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缓存辅助协作超密集网络中内容放置与用户关联的交叉熵方法

Cross-Entropy Method for Content Placement and User Association in Cache-Enabled Coordinated Ultra-Dense Networks.

作者信息

Yu Jia, Wang Ye, Gu Shushi, Zhang Qinyu, Chen Siyun, Zhang Yalin

机构信息

Communication Engineering Research Centre, Harbin Institute of Technology (Shenzhen), HIT Campus of University Town of Shenzhen, Shenzhen 518055, China.

Peng Cheng Laboratory, Shenzhen 518055, China.

出版信息

Entropy (Basel). 2019 Jun 8;21(6):576. doi: 10.3390/e21060576.

Abstract

Due to the high splitting-gain of dense small cells, Ultra-Dense Network (UDN) is regarded as a promising networking technology to achieve high data rate and low latency in 5G mobile communications. In UDNs, each User Equipment (UE) may receive signals from multiple Base Stations (BSs), which impose severe interference in the networks and in turn motivates the possibility of using Coordinated Multi-Point (CoMP) transmissions to further enhance network capacity. In CoMP-based Ultra-Dense Networks, a great challenge is to tradeoff between the gain of network throughput and the worsening backhaul latency. Caching popular files on BSs has been identified as a promising method to reduce the backhaul traffic load. In this paper, we investigated content placement strategies and user association algorithms for the proactive caching ultra dense networks. The problem has been formulated to maximize network throughput of cell edge UEs under the consideration of backhaul load, which is a constrained non-convex combinatorial optimization problem. To decrease the complexity, the problem is decomposed into two suboptimal problems. We first solved the content placement algorithm based on the cross-entropy (CE) method to minimize the backhaul load of the network. Then, a user association algorithm based on the CE method was employed to pursue larger network throughput of cell edge UEs. Simulation were conducted to validate the performance of the proposed cross-entropy based schemes in terms of network throughput and backhaul load. The simulation results show that the proposed cross-entropy based content placement scheme significantly outperform the conventional random and Most Popular Content placement schemes, with with 50% and 20% backhaul load decrease respectively. Furthermore, the proposed cross-entropy based user association scheme can achieve 30% and 23% throughput gain, compared with the conventional -best, No-CoMP, and Threshold based user association schemes.

摘要

由于密集小小区具有较高的分裂增益,超密集网络(UDN)被视为一种有前途的网络技术,可在5G移动通信中实现高数据速率和低延迟。在超密集网络中,每个用户设备(UE)可能会从多个基站(BS)接收信号,这会在网络中造成严重干扰,进而促使使用协作多点(CoMP)传输来进一步提高网络容量成为可能。在基于CoMP的超密集网络中,一个巨大的挑战是在网络吞吐量增益和回程延迟恶化之间进行权衡。在基站上缓存流行文件已被认为是一种减少回程流量负载的有前途的方法。在本文中,我们研究了用于主动缓存超密集网络的内容放置策略和用户关联算法。该问题已被表述为在考虑回程负载的情况下最大化小区边缘UE的网络吞吐量,这是一个受约束的非凸组合优化问题。为了降低复杂度,该问题被分解为两个次优问题。我们首先基于交叉熵(CE)方法求解内容放置算法,以最小化网络的回程负载。然后,采用基于CE方法的用户关联算法来追求小区边缘UE更大的网络吞吐量。进行了仿真以验证所提出的基于交叉熵的方案在网络吞吐量和回程负载方面的性能。仿真结果表明,所提出的基于交叉熵的内容放置方案明显优于传统的随机和最流行内容放置方案,回程负载分别降低了50%和20%。此外,与传统的最佳、无CoMP和基于阈值 的用户关联方案相比,所提出的基于交叉熵的用户关联方案可实现30%和23%的吞吐量增益。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/082c/7515065/3e61915e98d7/entropy-21-00576-g001.jpg

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