key enabler of ultra-high data rates in the future cellular and wireless networks. The need for directional communication between base stations (BSs) and users in mmWave systems, that is achieved through beamforming, increases the complexity of the channel estimation. Moreover, in order to provide better coverage, dense deployment of BSs is required which causes frequent handovers and increased association overhead. In this paper, we present an approach that jointly addresses the beamforming and handover problems. Our solution entails an efficient beamforming method with a minimum number of pilots and a learning-based handover method supporting mobile scenarios. We use reinforcement learning algorithm to learn the optimal choices of the backup BSs in different locations of a mobile user. We show that our method provides high rate and reliability in all locations of the user's trajectory with a minimal number of handovers. Simulation results in an outdoor environment based on geometric mmWave channel modeling and real building map data show the superior performance of our proposed solution in achievable instantaneous rate and trajectory rate.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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