2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)最新文献_第5页

A Spectrum Prediction Method for Bursty Frequency Bands 一种突发频带的频谱预测方法

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9420005

Chao Yang, Tao Peng, Peiliang Zuo, Xinyue Wang

{"title":"A Spectrum Prediction Method for Bursty Frequency Bands","authors":"Chao Yang, Tao Peng, Peiliang Zuo, Xinyue Wang","doi":"10.1109/WCNCW49093.2021.9420005","DOIUrl":"https://doi.org/10.1109/WCNCW49093.2021.9420005","url":null,"abstract":"Spectrum prediction is an important technology for spectrum cognition which constitutes the premise of cognitive radio technology. However, most of the existing spectrum prediction methods were proposed for common frequency bands. Based on this philosophy, these methods generally build a mapping model between historical statistical information and future state, and performance of the single model was verified to be limited due to that it cannot match the high burstiness of spectrum. This paper considers the prediction of bursty bands. The previous collected data of 2.4GHz Industrial, Scientific, Medical (ISM) bands is utilized for both feature analysis and method research. Specifically, the deep-reinforcement learning (DRL) method is adopted to address the high-dimensional state and action spaces caused by the classification process using the values of several features. Besides, this paper also employs the upper-limit results of the prediction to refine the classification process, i.e. those groups with better prediction performance using predictors corresponding to other classes will be readjusted. We thus name the proposed method as classification-based deep reinforcement learning (C- DRL). Finally, extensive experiments with the collected real data are conducted to verify the performance of C-DRL. The results suggest that C-DRL significantly outperforms the state-of-the-art algorithms in terms of the prediction performance.","PeriodicalId":262379,"journal":{"name":"2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123045926","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

A Novel Image Retrieval Method for Image Based Localization in Large-Scale Environment 大尺度环境下基于图像定位的图像检索方法

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9420009

Xiliang Yin, Lin Ma, Xuezhi Tan

引用次数: 0

Constant Modulus Waveform Design for Joint Multiuser MIMO Communication and MIMO Radar 联合多用户MIMO通信和MIMO雷达的恒模波形设计

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9420010

Xiang Liu, Tianyao Huang, Yimin Liu, Jie Zhou

引用次数: 3

A Metropolis-Hasting-Sampling Approach for Precoding in Downlink Massive MIMO Systems 下行海量MIMO系统中的一种大都市加速采样预编码方法

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9420013

Qian Deng, Shilong Xie, Chao Lu, Xiaopeng Liang, Qiying Wang, Cong Li, Xuehui Chen

引用次数: 0

A Novel Cognitive Scheme based on Dual-threshold Energy Detection in Satellite Systems 一种基于卫星系统双阈值能量检测的新认知方案

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9419991

Renkai Ren, Haitao Wang, Gengxin Zhang, Chen Zhang

{"title":"A Novel Cognitive Scheme based on Dual-threshold Energy Detection in Satellite Systems","authors":"Renkai Ren, Haitao Wang, Gengxin Zhang, Chen Zhang","doi":"10.1109/WCNCW49093.2021.9419991","DOIUrl":"https://doi.org/10.1109/WCNCW49093.2021.9419991","url":null,"abstract":"With the development of Spatial Information Network (SIN), the number of satellites in space continuously increases. However, the available spectrum resources are scarce. To address the spectrum scarcity, sharing the spectrum between different communication systems is a promising option. In this paper, a novel cognitive satellite scheme with geostationary earth orbit (GEO) and low earth orbit (LEO) broadband systems is studied. First, we present the concept of Cognitive Satellite Positioning (CSP) to describe the LEO satellite motion relative to the ground. Then, due to the uncertainty of space noises and absence of priori knowledge, an optimization algorithm with dual-threshold energy detection becomes a preferred candidate for satellite system, and a soft/hard fusion strategy is adopted so as to achieve a good balance between the traffic load of the dedicated links and the accuracy of Spectrum Sensing(SS). Finally, in conjunction with Radio Environment Mapping (REM) to improve the spatial reliability of SS. Numerical results show that the spectrum recognition scheme similar to the detection performance of cooperative SS only using soft fusion, which has strong robustness under environment with high uncertainty. The work here provides theoretical basis and technical support for the spectrum sharing strategy between GEO and LEO satellite system.","PeriodicalId":262379,"journal":{"name":"2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114495455","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: 0

Task Offloading and Resource Allocation in Heterogeneous Edge Computing Systems 异构边缘计算系统中的任务卸载与资源分配

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9420019

Shilin Li, Yiming Liu, Xiaoqi Qin, Zhi Zhang, Hang Li

{"title":"Task Offloading and Resource Allocation in Heterogeneous Edge Computing Systems","authors":"Shilin Li, Yiming Liu, Xiaoqi Qin, Zhi Zhang, Hang Li","doi":"10.1109/WCNCW49093.2021.9420019","DOIUrl":"https://doi.org/10.1109/WCNCW49093.2021.9420019","url":null,"abstract":"The computing architecture undergoes a trend toward heterogeneity, which introduces a new challenge of task offloading scheduling in multi-server mobile edge computing system. Considering the inherent heterogeneity of task parallelism, this paper investigates the interesting problem of how to exploit the parallelism of task and server by reasonably scheduling task offloading. We jointly optimize task offloading decision and resource allocation so as to minimize system-wide computation overhead in terms of task completion time and energy consumption. Leveraging the structure of the formulated problem, we decompose the original problem into task offloading decision problem and resource allocation problem. To solve for task offloading decision, we propose a heuristic algorithm based on the designed user preference, while we address resource allocation problem by quasiconvex and convex optimization techniques. Simulation results demonstrate that our proposed algorithm outperforms the existed benchmark approach, and that prominent gains can be achieved by conditionally deploying heterogeneous server.","PeriodicalId":262379,"journal":{"name":"2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117141450","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

A Digital Twin Approach for Self-optimization of Mobile Networks 移动网络自优化的数字孪生方法

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9420037

Jianjin Deng, Qingbi Zheng, Guangyi Liu, Jielin Bai, Kaicong Tian, Changhao Sun, Yujie Yan, Yitong Liu

{"title":"A Digital Twin Approach for Self-optimization of Mobile Networks","authors":"Jianjin Deng, Qingbi Zheng, Guangyi Liu, Jielin Bai, Kaicong Tian, Changhao Sun, Yujie Yan, Yitong Liu","doi":"10.1109/WCNCW49093.2021.9420037","DOIUrl":"https://doi.org/10.1109/WCNCW49093.2021.9420037","url":null,"abstract":"Most of the methods in operators' current 5G networks use expert knowledge assisted by machine learning algorithms to generate optimization decisions. However, these methods are inadaptive to the dynamic changes of high-dimensional network states, thus the result is often suboptimal. Reinforcement learning can better cope with high-dimensional network state space and parameter space. However, when applied to real network, challenges arise such as difficult to obtain data samples, time-consuming and risky to explore real networks during model training. To solve these problems, this paper proposes a combined approach of expert knowledge, reinforcement learning and digital twin for the self-optimization of mobile networks. By constructing a digital twin of the current network, the future network state is predicted based on which optimization decisions are generated by expert knowledge and reinforcement learning respectively, and then input into the digital twin. Digital twin simulates their rewards and decides a final action for execution. Simulation results have confirmed that the proposed scheme can achieve higher rewards than either expert knowledge or reinforcement learning, and can avoid negative impact on real network performance. This paper also describes several potential application scenarios for the proposed approach in 6G networks and discusses key issues for future research.","PeriodicalId":262379,"journal":{"name":"2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114343340","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: 23

Reconfigurable Intelligent Surface Assisted Internet-of-Things: MAC Design and Optimization 可重构智能表面辅助物联网:MAC设计与优化

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9420040

Xuelin Cao, Bofu Yang, Hongliang Zhang, Lijun Qian, C. Yuen, Zhu Han

引用次数: 2

SCMA Secure Communication Scheme for Satellite System Based on Distance Spectrum 基于距离频谱的卫星系统SCMA保密通信方案

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9420008

Yiheng Gui, Lidong Zhu, Jixuan Liu

引用次数: 2

Radio Environment Map Construction with Spatially Distributed Sensors 基于空间分布传感器的无线电环境图构建

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9419993

Yi-Qun Xu, Bangning Zhang, Bing Zhao, D. Guo

{"title":"Radio Environment Map Construction with Spatially Distributed Sensors","authors":"Yi-Qun Xu, Bangning Zhang, Bing Zhao, D. Guo","doi":"10.1109/WCNCW49093.2021.9419993","DOIUrl":"https://doi.org/10.1109/WCNCW49093.2021.9419993","url":null,"abstract":"In this paper, we propose an analysis framework based on spectrum data gathered by spatially distributed sensors to construct the radio environment map (REM). To the best of our knowledge, this is the first attempt to leverage the power of Bayesian analysis and Markov chain Monte Carlo (MCMC) in REM. Specifically, a three-stage Bayesian hierarchical model (BHM) is established to imitate the spectrum data generation process under spatially correlated shadow fading. Parameters of BHM are estimated with MCMC algorithm from data collected by the sensor network. Then, we address the space-dimension spectrum inference problem with the aim to interpolate the signal strength where there is no sensor node by composition sampling. We make spatial inference under two sensor location modes (square lattice located sensors and randomly located sensors) and two scenarios (with and without the information about the signal source), respectively. Simulation results demonstrate that although the randomly located sensors mode is suitable for parameter estimation, the inference performance is not better than the square lattice located sensors mode. Quantitative analysis of the inference performance confirms that the effectiveness of our data analysis framework is compelling.","PeriodicalId":262379,"journal":{"name":"2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128466753","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