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Collaborative Beamforming Aided Fog Radio Access Networks
IEEE Transactions on Vehicular Technology ( IF 6.8 ) Pub Date : 2022-04-19 , DOI: 10.1109/tvt.2022.3168371
Wenbo Zhu , Tuan D. Hoang , Eryk Dutkiewicz , Lajos Hanzo

The success of fog radio access networks (F-RANs) is critically dependent on the potential quality of service (QoS) that they can offer to users in the face of capacity-constrained fronthaul links and limited caches at their remote radio heads (RRHs). In this context, the collaborative beamforming design is very challenging, since it constitutes a large-dimensional nonlinearly constrained optimization problem. The paper develops a new technique for tackling these critical challenges in fog computing. We show that all the associated constraints can be efficiently dealt with maximizing the geometric mean (GM) of the user throughputs (GM-throughput) subject to the affordable total transmit power constraints. To elaborate, the GM-throughput maximization judiciously exploits the fronthaul links and the RRHs’ caches by relying on our novel algorithm, which evaluates low-complexity closed-form expressions in each of its iterations. The problem of F-RAN energy-efficiency is also addressed while maintaining the target throughput. Numerical examples are provided for quantifying the efficiency of the proposed algorithms.

中文翻译:

协作波束成形辅助雾无线接入网络

雾无线接入网络 (F-RAN) 的成功很大程度上取决于它们可以为用户提供的潜在服务质量 (QoS) . 在这种情况下,协同波束成形设计非常具有挑战性,因为它构成了一个大维非线性约束优化问题。本文开发了一种新技术来应对雾计算中的这些关键挑战。我们表明,在可承受的总发射功率约束下,可以有效地处理所有相关的约束,以最大化用户吞吐量(GM-吞吐量)的几何平均值(GM)。详细地说,GM 吞吐量最大化依靠我们的新算法明智地利用了前传链路和 RRH 的缓存,它在每次迭代中评估低复杂度的封闭式表达式。在保持目标吞吐量的同时,还解决了 F-RAN 能效问题。提供了数值例子来量化所提出算法的效率。
更新日期:2022-04-19
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