Power allocation method based on modified social network search algorithm

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Intelligence Pub Date : 2024-10-05 DOI:10.1007/s10489-024-05804-4
Hongyuan Gao, Huishuang Li, Yun Lin, Jingya Ma
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Abstract

With the increase of communication devices and demands, the problems of high power consumption, tight spectrum resources, and low energy efficiency in the two-layer heterogeneous network are the popular topics, which need to be solved urgently. For the purpose of solving these problems in a two-layer heterogeneous network consisting of femtocell base stations in randomly distributed a macrocell base station, which can also be called the Macrocell/Femtocell two-layer heterogeneous network, the hierarchical clustering algorithm is firstly used to cluster femtocell base stations in accordance with a distance threshold, the spectrum partitioning mechanism and non-orthogonal multiple access technique are combined to obtain spectrum allocation schemes for different users. Then, the modified social network search algorithm is used to simulate the power allocation problem in the two-layer heterogeneous network with system energy efficiency as the objective function. By comparing with the previous algorithms, the proposed algorithm’s superior performance is verified on the test functions. The results show that the proposed method can effectively improve spectrum utilization and reduce interference. The modified social network search algorithm is more robust and widely applicable regarding energy and computational efficiency.

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基于改进社交网络搜索算法的电力分配方法
随着通信设备和需求的增加,两层异构网络中的高功耗、频谱资源紧张、低能效等问题成为亟待解决的热门话题。为了解决由微微蜂窝基站随机分布在宏蜂窝基站组成的双层异构网络(也可称为宏蜂窝/微微蜂窝双层异构网络)中的这些问题,首先采用分层聚类算法将微微蜂窝基站按照距离阈值聚类,结合频谱划分机制和非正交多址技术,得到不同用户的频谱分配方案。然后,以系统能效为目标函数,利用改进的社交网络搜索算法模拟双层异构网络中的功率分配问题。通过与以往算法的比较,验证了所提算法在测试函数上的优越性能。结果表明,所提出的方法能有效提高频谱利用率并减少干扰。改进后的社交网络搜索算法在能量和计算效率方面更加稳健,适用范围更广。
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来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
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