基于贪婪异步分布式干扰避免算法的cr - noma Femtocell D2D下行功率分配

Comput. Pub Date : 2023-08-03 DOI:10.3390/computers12080158
Nahla Nurelmadina, R. Saeed, E. Saeid, E. Ali, Maha S. Abdelhaq, R. Alsaqour, N. Alharbe
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引用次数: 1

摘要

本文主要研究了基于认知无线电的非正交多址(CR-NOMA)系统在飞蜂窝环境下的下行功率分配问题,包括设备对设备(D2D)通信。提出的功率分配方案采用贪婪异步分布式干扰避免(GADIA)算法。考虑到基于cr - noma的飞蜂窝D2D系统的独特特性,本研究旨在优化下行传输中的功率分配。利用GADIA算法来减少干扰,有效地优化整个网络的功率分配。本文利用公平性指标提出了一种新的基于公平性约束的下行非正交多址(NOMA)系统功率分配算法。通过大量的仿真,证明了MRF算法在优化系统性能的同时,能有效地保持用户间的公平性。公平性指数被证明可以适应不同的用户数量,提供一个指定的范围和良好的响应性。GADIA算法的实现在网络内的次优频带分配方面显示出令人满意的结果。在MATLAB中评估的数学模型进一步证实了CR-NOMA优于最优功率分配NOMA (OPA)和固定功率分配NOMA (FPA)技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Downlink Power Allocation for CR-NOMA-Based Femtocell D2D Using Greedy Asynchronous Distributed Interference Avoidance Algorithm
This paper focuses on downlink power allocation for a cognitive radio-based non-orthogonal multiple access (CR-NOMA) system in a femtocell environment involving device-to-device (D2D) communication. The proposed power allocation scheme employs the greedy asynchronous distributed interference avoidance (GADIA) algorithm. This research aims to optimize the power allocation in the downlink transmission, considering the unique characteristics of the CR-NOMA-based femtocell D2D system. The GADIA algorithm is utilized to mitigate interference and effectively optimize power allocation across the network. This research uses a fairness index to present a novel fairness-constrained power allocation algorithm for a downlink non-orthogonal multiple access (NOMA) system. Through extensive simulations, the maximum rate under fairness (MRF) algorithm is shown to optimize system performance while maintaining fairness among users effectively. The fairness index is demonstrated to be adaptable to various user counts, offering a specified range with excellent responsiveness. The implementation of the GADIA algorithm exhibits promising results for sub-optimal frequency band distribution within the network. Mathematical models evaluated in MATLAB further confirm the superiority of CR-NOMA over optimum power allocation NOMA (OPA) and fixed power allocation NOMA (FPA) techniques.
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