Journal of Information and Intelligence最新文献

筛选

英文中文

Nonbinary polar coding with low decoding latency and complexity 具有低解码延迟和低复杂度的非二进制极性编码

Journal of Information and Intelligence Pub Date : 2023-05-01 DOI: 10.1016/j.jiixd.2022.10.002

Peiyao Chen , Baoming Bai , Xiao Ma

{"title":"Nonbinary polar coding with low decoding latency and complexity","authors":"Peiyao Chen , Baoming Bai , Xiao Ma","doi":"10.1016/j.jiixd.2022.10.002","DOIUrl":"https://doi.org/10.1016/j.jiixd.2022.10.002","url":null,"abstract":"<div><p>In this paper, we propose a new class of nonbinary polar codes, where the <em>symbol-level</em> polarization is achieved by using a 2 × 2 <em>q</em>-ary matrix <span><math><mfenced><mrow><mtable><mtr><mtd><mn>1</mn></mtd><mtd><mn>0</mn></mtd></mtr><mtr><mtd><mi>β</mi></mtd><mtd><mn>1</mn></mtd></mtr></mtable></mrow></mfenced></math></span> as the kernel. Under <em>bit-level</em> code construction, some <em>partially-frozen symbols</em> exist, where the frozen bits in these symbols can be used as <em>active-check</em> bits to facilitate the decoder. The encoder/decoder of the proposed codes has a similar structure to the original binary polar codes, admitting an easily configurable and flexible implementation, which is an obvious advantage over the existing nonbinary polar codes based on Reed-Solomon (RS) codes. A low-complexity decoding method is also introduced, in which only more competitive symbols are considered rather than the whole <em>q</em> symbols in the finite field. To support high spectral efficiency, we also present, in addition to the <em>single level coded</em> modulation scheme with field-matched modulation order, a <em>mixed multilevel coded</em> modulation scheme with arbitrary modulation in order to trade off the latency against complexity. Simulation results show that our proposed nonbinary polar codes exhibit comparable performance with the RS4-based polar codes and outperform binary polar codes with low decoding latency, suggesting a potential application for future ultra-reliable and low-latency communications (URLLC).</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"1 1","pages":"Pages 36-53"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49727782","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}

引用次数: 1

Vehicle Computing: Vision and challenges 车辆计算:愿景与挑战

Journal of Information and Intelligence Pub Date : 2023-05-01 DOI: 10.1016/j.jiixd.2022.10.001

Sidi Lu , Weisong Shi

引用次数: 3

A spatiotemporal graph wavelet neural network for traffic flow prediction 交通流预测的时空图小波神经网络

Journal of Information and Intelligence Pub Date : 2023-03-16 DOI: 10.1016/j.jiixd.2023.03.001

Linjie Zhang, Jianfeng Ma

{"title":"A spatiotemporal graph wavelet neural network for traffic flow prediction","authors":"Linjie Zhang, Jianfeng Ma","doi":"10.1016/j.jiixd.2023.03.001","DOIUrl":"10.1016/j.jiixd.2023.03.001","url":null,"abstract":"<div><div>The traffic flow prediction is fast becoming a key instrument in the transportation system, which has achieved impressive performance for traffic management. The graph neural network plays a critical role in the development of the traffic network management. However, it is worthwhile mentioning that the complexity of road networks and traffic conditions makes it unable to obtain sufficient spatiotemporal information. In view of capturing precise environment characteristics, the context could have a precise effect on the prediction results while previous methods rarely took this into account. Besides, the nonlinear characteristics of the graph neural network are hard to quantify with fine granularity and to eliminate overfitting. To stack these challenges, in this paper, we present a spatiotemporal graph wavelet neural network to improve the ability of representations. Specifically, we introduce the wavelet transforms into the deep learning model according to the strong nonlinear optimization ability. Furthermore, we dig the location and time patterns to evaluate the temporal dependence and the spatial proximity correlation. In addition, we introduce a historical context attention mechanism giving fine-grained historical context grade evaluation to ease the phenomenon of over-smoothing. The experimental results on real-world datasets show that our work gets considerable results compared with the baseline and start-of-the-art models. Moreover, our work has better learning performance by employing the connection and interaction of graphs.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 2","pages":"Pages 173-188"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84523151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

首页上一页