2021 IEEE International Conference on Communications Workshops (ICC Workshops)最新文献_第7页

Resource Allocation for Secure Rate-Splitting Multiple Access with Adaptive Beamforming 基于自适应波束形成的安全分速多址资源分配

2021 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2021-06-01 DOI: 10.1109/ICCWorkshops50388.2021.9473880

Tenghao Cai, Jia Zhang, Shihao Yan, Lili Meng, Jiande Sun, N. Al-Dhahir

{"title":"Resource Allocation for Secure Rate-Splitting Multiple Access with Adaptive Beamforming","authors":"Tenghao Cai, Jia Zhang, Shihao Yan, Lili Meng, Jiande Sun, N. Al-Dhahir","doi":"10.1109/ICCWorkshops50388.2021.9473880","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473880","url":null,"abstract":"Rate splitting multiple access (RSMA) is promising to achieve high spectral efficiency with a higher flexibility relative to non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). In this paper, a novel RSMA-based secure transmission scheme with artificial noise (AN) and an adaptive beamforming is developed with the aim of maximizing the secrecy sum rate (SSR) of the considered system subject to specific constraints. The joint optimization of the power allocation between useful messages and AN signals, rate splitting between the two legitimate receivers, and the two mastering parameters of the beamforming design is tackled. To solve such a non-convex problem, we first analytically reveal some properties of the solution and then focus on an asymptotic scenario with a sufficiently large number of transmit antennas to derive closed-form expressions for the optimal power allocation coefficients. This enables us to develop an efficient method to identify the optimal rate splitting and beamforming parameters. Our examinations demonstrate that the proposed RSMA-based scheme outperforms two benchmark schemes in terms of achieving a higher SSR and the achievable performance gain is exceptional when the number of transmit antennas is small.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115557180","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: 7

Path Design for NOMA-Enhanced Robots: A Machine Learning Approach with Radio Map noma增强机器人路径设计:基于无线电地图的机器学习方法

2021 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2021-06-01 DOI: 10.1109/ICCWorkshops50388.2021.9473594

Ruikang Zhong, Xiao Liu, Yuanwei Liu, Di Zhang, Yue Chen

{"title":"Path Design for NOMA-Enhanced Robots: A Machine Learning Approach with Radio Map","authors":"Ruikang Zhong, Xiao Liu, Yuanwei Liu, Di Zhang, Yue Chen","doi":"10.1109/ICCWorkshops50388.2021.9473594","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473594","url":null,"abstract":"A communication enabled indoor intelligent robots (IRs) service framework is proposed, where the non-orthogonal multiple access (NOMA) technique is adopted to enhance the data rate and user fairness. Build on the proposed communication model, motions of IRs and the down-link power allocation policy are jointly optimized to maximize the mission efficiency and communication reliability of IRs. In an effort to find the optimal path for IRs from the initial point to their mission destinations, a novel reinforcement learning approach named deep transfer deterministic policy gradient (DT-DPG) algorithm is proposed. In order to save the training time and hardware costs, the radio map is investigated and provided to the agent as a virtual training environment. Our simulation demonstrates that 1) The participation of the NOMA technique effectively improves the communication reliability of IRs; 2) The radio map is qualified to be a virtual training environment, and its statistical channel state information improves training efficiency by about 30%; 3) The proposed algorithm is superior to the deep deterministic policy gradient (DDPG) algorithm in terms of the optimization performance, training time, and anti-local optimum ability.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"344 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124313104","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

[Copyright notice] (版权)

2021 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2021-06-01 DOI: 10.1109/iccworkshops50388.2021.9473687

引用次数: 0

Joint Resource Allocation for Efficient Federated Learning in Internet of Things Supported by Edge Computing 基于边缘计算的物联网高效联邦学习联合资源分配

2021 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2021-06-01 DOI: 10.1109/ICCWorkshops50388.2021.9473734

Jian-ji Ren, Junshuai Sun, Hui Tian, Wanli Ni, Gaofeng Nie, Yingying Wang

{"title":"Joint Resource Allocation for Efficient Federated Learning in Internet of Things Supported by Edge Computing","authors":"Jian-ji Ren, Junshuai Sun, Hui Tian, Wanli Ni, Gaofeng Nie, Yingying Wang","doi":"10.1109/ICCWorkshops50388.2021.9473734","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473734","url":null,"abstract":"Federated learning (FL) and edge computing are both important technologies to support the future Internet of Things (IoT). Despite that the network supported by edge computing has great potential to promote FL, it is more challenging to achieve efficient FL due to more complex resource coupling in it. Focus on this problem, we formulate a problem which minimizes the weighted sum of system cost and learning cost by jointly optimizing bandwidth, computation frequency, transmission power allocation and subcarrier assignment. In order to solve this mixed-integer non-linear problem, we first decouple the bandwidth allocation subproblem from the original problem and obtain a closed-form solution. Further considering the remaining joint optimization problem of computation frequency, transmission power and subcarrier, an iterative algorithm with polynomial time complexity is designed. In an iteration, the latency and computation frequency optimization subproblem and transmission power and subcarrier optimization subproblem are solved using the proposed algorithms in turn. The iterative algorithm is repeated until convergence. Finally, to verify the performance of the algorithm, we compare the proposed algorithm with five baselines. Numerical results show the significant performance gain and the robustness of the proposed algorithm.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122103154","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: 8

Line of Sight Probability Prediction for UAV Communication 无人机通信瞄准线概率预测

2021 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2021-06-01 DOI: 10.1109/ICCWorkshops50388.2021.9473740

Imran Mohammed, I. Collings, S. Hanly

引用次数: 8

Cooperative Spectrum Sensing: A New Approach for Minimum Interference and Maximum Utilisation 协同频谱传感:一种最小干扰和最大利用的新方法

2021 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2021-06-01 DOI: 10.1109/ICCWorkshops50388.2021.9473716

Ogeen H. Toma, M. López-Benítez

引用次数: 5

Cost-Aware Dynamic Bayesian Coalitional Game for Energy Trading among Microgrids 微电网间能源交易的成本感知动态贝叶斯联合博弈

2021 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2021-06-01 DOI: 10.1109/ICCWorkshops50388.2021.9473855

M. Sadeghi, Shahram Mollahasani, M. Erol-Kantarci

引用次数: 2

A Verified Protocol for Secure Autonomous and Cooperative Public Transportation in Smart Cities 智慧城市安全自主协作公共交通的验证协议

2021 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2021-06-01 DOI: 10.1109/ICCWorkshops50388.2021.9473591

H. A. Hamadi, Nida Nasir, C. Yeun, E. Damiani

引用次数: 1

Packet Drop Probability-Optimal Cross-layer Scheduling: Dealing with Curse of Sparsity using Prioritized Experience Replay 丢包概率-最优跨层调度:使用优先体验重放处理稀疏性诅咒

2021 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2021-06-01 DOI: 10.1109/ICCWorkshops50388.2021.9473857

M. Sharma, P. Tan, E. Kurniawan, Sumei Sun

{"title":"Packet Drop Probability-Optimal Cross-layer Scheduling: Dealing with Curse of Sparsity using Prioritized Experience Replay","authors":"M. Sharma, P. Tan, E. Kurniawan, Sumei Sun","doi":"10.1109/ICCWorkshops50388.2021.9473857","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473857","url":null,"abstract":"In this work, we develop a reinforcement learning (RL) based model-free approach to obtain a policy for joint packet scheduling and rate adaptation, such that the packet drop probability (PDP) is minimized. The developed learning scheme yields an online cross-layer scheduling policy which takes into account the randomness in packet arrivals and wireless channels, as well as the state of packet buffers. Inherent difference in the time-scales of packet arrival process and the wireless channel variations leads to sparsity in the observed reward signal. Since an RL agent learns by using the feedback obtained in terms of rewards for its actions, the sample complexity of RL approach increases exponentially due to resulting sparsity. Therefore, a basic RL based approach, e.g., double deep Q-network (DDQN) based RL, results in a policy with negligible performance gain over the state-of-the-art schemes, such as shortest processing time (SPT) based scheduling. In order to alleviate the sparse reward problem, we leverage prioritized experience replay (PER) and develop a DDQN-based learning scheme with PER. We observe through simulations that the policy learned using DDQN-PER approach results in a 3-5% lower PDP, compared to both the basic DDQN based RL and SPT scheme.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129879109","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

Accuracy and Generalization of Deep Learning Applied to Large Scale Attacks 深度学习在大规模攻击中的准确性和泛化

2021 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2021-06-01 DOI: 10.1109/ICCWorkshops50388.2021.9473824

Christopher B. Freas, Dhara Shah, Robert W. Harrison

引用次数: 0