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arXiv 2103.05061cs.NI
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mmWave中基于机器学习的波束间小区间干扰抑制

Machine Learning-based Inter-Beam Inter-Cell Interference Mitigation in mmWave

Medhat Elsayed, Kevin Shimotakahara, Melike Erol-Kantarci

摘要:

在本文中,我们解决了采用毫米波(mmWave)、波束成形和非正交多址(NOMA)技术的5G网络中的波束间小区间干扰缓解问题。这些技术通过在空间域和功率域上复用用户,在提高网络容量和频谱效率方面发挥着关键作用。此外,来自不同小区的多个波束的覆盖区域可以相交,从而允许用户-小区关联的更大灵活性。然而,覆盖区域的交叉也意味着增加的波束间小区间干扰,即由附近小区形成的波束之间的干扰。因此,联合用户-小区关联和波束间功率分配是减轻波束间、小区间干扰的有希望的解决方案。在本文中,我们考虑了5G毫米波网络,并提出了一种强化学习算法来执行联合用户-小区关联和波束间功率分配,以最大化网络的和速率。所提出的算法相比,一个均匀的功率分配,平均划分每个小区的波束之间的功率。仿真结果表明,在最低和最高流量负载下,网络的总速率分别提高了13-30%。

英文摘要:

In this paper, we address inter-beam inter-cell interference mitigation in 5G networks that employ millimeter-wave (mmWave), beamforming and non-orthogonal multiple access (NOMA) techniques. Those techniques play a key role in improving network capacity and spectral efficiency by multiplexing users on both spatial and power domains. In addition, the coverage area of multiple beams from different cells can intersect, allowing more flexibility in user-cell association. However, the intersection of coverage areas also implies increased inter-beam inter-cell interference, i.e. interference among beams formed by nearby cells. Therefore, joint user-cell association and inter-beam power allocation stand as a promising solution to mitigate inter-beam, inter-cell interference. In this paper, we consider a 5G mmWave network and propose a reinforcement learning algorithm to perform joint user-cell association and inter-beam power allocation to maximize the sum rate of the network. The proposed algorithm is compared to a uniform power allocation that equally divides power among beams per cell. Simulation results present a performance enhancement of 13-30% in network's sum-rate corresponding to the lowest and highest traffic loads, respectively.

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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