Error Rate Analysis of Intelligent Reflecting Surfaces Aided Non-Orthogonal Multiple Access System

IF 2 4区计算机科学 Q2 Computer Science

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI:10.32604/iasc.2022.022586

A. Vasuki, V. Ponnusamy

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引用次数: 1

Abstract

A good wireless device in a system needs high spectral efficiency. NonOrthogonal Multiple Access (NOMA) is a technique used to enhance spectral efficiency, thereby allowing users to share information at the same time and same frequency. The information of the user is super-positioned either in the power or code domain. However, interference cancellation in NOMA aided system is challenging as it determines the reliability of the system in terms of Bit Error Rate (BER). BER is an essential performance parameter for any wireless network. Intelligent Reflecting Surfaces (IRS) enhances the BER of the users by controlling the electromagnetic wave propagation of a given channel. IRS is able to boost the Signal to Noise Ratio (SNR) at the receiver by introducing a phase shift in the incoming signal utilizing cost-effective reflecting materials. This paper evaluates users’ error rate performance by utilizing IRS in NOMA. The error probability expression of users is derived under Rayleigh and Rician fading channel. The accuracy of derived analytical expressions is then validated via simulations. Impact of power allocation factor, coherent and random phase shifting of IRS is evaluated for the proposed IRS-NOMA system.

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智能反射面辅助非正交多址系统错误率分析

系统中一个好的无线设备需要高的频谱效率。非正交多址(NOMA)是一种用于提高频谱效率的技术，从而允许用户在同一时间和同一频率共享信息。用户信息被叠加在功率域或代码域。然而，在NOMA辅助系统中，干扰消除是一个挑战，因为它决定了系统的误码率(BER)的可靠性。误码率是任何无线网络的基本性能参数。智能反射面(IRS)通过控制给定信道的电磁波传播来提高用户的误码率。IRS能够通过在输入信号中引入相移来提高接收机的信噪比(SNR)，利用经济高效的反射材料。本文利用IRS在NOMA中对用户错误率性能进行了评价。推导了用户在瑞利和瑞利衰落信道下的错误概率表达式。然后通过仿真验证了推导出的解析表达式的准确性。分析了功率分配因子、相干相移和随机相移对IRS- noma系统的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Intelligent Automation and Soft Computing 工程技术-计算机：人工智能

CiteScore

3.50

自引率

10.00%

发文量

429

审稿时长

10.8 months

期刊介绍： An International Journal seeks to provide a common forum for the dissemination of accurate results about the world of intelligent automation, artificial intelligence, computer science, control, intelligent data science, modeling and systems engineering. It is intended that the articles published in the journal will encompass both the short and the long term effects of soft computing and other related fields such as robotics, control, computer, vision, speech recognition, pattern recognition, data mining, big data, data analytics, machine intelligence, cyber security and deep learning. It further hopes it will address the existing and emerging relationships between automation, systems engineering, system of systems engineering and soft computing. The journal will publish original and survey papers on artificial intelligence, intelligent automation and computer engineering with an emphasis on current and potential applications of soft computing. It will have a broad interest in all engineering disciplines, computer science, and related technological fields such as medicine, biology operations research, technology management, agriculture and information technology.