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Design and Analysis of Network Behaviors for Optimizing Network Energy Efficiency in 5G Mmwave Systems
IEEE Transactions on Network Science and Engineering ( IF 6.6 ) Pub Date : 2021-04-20 , DOI: 10.1109/tnse.2021.3074362
Jinsong Gui , Xiangwen Dai , Neal N. Xiong

Network behaviors such as power control and transmission duration allocation significantly affect network performance. The existing related works explored network performance improvement by balancing radio resources between backhaul and access links of each millimeter wave (mmWave) small cell in the fifth generation (5G) cellular systems, but they only focused on network throughput instead of network energy efficiency. We address the design and analysis of network behaviors for optimizing network energy efficiency, where each small cell is allowed to adjust not only its individual transmission duration but also its backhaul transmission power, and also each access user is allowed to select an out-band Device-to-Device (D2D) relay if its mmWave access link is blocked. Unlike the existing related works, our scheme allows both backhaul and access powers to be adjusted, which makes the being addressed problem more challenging. Therefore, we formulate it as the Stackelberg game model, which makes the large solution space problem be decomposed into the several interrelated small solution space sub-problems. Thus, the solving difficulty of the challenging problem is reduced. The simulation results demonstrate that the proposed scheme outperforms the comparison algorithms in terms of network energy efficiency while keeping network throughput and convergence speed at acceptable levels.

中文翻译:

用于优化 5G 毫米波系统网络能效的网络行为设计和分析

功率控制和传输持续时间分配等网络行为会显着影响网络性能。现有的相关工作通过平衡第五代(5G)蜂窝系统中每个毫米波(mmWave)小基站的回程和接入链路之间的无线电资源来探索网络性能提升,但他们只关注网络吞吐量而不是网络能效。我们解决了优化网络能效的网络行为设计和分析,其中每个小基站不仅可以调整其单独的传输持续时间,还可以调整其回程传输功率,并且每个接入用户都可以选择带外设备到设备 (D2D) 中继,如果其毫米波访问链接被阻止。不同于现有的相关作品,我们的方案允许调整回程和接入功率,这使得正在解决的问题更具挑战性。因此,我们将其表述为 Stackelberg 博弈模型,将大解空间问题分解为几个相互关联的小解空间子问题。因此,降低了挑战性问题的解决难度。仿真结果表明,所提出的方案在网络能效方面优于比较算法,同时将网络吞吐量和收敛速度保持在可接受的水平。降低了挑战性问题的求解难度。仿真结果表明,所提出的方案在网络能效方面优于比较算法,同时将网络吞吐量和收敛速度保持在可接受的水平。降低了挑战性问题的求解难度。仿真结果表明,所提出的方案在网络能效方面优于比较算法,同时将网络吞吐量和收敛速度保持在可接受的水平。
更新日期:2021-04-20
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