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An Efficient Radio Access Resource Management Scheme Based on Priority Strategy in Dense mmWave Cellular Networks

机译:基于密集MMWAVE蜂窝网络优先级策略的高效无线电接入资源管理方案

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摘要

In millimeter wave (mmWave) communication systems, beamforming-enabled directional transmission and network densification are usually used to overcome severe signal path loss problem and improve signal coverage quality. The combination of directional transmission and network densification poses a challenge to radio access resource management. The existing work presented an effective solution for dense mmWave wireless local area networks (WLANs). However, this scheme cannot adapt to network expansion when it is applied directly to dense mmWave cellular networks. In addition, there is still room for improvement in terms of energy efficiency and throughput. Therefore, we firstly propose an efficient hierarchical beamforming training (BFT) mechanism to establish directional links, which allows all the small cell base stations (SBSs) to participate in the merging of training frames to adapt to network expansion. Then, we design a BFT information-aided radio access resource allocation algorithm to improve the downlink energy efficiency of the entire mmWave cellular network by reasonably selecting beam directions and optimizing transmission powers and beam widths. Simulation results show that the proposed hierarchical BFT mechanism has the smaller overhead of BFT than the existing BFT mechanism, and the proposed BFT information-aided radio access resource allocation algorithm outperforms the existing corresponding algorithm in terms of average energy efficiency and throughput per link.
机译:在毫米波(MMWAVE)通信系统中,风格化的方向传输和网络致密化通常用于克服严重的信号路径损耗问题,并提高信号覆盖质量。方向传输和网络致密化的组合对无线电接入资源管理构成了挑战。现有的工作为密集MMWAVE无线局域网(WLAN)提出了有效解决方案。然而,当它直接应用于密集的MMWAVE蜂窝网络时,该方案不能适应网络扩展。此外,能源效率和吞吐量方面仍有改进的余地。因此,我们首先提出了一种有效的分层波束形成训练(BFT)机制来建立定向链路,其允许所有小小区基站(SBSS)参与训练帧的合并以适应网络扩展。然后,我们通过合理地选择光束方向和优化传输功率和光束宽度来设计BFT信息辅助无线电接入资源分配算法以提高整个MMWAVE蜂窝网络的下行链路能效。仿真结果表明,所提出的等级BFT机构具有比现有的BFT机制更小的BFT开销,并且所提出的BFT信息辅助无线电接入资源分配算法在平均能量效率和每个链路吞吐量方面优于现有的相应算法。

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