The highest rate at which information may be reliably sent via a communication link is known as its capacity. In the case of non-Gaussian noise, the capacity of the channel depends on the specific characteristics of the noise, which can cause severe errors and reduce the reliability of communication systems over a fading channel. The Gaussian mixture impulsive noise model (GMINM), which is a more general and flexible non-Gaussian model for impulsive noise, has been compared in this paper with the Middleton Class-A impulsive noise model (MCAINM) in terms of derived channel capacity normalized by channel bandwidth (C/BW) with and without Rayleigh fading (Rf) channels. It also investigated the trade-off between complexity and accuracy in modeling the impulsive noise using two simplified Middleton Class-A impulsive noise models based on derived C/BW. The derived C/BW of these models under various conditions, such as different signal-to-noise ratios and impulsive noise parameters and models, have been performed and evaluated using two different scenarios: the exact method and the semi-analytical method. When the impulsive noise parameters and A are both near 0 in GMINM and MCAINM, respectively, the capacity of the impulsive noise channel is found to be equivalent to that of the Gaussian channel sustainable, as shown by the findings based on Monte-Carlo simulations. We have shown that when the impulsive noise decreases, the capacity increases in all models; however, the capacity of Gaussian noise is higher than the capacity of non-Gaussian noise, which in turn is higher than the capacity of non-Gaussian noise over the Rf channel overall values of SNR in dB. Moreover, multi-channel configuration introduces spatial diversity and multiplexing gains that have been proposed to sustainably optimize the ergodic capacity for the challenge case when the channel state information (CSI) is unknown at the transmitter in non-Gaussian noise over Rf channel. In today's rapidly evolving world, sustainable communication systems play a crucial role in ensuring efficient and responsible utilization of resources. As the demand for wireless communication continues to rise, it becomes imperative to optimize the capacity of communication channels, especially in scenarios involving non-Gaussian noise models and fading channels.
通过通信链路可靠发送信息的最高速率称为其容量。在非高斯噪声的情况下，信道容量取决于噪声的具体特性，噪声会造成严重的误差，降低通信系统在衰减信道上的可靠性。本文比较了高斯混合脉冲噪声模型（GMINM）与米德尔顿 A 类脉冲噪声模型（MCAINM）在有和无瑞利衰落（Rf）信道情况下按信道带宽（C/BW）归一化的推导信道容量。研究还利用两个基于推导 C/BW 的简化 Middleton A 类脉冲噪声模型，研究了脉冲噪声建模的复杂性和准确性之间的权衡。在不同信噪比、脉冲噪声参数和模型等各种条件下，使用精确法和半分析法两种不同方案对这些模型的推导 C/BW 进行了计算和评估。基于蒙特卡洛模拟的结果表明，当 GMINM 和 MCAINM 中的脉冲噪声参数和 A 都分别接近 0 时，脉冲噪声信道的容量与高斯信道的容量相当。我们已经证明，当脉冲噪声减小时，所有模型的容量都会增大；然而，高斯噪声的容量高于非高斯噪声的容量，而非高斯噪声的容量又高于 Rf 信道总体 SNR 值（以 dB 为单位）上的非高斯噪声的容量。此外，多信道配置引入了空间分集和多路复用增益，可持续优化在非高斯噪声 Rf 信道上信道状态信息（CSI）未知情况下发射机的麦格容量。在当今快速发展的世界中，可持续通信系统在确保高效、负责任地利用资源方面发挥着至关重要的作用。随着无线通信需求的不断增长，优化通信信道容量已成为当务之急，尤其是在涉及非高斯噪声模型和衰减信道的情况下。
{"title":"Optimization of capacity in non-Gaussian noise models with and without fading channels for sustainable communication systems","authors":"Ghanim A. Al-Rubaye, H. Alrikabi, H. Hazim","doi":"10.37868/hsd.v5i2.243","DOIUrl":"https://doi.org/10.37868/hsd.v5i2.243","url":null,"abstract":"The highest rate at which information may be reliably sent via a communication link is known as its capacity. In the case of non-Gaussian noise, the capacity of the channel depends on the specific characteristics of the noise, which can cause severe errors and reduce the reliability of communication systems over a fading channel. The Gaussian mixture impulsive noise model (GMINM), which is a more general and flexible non-Gaussian model for impulsive noise, has been compared in this paper with the Middleton Class-A impulsive noise model (MCAINM) in terms of derived channel capacity normalized by channel bandwidth (C/BW) with and without Rayleigh fading (Rf) channels. It also investigated the trade-off between complexity and accuracy in modeling the impulsive noise using two simplified Middleton Class-A impulsive noise models based on derived C/BW. The derived C/BW of these models under various conditions, such as different signal-to-noise ratios and impulsive noise parameters and models, have been performed and evaluated using two different scenarios: the exact method and the semi-analytical method. When the impulsive noise parameters and A are both near 0 in GMINM and MCAINM, respectively, the capacity of the impulsive noise channel is found to be equivalent to that of the Gaussian channel sustainable, as shown by the findings based on Monte-Carlo simulations. We have shown that when the impulsive noise decreases, the capacity increases in all models; however, the capacity of Gaussian noise is higher than the capacity of non-Gaussian noise, which in turn is higher than the capacity of non-Gaussian noise over the Rf channel overall values of SNR in dB. Moreover, multi-channel configuration introduces spatial diversity and multiplexing gains that have been proposed to sustainably optimize the ergodic capacity for the challenge case when the channel state information (CSI) is unknown at the transmitter in non-Gaussian noise over Rf channel. In today's rapidly evolving world, sustainable communication systems play a crucial role in ensuring efficient and responsible utilization of resources. As the demand for wireless communication continues to rise, it becomes imperative to optimize the capacity of communication channels, especially in scenarios involving non-Gaussian noise models and fading channels.","PeriodicalId":505792,"journal":{"name":"Heritage and Sustainable Development","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139341287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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