低延迟稳定电力线通信的功率与子载波联合分配

IF 1.4 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Zhixiong Chen, Zhihui Yang, Zeng Dou
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引用次数: 0

摘要

电力线通信(PLC)可以实现低成本的物联网接入,广泛应用于家庭和新能源应用。为了满足远程控制和需求侧响应等低时延业务的需求,提出了一种基于分集分组和信道预测的子载波及其功率联合优化分配算法。首先,考虑信道估计和预测误差的影响,以子载波数据量和传输功率为约束,以最小化多个时隙的总时延为目标,建立了资源分配模型;通过子载波分集分组和改进遗传算法实现单时隙条件下的最优功率分配,然后将低于速率阈值的子载波功率回收分配到预测性能较好的时隙中。最后,通过仿真对算法的性能进行了比较和分析。结果表明,在保证平均速率最优的情况下,该算法可以减小速率波动,提高系统延迟性能和确定性传输能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Joint Allocation of Power and Subcarrier for Low Delay and Stable Power Line Communication

Joint Allocation of Power and Subcarrier for Low Delay and Stable Power Line Communication

Joint Allocation of Power and Subcarrier for Low Delay and Stable Power Line Communication

Joint Allocation of Power and Subcarrier for Low Delay and Stable Power Line Communication

Joint Allocation of Power and Subcarrier for Low Delay and Stable Power Line Communication

Power line communication (PLC) can realize low-cost IOT access and is widely used in home and new energy applications. To meet the requirements of low-latency services such as remote control and demand-side response, a joint optimal allocation algorithm of subcarriers and their power based on diversity grouping and channel prediction is proposed. First, considering the influence of channel estimation and prediction errors, a resource allocation model is established with the constraints of subcarrier data volume and transmission power, and the objective is to minimize the total delay of multiple slots. The optimal power allocation under the condition of a single slot is realized by subcarrier diversity grouping and improved genetic algorithm, and then the subcarrier power below the rate threshold is recycled and allocated to the slot with good prediction performance. Finally, the performance of the algorithm is compared and analyzed by simulation. The results show that the proposed algorithm can reduce the rate fluctuation and improve the system delay performance and deterministic transmission ability under the condition of ensuring the average rate optimization.

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来源期刊
IET Signal Processing
IET Signal Processing 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.90%
发文量
83
审稿时长
9.5 months
期刊介绍: IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf
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