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Sensor Intelligence Based Beam Tracking for 5G mmWave Systems: A Practical Approach

机译:基于传感器智能的5G mmWave系统光束跟踪:一种实用方法

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Beamforming is the core principle used for communication in mmWave bands to overcome the excessive pathloss experienced at these bands. Usage of narrow beams results in a large number of beams at transmitter and receiver covering the given range of azimuth and elevation angles. Narrow beams require frequent beam alignment to ensure maximum beam gain of the link, requiring periodic beam search. Beam search complexity is proportional to the number of transmit-receive beam pairs. The larger beam-pair set will inherently delay the full search and hence the delay in finding the best possible beam pair. In this paper, we analyze the beam selection algorithm at user mobile equipment (UE) with device orientation change. We model a propagation channel with device orientation changes using the 3GPP channel model and understand the effect of it. Further, we propose a beam tracking and selection algorithm using orientation sensors to optimize the best beam selection procedure by reducing the beam search space. We show a possible practical implementation of the proposed algorithm on the actual mmWave device. Performance evaluation of the proposed algorithm is done both on the simulator and in the lab setup in various conditions. We illustrate the gains in downlink by throughput and in uplink by transmit power. For example, with this proposed algorithm, at 50º/sec orientation change rate, downlink throughput gain is 33% and the uplink power gain is 6 dB. Similarly, lab results show a throughput gain of 20% in the downlink with typical human usage scenarios.
机译:波束成形是毫米波频段内通信的核心原理,可以克服在这些频段上遇到的过大路径损耗。窄波束的使用导致发射器和接收器处的大量波束覆盖了给定的方位角和仰角范围。窄波束需要频繁的波束对准以确保链路的最大波束增益,从而需要定期进行波束搜索。波束搜索的复杂度与发射-接收波束对的数量成正比。较大的光束对集会固有地延迟整个搜索,因此会延迟寻找最佳可能光束对的时间。在本文中,我们分析了具有设备方向变化的用户移动设备(UE)处的波束选择算法。我们使用3GPP信道模型对具有设备方向更改的传播信道进行建模,并了解其效果。此外,我们提出了一种使用方向传感器的光束跟踪和选择算法,以通过减少光束搜索空间来优化最佳光束选择过程。我们展示了在实际的毫米波设备上提出的算法的可能的实际实现。在各种条件下,在模拟器上和实验室设置中均对所提出算法的性能进行了评估。我们通过吞吐量说明了下行链路中的增益,通过发射功率说明了上行链路中的增益。例如,使用此提出的算法,在50º/ sec方向变化率下,下行链路吞吐量增益为33%,上行链路功率增益为6 dB。同样,实验室结果显示,在典型的人类使用场景下,下行链路的吞吐量提高了20%。

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