Diego Barragán-Guerrero, Minh Au, Ghyslain Gagnon, François Gagnon, Pascal Giard
{"title":"Early-detection scheme based on sequential tests for low-latency communications.","authors":"Diego Barragán-Guerrero, Minh Au, Ghyslain Gagnon, François Gagnon, Pascal Giard","doi":"10.1186/s13638-023-02240-9","DOIUrl":"https://doi.org/10.1186/s13638-023-02240-9","url":null,"abstract":"<p><p>We propose an early-detection scheme to reduce communications latency based on sequential tests under finite blocklength regime for a fixed-rate transmission without any feedback channel. The proposed scheme processes observations sequentially to decide in favor of one of the candidate symbols. Such a process stops as soon as a decision rule is satisfied or waits for more samples under a given accuracy. We first provide the optimal achievable latency in additive white Gaussian noise channels for every channel code given a probability of block error. For example, for a rate <math><mrow><mi>R</mi> <mo>=</mo> <mn>0.5</mn></mrow> </math> and a blocklength of 500 symbols, we show that only <math><mrow><mn>63</mn> <mo>%</mo></mrow> </math> of the symbol time is needed to reach an error rate equal to <math><msup><mn>10</mn> <mrow><mo>-</mo> <mn>5</mn></mrow> </msup> </math> . Then, we prove that if short messages can be transmitted in parallel Gaussian channels via a multi-carrier modulation, there exists an optimal low-latency strategy for every code. Next, we show how early detection can be effective with band-limited orthogonal frequency-division multiplexing signals while maintaining a given spectral efficiency by random coding or pre-coding random matrices. Finally, we show how the proposed early-detection scheme is effective in multi-hop systems.</p>","PeriodicalId":12040,"journal":{"name":"EURASIP Journal on Wireless Communications and Networking","volume":"2023 1","pages":"31"},"PeriodicalIF":2.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10030544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9184005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arthur Louchart, Ehsan Tohidi, Philippe Ciblat, David Gesbert, Eva Lagunas, Charly Poulliat
{"title":"Some power allocation algorithms for cognitive uplink satellite systems.","authors":"Arthur Louchart, Ehsan Tohidi, Philippe Ciblat, David Gesbert, Eva Lagunas, Charly Poulliat","doi":"10.1186/s13638-023-02234-7","DOIUrl":"https://doi.org/10.1186/s13638-023-02234-7","url":null,"abstract":"<p><p>Cognitive satellite communication (SatCom) is rapidly emerging as a promising technology to overcome the scarcity of the exclusive licensed band model in order to fulfill the increasing demand for high data rate services. The paper addresses power allocation methods for multi-operator multi-beam uplink satellite communication systems co-existing with a Ka-band terrestrial network, using cognitive radio paradigm. Such a scenario is especially challenging because of (i) the coexisting multiple SatCom operators over the cognitive band need to coordinate the use of their resources under limited inter-operator information exchange, and (ii) nonlinear onboard high power amplifier (HPA) which leads to nonlinear interference between users and beams. In order to tackle the first challenge, we propose distributed power allocation algorithms including the standard Alternate Direction Multiplier Method (ADMM); Regarding the HPA nonlinear impairment, we propose nonlinear-aware power allocation based on Signomial Programming. The proposed solutions outperform state-of-the-art in both cases.</p>","PeriodicalId":12040,"journal":{"name":"EURASIP Journal on Wireless Communications and Networking","volume":"2023 1","pages":"32"},"PeriodicalIF":2.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10078052/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9290229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhongping Dong, Wei Liang, Yan Liang, Weibo Gao, Yi Lu
{"title":"Blockchained supply chain management based on IoT tracking and machine learning.","authors":"Zhongping Dong, Wei Liang, Yan Liang, Weibo Gao, Yi Lu","doi":"10.1186/s13638-022-02209-0","DOIUrl":"https://doi.org/10.1186/s13638-022-02209-0","url":null,"abstract":"<p><p>When it comes to running and managing modern supply chains, 6G Internet of things (IoT) is of utmost importance. To provide IoT with security and automation, blockchain and machine learning are two upper-layer technology that can help. First, we propose to utilize blockchain in modern supply chains to ensure efficient collaboration between all parties. Second, we adopt multi-head attention (MHA)-based gated recurrent unit (GRU) to do inbound logistics task prediction. Finally, numerical results justify that multi-head attention-based GRU model has better fitting efficiency and prediction accuracy than its counterparts.</p>","PeriodicalId":12040,"journal":{"name":"EURASIP Journal on Wireless Communications and Networking","volume":"2022 1","pages":"127"},"PeriodicalIF":2.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9798956/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10480509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}