5G-EECC:基于高效协同的设备间通信内容共享策略

Nauman Khan, I. Khan, Jawad Usman Arshed, Mehtab Afzal, Mohammad Masroor Ahmed, Muhammad Arif
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引用次数: 5

摘要

在过去几年中,由于智能应用程序的出现,移动数据流量呈指数级增长。尽管诸如宏蜂窝和飞蜂窝之类的吞吐量增强技术减小了蜂窝的尺寸,但实现起来相对昂贵。移动设备对设备(D2D)通信作为支持下一代5G蜂窝网络中本地服务的多媒体内容日益普及的解决方案而出现。内容共享是D2D通信的突出特性,它有助于减少网络上的卸载流量,提高设备的能源效率,降低回程连接成本。在传统的一对或一对多映射方法中,大量的流量分布在设备之间,导致能耗高。在本文中,我们提出了一种新的节能内容共享方案,称为D2D通信中基于节能协作的内容(EECC)共享策略,该策略根据移动设备的容量和电池寿命在设备之间平等地共享内容。建议的工作包括集群形成、簇头选择和辅助节点选择。此外,我们依赖于协作缓存策略来确保内容的有效分发。模拟结果表明,与使用2g视频文件的最先进技术相比,能耗降低了12.05%。为了评估可伸缩性,我们将文件大小从3 gb增加到4 gb,但在能耗方面的性能保持不变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
5G-EECC: Energy-Efficient Collaboration-Based Content Sharing Strategy in Device-to-Device Communication
In the past few years, mobile data traffic has seen exponential growth due to the emergence of smart applications. Although throughput enhancement techniques such as macro- and femtocells reduce cell size, they are relatively expensive to implement. Mobile device-to-device (D2D) communication has emerged as a solution to support the growing popularity of multimedia content for local service in next-generation 5G cellular networks. Content sharing is the prominent feature, which helps D2D communication in reducing offload traffic on the network, improving the energy efficiency of the device, and reducing backhaul connectivity costs. In traditional mapping approaches such as one to one or one to many, a massive amount of traffic is distributed among the devices resulting in high-energy consumption. In this paper, we propose a novel energy-efficient content sharing scheme called Energy-Efficient Collaboration-based Content (EECC) sharing strategy in D2D communication that shares content equally across devices based on their capacities and battery life under mobility. The proposed work includes cluster formation, cluster head selection, and helper node selection. In addition, we relied on a cooperative caching policy to ensure that content is distributed efficiently. The simulation results indicate a 12.05% reduction in energy compared to the state-of-the-art technique with a 2-gigabyte video file. To evaluate scalability, we increased the file size from 3 to 4 gigabytes, yet the performance in terms of energy consumption remained the same.
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