arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 1606.04849cs.NI
收藏

用于中继辅助设备到设备通信的生物启发资源分配

Bio-Inspired Resource Allocation for Relay-Aided Device-to-Device Communications

Christoforos Vlachos, Hisham Elshaer, Jian Chen, Vasilis Friderikos, Mischa Dohler

摘要:

设备到设备(D2D)通信原理是移动节点之间的直接本地化通信的关键使能器,并且被期望推动过多的新颖多媒体服务。然而,即使它主要由于邻近性和资源重用增益而提供了广泛的能力集合,也必须仔细地控制干扰以最大化共存的蜂窝和D2D用户的可实现速率。这项工作的范围是提供一个干扰感知的实时资源分配(RA)框架中继辅助D2D通信,蜂窝网络。主要目标是通过保证蜂窝和D2D链路的最小速率阈值来最大化整体网络吞吐量。在这个方向上,遗传算法(GA)被证明是强大的和通用的方法,不仅提高了性能,但也降低了新兴的无线网络中的计算复杂性。数值研究强调了与基线RA方法相比的性能增益,特别是在未来5G网络中的高密度场景中。

英文摘要:

The Device-to-Device (D2D) communication principle is a key enabler of direct localized communication between mobile nodes and is expected to propel a plethora of novel multimedia services. However, even though it offers a wide set of capabilities mainly due to the proximity and resource reuse gains, interference must be carefully controlled to maximize the achievable rate for coexisting cellular and D2D users. The scope of this work is to provide an interference-aware real-time resource allocation (RA) framework for relay-aided D2D communications that underlay cellular networks. The main objective is to maximize the overall network throughput by guaranteeing a minimum rate threshold for cellular and D2D links. To this direction, genetic algorithms (GAs) are proven to be powerful and versatile methodologies that account for not only enhanced performance but also reduced computational complexity in emerging wireless networks. Numerical investigations highlight the performance gains compared to baseline RA methods and especially in highly dense scenarios which will be the case in future 5G networks.

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。

↑