Mhammed Benhayoun, Mouhcine Razi, Anas Mansouri, Ali Ahaitouf
{"title":"用于超可靠低延迟通信的LDPC解码器的嵌入式并行实现","authors":"Mhammed Benhayoun, Mouhcine Razi, Anas Mansouri, Ali Ahaitouf","doi":"10.1155/2023/5573438","DOIUrl":null,"url":null,"abstract":"Ultra-reliable low-latency communications, URLLC, are designed for applications such as self-driving cars and telesurgery requiring a response in milliseconds and are very sensitive to transmission errors. To match the computational complexity of LDPC decoding algorithms to URLLC applications on IoT devices having very limited computational resources, this paper presents a new parallel and low-latency software implementation of the LDPC decoder. First, a decoding algorithm optimization and a compact data structure are proposed. Next, a parallel software implementation is performed on ARM multicore platforms in order to evaluate the latency of the proposed optimization. The synthesis results highlight a reduction in the memory size requirement by 50% and a three-time speedup in terms of processing time when compared to previous software decoder implementations. The reached decoding latency on the parallel processing platform is 150 μs for 288 bits with a bit error ratio of 3.410–9.","PeriodicalId":44894,"journal":{"name":"Applied Computational Intelligence and Soft Computing","volume":"14 4","pages":"0"},"PeriodicalIF":2.4000,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Embedded Parallel Implementation of LDPC Decoder for Ultra-Reliable Low-Latency Communications\",\"authors\":\"Mhammed Benhayoun, Mouhcine Razi, Anas Mansouri, Ali Ahaitouf\",\"doi\":\"10.1155/2023/5573438\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ultra-reliable low-latency communications, URLLC, are designed for applications such as self-driving cars and telesurgery requiring a response in milliseconds and are very sensitive to transmission errors. To match the computational complexity of LDPC decoding algorithms to URLLC applications on IoT devices having very limited computational resources, this paper presents a new parallel and low-latency software implementation of the LDPC decoder. First, a decoding algorithm optimization and a compact data structure are proposed. Next, a parallel software implementation is performed on ARM multicore platforms in order to evaluate the latency of the proposed optimization. The synthesis results highlight a reduction in the memory size requirement by 50% and a three-time speedup in terms of processing time when compared to previous software decoder implementations. The reached decoding latency on the parallel processing platform is 150 μs for 288 bits with a bit error ratio of 3.410–9.\",\"PeriodicalId\":44894,\"journal\":{\"name\":\"Applied Computational Intelligence and Soft Computing\",\"volume\":\"14 4\",\"pages\":\"0\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Computational Intelligence and Soft Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1155/2023/5573438\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Computational Intelligence and Soft Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2023/5573438","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Embedded Parallel Implementation of LDPC Decoder for Ultra-Reliable Low-Latency Communications
Ultra-reliable low-latency communications, URLLC, are designed for applications such as self-driving cars and telesurgery requiring a response in milliseconds and are very sensitive to transmission errors. To match the computational complexity of LDPC decoding algorithms to URLLC applications on IoT devices having very limited computational resources, this paper presents a new parallel and low-latency software implementation of the LDPC decoder. First, a decoding algorithm optimization and a compact data structure are proposed. Next, a parallel software implementation is performed on ARM multicore platforms in order to evaluate the latency of the proposed optimization. The synthesis results highlight a reduction in the memory size requirement by 50% and a three-time speedup in terms of processing time when compared to previous software decoder implementations. The reached decoding latency on the parallel processing platform is 150 μs for 288 bits with a bit error ratio of 3.410–9.
期刊介绍:
Applied Computational Intelligence and Soft Computing will focus on the disciplines of computer science, engineering, and mathematics. The scope of the journal includes developing applications related to all aspects of natural and social sciences by employing the technologies of computational intelligence and soft computing. The new applications of using computational intelligence and soft computing are still in development. Although computational intelligence and soft computing are established fields, the new applications of using computational intelligence and soft computing can be regarded as an emerging field, which is the focus of this journal.