arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2006.04314eess.SP
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基于机器学习的分布式大规模多输入多输出前导冲突解决

Machine Learning Enabled Preamble Collision Resolution in Distributed Massive MIMO

Jie Ding, Daiming Qu, Pei Liu, Jinho Choi

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31 pages, 7 figures
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摘要：

前导码冲突是影响免授权随机接入（grant-free RA，GFRA）中随机接入用户设备（user equipment，UE）性能的瓶颈。在本文中，通过利用分布式大规模多输入多输出（mMIMO）与机器学习，提出了一种新的基于机器学习的框架解决方案，以解决在GFRA的前导冲突问题。其关键思想是识别并使用冲突RA UE的相邻接入点（AP）而不是所有AP来进行其数据解码，从而可以有效地减轻冲突RA UE之间的相互干扰。为此，我们首先设计了一个定制的深度神经网络（DNN），以实现GFRA中的前导多重性估计，其中还提出了一种能量检测（ED）方法用于性能比较。与估计的前导多重性，然后，我们提出了一个K-means AP聚类算法聚类冲突的RA UE的相邻AP和组织每个AP集群单独解码接收到的数据。仿真结果表明，一个体面的性能的前导码多重性估计的准确性和可靠性方面可以实现由建议DNN，并确认所提出的方案是有效的前导码冲突解决GFRA，能够实现一个接近最佳的性能方面的上行链路可实现的速率每个冲突的RA UE，并提供显着的性能改善传统的计划。

英文摘要：

Preamble collision is a bottleneck that impairs the performance of random access (RA) user equipment (UE) in grant-free RA (GFRA). In this paper, by leveraging distributed massive multiple input multiple output (mMIMO) together with machine learning, a novel machine learning based framework solution is proposed to address the preamble collision problem in GFRA. The key idea is to identify and employ the neighboring access points (APs) of a collided RA UE for its data decoding rather than all the APs, so that the mutual interference among collided RA UEs can be effectively mitigated. To this end, we first design a tailored deep neural network (DNN) to enable the preamble multiplicity estimation in GFRA, where an energy detection (ED) method is also proposed for performance comparison. With the estimated preamble multiplicity, we then propose a K-means AP clustering algorithm to cluster the neighboring APs of collided RA UEs and organize each AP cluster to decode the received data individually. Simulation results show that a decent performance of preamble multiplicity estimation in terms of accuracy and reliability can be achieved by the proposed DNN, and confirm that the proposed schemes are effective in preamble collision resolution in GFRA, which are able to achieve a near-optimal performance in terms of uplink achievable rate per collided RA UE, and offer significant performance improvement over traditional schemes.

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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