Adaptive joint source coding LDPC for energy efficient communication in wireless network on chip

IF 1.6 Q2 ENGINEERING, MULTIDISCIPLINARY International Journal of System Assurance Engineering and Management Pub Date : 2024-05-29 DOI:10.1007/s13198-024-02370-3
Anupama Sindgi, U. B. Mahadevaswamy
{"title":"Adaptive joint source coding LDPC for energy efficient communication in wireless network on chip","authors":"Anupama Sindgi, U. B. Mahadevaswamy","doi":"10.1007/s13198-024-02370-3","DOIUrl":null,"url":null,"abstract":"<p>The Wireless Network-on-Chip (WiNoC) technology has emerged as a promising approach to overcome the growing communication constraints present in multi-core systems. Nevertheless, a significant obstacle is presented by WiNoCs’ steadily rising energy consumption. In this article, we present a novel method for addressing this issue by combining adaptive joint source coding with low-density parity-check (LDPC) encoding. This strategy is presented as an innovative way to handle the problem. Two key modifications are involved in the implementation of our method: firstly, the accurate tuning of the transform coding threshold in compressive sensing to achieve effective data compression, and secondly, the intelligent control of the number of parity checks in LDPC coding to reduce both energy consumption and latency. These adaptive techniques are tailored to meet the signal-to-noise ratio estimates and the dependability standards unique to the application. Our findings demonstrate a substantial accomplishment, with a remarkable 4.2% reduction in power consumption compared to other methods currently in use. This achievement highlights the vast potential for achieving significant energy savings in real-world applications and is a pioneering contribution to the development of energy-efficient communication systems.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of System Assurance Engineering and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13198-024-02370-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

The Wireless Network-on-Chip (WiNoC) technology has emerged as a promising approach to overcome the growing communication constraints present in multi-core systems. Nevertheless, a significant obstacle is presented by WiNoCs’ steadily rising energy consumption. In this article, we present a novel method for addressing this issue by combining adaptive joint source coding with low-density parity-check (LDPC) encoding. This strategy is presented as an innovative way to handle the problem. Two key modifications are involved in the implementation of our method: firstly, the accurate tuning of the transform coding threshold in compressive sensing to achieve effective data compression, and secondly, the intelligent control of the number of parity checks in LDPC coding to reduce both energy consumption and latency. These adaptive techniques are tailored to meet the signal-to-noise ratio estimates and the dependability standards unique to the application. Our findings demonstrate a substantial accomplishment, with a remarkable 4.2% reduction in power consumption compared to other methods currently in use. This achievement highlights the vast potential for achieving significant energy savings in real-world applications and is a pioneering contribution to the development of energy-efficient communication systems.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
自适应联合信源编码 LDPC 用于片上无线网络的高能效通信
无线片上网络(WiNoC)技术已成为克服多核系统中日益增长的通信限制的一种有前途的方法。然而,WiNoC 持续上升的能耗带来了巨大的障碍。在本文中,我们将自适应联合源编码与低密度奇偶校验(LDPC)编码相结合,提出了一种解决这一问题的新方法。这一策略是处理这一问题的创新方法。我们的方法在实施过程中涉及两个关键的修改:首先,在压缩传感中精确调整变换编码阈值,以实现有效的数据压缩;其次,在 LDPC 编码中智能控制奇偶校验的数量,以降低能耗和延迟。这些自适应技术是为满足信噪比估计值和应用特有的可靠性标准而量身定制的。我们的研究结果表明我们取得了巨大成就,与目前使用的其他方法相比,功耗显著降低了 4.2%。这一成就彰显了在实际应用中实现显著节能的巨大潜力,是对高能效通信系统开发的开创性贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
4.30
自引率
10.00%
发文量
252
期刊介绍: This Journal is established with a view to cater to increased awareness for high quality research in the seamless integration of heterogeneous technologies to formulate bankable solutions to the emergent complex engineering problems. Assurance engineering could be thought of as relating to the provision of higher confidence in the reliable and secure implementation of a system’s critical characteristic features through the espousal of a holistic approach by using a wide variety of cross disciplinary tools and techniques. Successful realization of sustainable and dependable products, systems and services involves an extensive adoption of Reliability, Quality, Safety and Risk related procedures for achieving high assurancelevels of performance; also pivotal are the management issues related to risk and uncertainty that govern the practical constraints encountered in their deployment. It is our intention to provide a platform for the modeling and analysis of large engineering systems, among the other aforementioned allied goals of systems assurance engineering, leading to the enforcement of performance enhancement measures. Achieving a fine balance between theory and practice is the primary focus. The Journal only publishes high quality papers that have passed the rigorous peer review procedure of an archival scientific Journal. The aim is an increasing number of submissions, wide circulation and a high impact factor.
期刊最新文献
Vision-based gait analysis to detect Parkinson’s disease using hybrid Harris hawks and Arithmetic optimization algorithm with Random Forest classifier Zero crossing point detection in a distorted sinusoidal signal using random forest classifier FL-XGBTC: federated learning inspired with XG-boost tuned classifier for YouTube spam content detection A generalized product adoption model under random marketing conditions Assessing e-learning platforms in higher education with reference to student satisfaction: a PLS-SEM approach
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1