基于低复杂度ml辅助的5G HetNets用户关联与资源分配方法

Bharat Agarwal, Mohammed Amine Togou, M. Ruffini, Gabriel-Miro Muntean
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引用次数: 1

摘要

小型蜂窝正在部署在最新的异构网络(HetNet)环境中,这些环境由第五代(5G)网络解决方案支持。它们提高了传统宏细胞网络的性能。在HetNets开始数据传输之前,会触发与特定基站的UA (user association)过程。数据传输过程中还采用了多种资源分配算法。增强网络负载均衡、频谱性能和能源效率都是UA-RA解决方案帮助实现的重要目标。本文的作者先前提出了一种用于解决HetNets中UA-RA问题的元启发式算法,称为性能改进的简化搜索空间模拟退火(PIRS3A)。但是,PIRS3A只适用于用户数量较少的场景,因为它的复杂度会随着用户数量的增加而增加。在本文中,我们通过使用机器学习(ML)来增强PIRS3A以支持大量用户。我们提出了增强的PIRS3A (EPIRS3A),它使用有监督的ML技术将用户分为两类,即需要卸载的用户和不需要卸载的用户。PIRS3A将用于为那些需要卸载以提高服务质量的用户解决UA-RA问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Low Complexity ML-Assisted Multi-knapsack-based Approach for User Association and Resource Allocation in 5G HetNets
Small cells are being deployed in the most recent Heterogeneous Network (HetNet) environments, which are supported by 5th-generation (5G) network solutions. They improve the performance of conventional macro-cell networks. Before data transmission in HetNets begins, a process of user association (UA) with a particular base station (BS) is triggered. Diverse resource allocation (RA) algorithms are also used during data transmission. Enhancing network load balancing, spectrum performance, and energy efficiency are all important goals that UA-RA solutions help to achieve. The authors of this paper previously presented a meta-heuristics algorithm for solving UA-RA problems in HetNets known as Performance-Improved Reduced Search Space Simulated Annealing (PIRS3A). Still, PIRS3A is only suitable for scenarios with low number of users as its complexity increases with the increase in the number of users. In this paper, we enhance the PIRS3A to support a large number of users by using Machine Learning (ML). We propose the Enhanced PIRS3A (EPIRS3A) which uses supervised ML techniques to classify users into two separate classes, i.e., users who need offloading and users who do not. PIRS3A will be used to solve the UA-RA problem for those users who need offloading in order to improve the quality of their services.
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