2021 IEEE/CIC International Conference on Communications in China (ICCC)最新文献_第2页

CNN-Based Distributed Learning for Spectrum Sensing in Cognitive Radio Networks 认知无线电网络中基于cnn的频谱感知分布式学习

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580342

Wenbo Fan, Lu He, Yan Long, Honghao Ju, Shijun Lin

引用次数: 0

Performance Analysis of a Two-Hop Relaying LoRa System 一种两跳中继LoRa系统的性能分析

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580324

Wenyang Xu, Guofa Cai, Yi Fang, Guanrong Chen

引用次数: 4

Cooperative Relaying and Power Control for UAV-Assisted Vehicular Networks with Deep Q-Network 基于深度q -网络的无人机辅助车载网络协同中继与功率控制

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580284

Yuhan Su, M. Liwang, Seyyedali Hosseinalipour, Lianfeng Huang, H. Dai

{"title":"Cooperative Relaying and Power Control for UAV-Assisted Vehicular Networks with Deep Q-Network","authors":"Yuhan Su, M. Liwang, Seyyedali Hosseinalipour, Lianfeng Huang, H. Dai","doi":"10.1109/iccc52777.2021.9580284","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580284","url":null,"abstract":"This paper investigates the usage of unmanned aerial vehicles (UAV s) as relays for data transmission in vehicular networks. We are motivated to address the challenges induced by the lack of direct communication between the vehicles and the infrastructures, such as signal coverage limitations and the existence of obstacles. We consider a scenario in which UAV relays perform cooperative communication in vehicular networks to offer extended coverage to the vehicles, which results in an improvement in the system capacity and reliability. Identifying an efficient UAV-assisted collaboration strategy for vehicular networks is challenging due to the vehicle mobility and the limited power consumption of UAVs. To tackle this problem, we propose a UAV-assisted cooperative relaying scheme based on deep reinforcement learning. To this end, we first determine the optimal transmit powers of a given set of UAV relays to maximize the total throughput of the system. Then, we formulate the UAV-assisted cooperative relaying process as a Markov process and apply a deep Q-network to obtain an effective UAV relay selection strategy. One of the advantages of our solution is that it does not require the knowledge of the vehicle moving trajectories. Through simulations, we demonstrate the effectiveness of our proposed method.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129294453","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: 3

Three-Dimensional Trajectory Design for Multi-User MISO UAV Communications: A Deep Reinforcement Learning Approach 多用户MISO无人机通信三维轨迹设计:一种深度强化学习方法

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580401

Yang Wang, Zhen Gao

{"title":"Three-Dimensional Trajectory Design for Multi-User MISO UAV Communications: A Deep Reinforcement Learning Approach","authors":"Yang Wang, Zhen Gao","doi":"10.1109/iccc52777.2021.9580401","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580401","url":null,"abstract":"In this paper, we investigate a multi-user downlink multiple-input single-output (MISO) unmanned aerial vehicle (UAV) communication system, where a multi-antenna UAV is employed to serve multiple ground terminals. Unlike existing approaches focus only on a simplified two-dimensional scenario, this paper considers a three-dimensional (3D) urban environment, where the UAV's 3D trajectory is designed to minimize data transmission completion time subject to practical throughput and flight movement constraints. Specifically, we propose a deep reinforcement learning (DRL)-based trajectory design for completion time minimization (DRL- TDCTM), which is developed from a deep deterministic policy gradient algorithm. In particular, to represent the state information of UAV and environment, we set an additional information, i.e., the merged pheromone, as a reference of reward which facilitates the algorithm design. By interacting with the external environment in the corresponding Markov decision process, the proposed algorithm can continuously and adaptively learn how to adjust the UAV's movement strategy. Finally, simulation results show the superiority of the proposed DRL- TDCTM algorithm over the conventional baseline methods.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126763550","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: 2

Design of Protograph-based LDPC Codes for DVB-S2 Systems 基于原型的DVB-S2系统LDPC码设计

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580405

Runze Li, Jingyi Zhang, Pingping Chen, Yi Fang

引用次数: 1

Mobility Prediction-Based Wireless Edge Caching Using Deep Reinforcement Learning 基于移动性预测的基于深度强化学习的无线边缘缓存

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580283

Yuekai Cai, Youjia Chen, Ming Ding, Peng Cheng, Jun Li

{"title":"Mobility Prediction-Based Wireless Edge Caching Using Deep Reinforcement Learning","authors":"Yuekai Cai, Youjia Chen, Ming Ding, Peng Cheng, Jun Li","doi":"10.1109/iccc52777.2021.9580283","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580283","url":null,"abstract":"Content caching in the edge of wireless networks is a promising technology to reduce the backhaul traffic of duplicated data transmission. Its key issue lies in the accurate prediction of user requirements. Considering the pervasive movement of users in cellular networks, especially in small-cell networks, we propose a mobility prediction-based content caching replacement strategy in this paper. Note that the impact of unevenly distributed file popularity is much larger in small cells than in macro cells due to their small coverage, where the local file request profile does not match the global one. In more detail, the user location predicted by long short term memory (LSTM) is incorporated into the caching replacement algorithm based on a deep reinforcement learning (DRL) framework. Simulation results show that the mobility prediction brings significant performance improvement in terms of cache hit ratio (CHR) in various movement scenarios, especially for a more regular movement pattern of users. Moreover, the optimal CHR threshold in the proposed algorithm is analytically derived, and the performance impact of learning rate as well as the storage size is also well investigated.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121541678","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

Joint Task Secure Offloading and Resource Allocation for Multi-MEC Server to Improve User QoE 多mec服务器联合任务安全卸载与资源分配提高用户QoE

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580302

Liang Zhang, Min Jia, Jian Wu, Qing Guo, Xuemai Gu

{"title":"Joint Task Secure Offloading and Resource Allocation for Multi-MEC Server to Improve User QoE","authors":"Liang Zhang, Min Jia, Jian Wu, Qing Guo, Xuemai Gu","doi":"10.1109/iccc52777.2021.9580302","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580302","url":null,"abstract":"As a new computing paradigm after cloud computing, Mobile-edge computing (MEC) sinks computing power to the edge of the network, provides data caching and processing functions, and has the characteristics of low latency, high security, and location awareness. Users offloaded computing-intensive tasks to edge servers with stronger processing capabilities to further satisfy their QoE. A joint task offloading and resource allocation strategy was proposed to maximize users’ offloading gain by weighting task execution time and energy consumption. The above optimization problem is modeled as a mixed-integer non-linear programming problem that jointly optimizes task offloading decisions, the mobile users’ uplink transmission power, and edge server computing resource allocation. In a large-scale communication network, it is difficult to optimize the above problems to achieve the optimal solution. Therefore, we decoupled the above problems into the resource allocation problem under the fixed offloading decision and the offloading decision problem under the optimal resource allocation. Simulation results showed that the proposed method could effectively meet the users’ QoE. As the bandwidth compression factor γ and transfer data ${d_{u}}$ increase, the system utility function decreases. Well, as task loading ${C_{u}}$ increases, the system utility function increases.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"15 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113964271","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

Joint UAV Deployment and Energy Transmission Design for Throughput Maximization in IoRT Networks 面向IoRT网络吞吐量最大化的联合无人机部署与能量传输设计

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580339

Jiarong Lu, Ying Wang, Yuanbin Chen, Huaiqi Jia

{"title":"Joint UAV Deployment and Energy Transmission Design for Throughput Maximization in IoRT Networks","authors":"Jiarong Lu, Ying Wang, Yuanbin Chen, Huaiqi Jia","doi":"10.1109/iccc52777.2021.9580339","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580339","url":null,"abstract":"On account of the difficulty of deploying the ground base station (BS) and the limited power consumption of smart devices in Internet of Remote Things (IoRT) networks, it is necessary to construct a system assisted by unmanned aerial vehicles (UAVs) for sustainable communication. This paper aims to study a resource allocation problem in the wireless powered communication network (WPCN) while a UAV serves as an aerial BS. The optimization goal of this paper is to maximize the minimum throughput of ground terminals (GTs) by jointly optimizing the UAV deployment, time resource allocation, and power control. Nevertheless, the optimization problem is nonlinear and non-convex, which is difficult to solve directly. Therefore, it is transformed into two sub-problems, which can be iterated alternately to maximize the minimum throughput of each device in WPCN. The successive convex approximation (SCA) technique and variable substitution are used to transform two non-convex sub-problems into solvable problems. Finally, an iterative algorithm combining two sub-problems is designed to obtain the resource allocation strategies. The numerical results show that the communication resource can be fully shared by adjusting the position of the UAV, and the efficiency and performance of the algorithm are verified.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127654574","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: 2

Delay-aware Offloading for Cooperative NOMA-based Near-and-far MEC Networks 基于协同noma的远近MEC网络延迟感知卸载

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580414

Tianming He, Dawei Wang, Fuhui Zhou, Wei Liang, Xiao Tang, D. Zhai

引用次数: 3

Probabilistic Model Based Caching Strategy for Device-to-Device Communications 基于概率模型的设备间通信缓存策略

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580421

Jianpeng Xu, Rong Chen, Mingzhi Xu, M. Jiang, Xuming Lu

引用次数: 0