2020 IEEE International Conference on Communications Workshops (ICC Workshops)最新文献_第5页

Distributed Reinforcement Learning for NOMA-Enabled Mobile Edge Computing 支持noma的移动边缘计算的分布式强化学习

2020 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2020-06-01 DOI: 10.1109/ICCWorkshops49005.2020.9145457

Zhong Yang, Yuanwei Liu, Yue Chen

{"title":"Distributed Reinforcement Learning for NOMA-Enabled Mobile Edge Computing","authors":"Zhong Yang, Yuanwei Liu, Yue Chen","doi":"10.1109/ICCWorkshops49005.2020.9145457","DOIUrl":"https://doi.org/10.1109/ICCWorkshops49005.2020.9145457","url":null,"abstract":"A novel non-orthogonal multiple access (NOMA) enabled cache-aided mobile edge computing (MEC) framework is proposed, for minimizing the sum energy consumption. The NOMA strategy enables mobile users to offload computation tasks to the access point (AP) simultaneously, which improves the spectrum efficiency. In this article, the considered resource allocation problem is formulated as a long-term reward maximization problem that involves a joint optimization of task offloading decision, computation resource allocation, and caching decision. To tackle this nontrivial problem, a single-agent Q-learning (SAQ-learning) algorithm is invoked to learn a long-term resource allocation strategy from historical experience. Moreover, a Bayesian learning automata (BLA) based multi-agent Q-learning (MAQ-learning) algorithm is proposed for task offloading decisions. More specifically, a BLA based action select scheme is proposed for the agents in MAQ-learning to select the optimal actions in every state. The proposed BLA based action selection scheme is instantaneously self-correcting, consequently, if the probabilities of two computing models (i.e., local computing and offloading computing) are not equal, the optimal action unveils eventually. Extensive simulations demonstrate that: 1) The proposed cache-aided NOMA MEC framework significantly outperforms the other representative benchmark schemes under various network setups. 2) The effectiveness of the proposed BAL-MAQ-learning algorithm is confirmed from the comparison with the results of conventional reinforcement learning algorithms.","PeriodicalId":254869,"journal":{"name":"2020 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130136142","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

Performance Analysis of Hybrid FSO/RF System with Transmit Aperture Selection 具有发射孔径选择的FSO/RF混合系统性能分析

2020 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2020-06-01 DOI: 10.1109/ICCWorkshops49005.2020.9145410

Shubha Sharma, A. Madhukumar, R. Swaminathan

{"title":"Performance Analysis of Hybrid FSO/RF System with Transmit Aperture Selection","authors":"Shubha Sharma, A. Madhukumar, R. Swaminathan","doi":"10.1109/ICCWorkshops49005.2020.9145410","DOIUrl":"https://doi.org/10.1109/ICCWorkshops49005.2020.9145410","url":null,"abstract":"This paper presents a multiple-input-single-output (MISO) hybrid free-space optics/radio frequency (FSO/RF) system with transmit aperture selection (TAS) scheme. The proposed system consists of a MISO FSO sub-system with N transmit apertures and a single millimeter wave (MMW) RF link. The MISO FSO sub-system is given higher priority to transmit using the selected best link. The aperture with highest gain at the receiver is considered as the best link. The MMW RF link is used when the FSO sub-system is in outage. The FSO links experience Gamma-Gamma atmospheric turbulence induced fading together with pointing errors. To model the pointing errors, the effect of transmit beam waist, detector size, and jitter variance are considered explicitly for FSO link. The MMW RF link experiences κ-μ shadowed fading. The novel expressions of probability density function (PDF) and cumulative distribution function (CDF) of the MISO FSO sub-system using TAS scheme are derived for generalized N transmit apertures. These expressions are further utilized to derive the exact expressions for outage probability and average SER for the proposed system. The results show that proposed hybrid system outperforms the individual MISO FSO system especially for high pointing errors scenario. The performance improvement is observed with each added link for the proposed system. However, the performance improvement with Nth aperture is with less SNR gain compared to (N-1)th aperture.","PeriodicalId":254869,"journal":{"name":"2020 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129087665","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

Joint Optimization of Data Sampling and User Selection for Federated Learning in the Mobile Edge Computing Systems 移动边缘计算系统中联合学习的数据采样和用户选择联合优化

2020 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2020-06-01 DOI: 10.1109/ICCWorkshops49005.2020.9145182

Chenyuan Feng, Yidong Wang, Zhongyuan Zhao, Tony Q. S. Quek, M. Peng

{"title":"Joint Optimization of Data Sampling and User Selection for Federated Learning in the Mobile Edge Computing Systems","authors":"Chenyuan Feng, Yidong Wang, Zhongyuan Zhao, Tony Q. S. Quek, M. Peng","doi":"10.1109/ICCWorkshops49005.2020.9145182","DOIUrl":"https://doi.org/10.1109/ICCWorkshops49005.2020.9145182","url":null,"abstract":"Federated learning is a model-level aggregation learning paradigm, which can generate high quality models without collecting the local private data of users. As a distributed coordination learning method, it can be deployed at the edge devices in mobile edge computing (MEC) systems, and provides an applicable solution of implementing network edge intelligence. However, the performance of federated learning cannot be guaranteed in the MEC systems, since the quality of local training data and wireless channels is not always satisfactory. To tackle with this problem, the joint optimization of data sampling and user selection is studied in this paper. First, to capture the key features of deploying federated learning in the MEC systems, we formulate an optimization problem to minimize the accuracy loss and cost, considering the computation and communication resource constraints. Then, an optimization algorithm is designed to jointly optimize the data sampling and user selection strategies, which can approach the stationary optimal solution efficiently. Finally, the numerical simulation and experiment results are provided to evaluate the performance of our proposed optimization scheme, which show that our proposed algorithm can significantly improve the performance of federated learning in the MEC systems.","PeriodicalId":254869,"journal":{"name":"2020 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130617825","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: 19

Blockchain-Based Multi-Tier Double Auctions for Smart Energy Distribution Grids 基于区块链的智能能源配电网多层双拍卖

2020 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2020-06-01 DOI: 10.1109/ICCWorkshops49005.2020.9145310

Marius Stübs, Wolf Posdorfer, S. Momeni

引用次数: 9

Coverage Probability-Constrained Maximum Throughput in UAV-Aided SWIPT Networks 无人机辅助SWIPT网络覆盖概率约束下的最大吞吐量

2020 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2020-06-01 DOI: 10.1109/ICCWorkshops49005.2020.9145329

Ruihong Jiang, Ke Xiong, Tong Liu, Duohua Wang, Z. Zhong

{"title":"Coverage Probability-Constrained Maximum Throughput in UAV-Aided SWIPT Networks","authors":"Ruihong Jiang, Ke Xiong, Tong Liu, Duohua Wang, Z. Zhong","doi":"10.1109/ICCWorkshops49005.2020.9145329","DOIUrl":"https://doi.org/10.1109/ICCWorkshops49005.2020.9145329","url":null,"abstract":"This paper investigates the coverage provability-constrained throughput in unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) networks, where UAVs are used as aerial base stations and their positions are modeled by the 2-dimension Poisson point process (2-D PPP). The ground users (GUs) decode information as well as harvest energy from the transmitted signals from UAVs. Both power splitting (PS) and time switching (TS) architectures are employed at GUs. By using a stochastic geometry approach, the explicit expressions of the information-energy (I-E) coverage probabilities are derived. To describe the optimal deployment density of UAVs, an optimization problem is formulated to maximize the system throughput subject to the I-E coverage probability constraint. By using Karush-Kuhn-Tucker (KKT) conditions, the closed-form solution is derived. Simulation results demonstrate the correctness of our derived analytical results and show that compared with traditional linear EH model, the nonlinear EH model yields significant difference performance behaviors of the system. Moreover, the nonlinear EH model has a greater impact on EH for the system with TS-enabled GU than that with PS-enabled one. With the increment of the outage threshold, the required density of UAVs should be increased and both the throughput and the energy first increase and then decrease.","PeriodicalId":254869,"journal":{"name":"2020 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132461017","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

Joint Redundant MDS Codes and Cluster Cooperation Based Coded Caching in Fog Radio Access Networks 雾无线接入网中联合冗余MDS码和基于集群协作的编码缓存

2020 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2020-06-01 DOI: 10.1109/ICCWorkshops49005.2020.9145207

Bao Wang, Yanxiang Jiang, F. Zheng, M. Bennis, Xiqi Gao, X. You

{"title":"Joint Redundant MDS Codes and Cluster Cooperation Based Coded Caching in Fog Radio Access Networks","authors":"Bao Wang, Yanxiang Jiang, F. Zheng, M. Bennis, Xiqi Gao, X. You","doi":"10.1109/ICCWorkshops49005.2020.9145207","DOIUrl":"https://doi.org/10.1109/ICCWorkshops49005.2020.9145207","url":null,"abstract":"In this paper, we investigate maximum-distance separable (MDS) codes and cluster based coded caching in fog radio access networks (F-RANs). In order to minimize the fronthaul rate, multicast opportunities need to be constructed at the cloud server. Firstly, a redundant MDS codes based coded placement scheme is proposed to provide redundant coded packets and symmetrical cache contents. The redundant coded packets can be used to construct multicast opportunities for requests on the same file. Furthermore, based on the symmetrical cache contents, we propose a cluster cooperation based coded delivery scheme, which can induce considerable multicast opportunities between any two clusters regardless of whether the requests are on the same file or not. Finally, by utilizing the redundant coded packets and the symmetry of cache contents, a joint redundant MDS codes and cluster cooperation based coded caching policy is proposed to minimize the fronthaul rate. Simulation results show that our proposed policy can provide 30% savings of the fronthaul rate compared to the MDS-based uncoded delivery policy.","PeriodicalId":254869,"journal":{"name":"2020 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128879012","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

Towards Very Low-Power Mobile Terminals through Optimized Computational Offloading 通过优化计算卸载实现极低功耗移动终端

2020 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2020-06-01 DOI: 10.1109/ICCWorkshops49005.2020.9145197

Hergys Rexha, S. Lafond, G. Rigazzi, Jani-Pekka Kainulainen

{"title":"Towards Very Low-Power Mobile Terminals through Optimized Computational Offloading","authors":"Hergys Rexha, S. Lafond, G. Rigazzi, Jani-Pekka Kainulainen","doi":"10.1109/ICCWorkshops49005.2020.9145197","DOIUrl":"https://doi.org/10.1109/ICCWorkshops49005.2020.9145197","url":null,"abstract":"Energy consumption is a major issue for modern embedded mobile computing platforms, and with new technological developments, such as IoT and Edge/Fog computing, the number of connected embedded mobile computing systems is rapidly increasing. Heterogeneous multi-core CPUs seek to improve the performance of these platforms, with a particular focus on energy efficiency. By using different techniques like DVFS, core mapping, and multi-threading, a substantial improvement in the achievable CPU energy efficiency level for Multi-processor system-on-chip(MPSoC) can be observed. However, controlling only the CPU power dissipation has a limited effect on the overall platform energy consumption. Other components of the platform, including memory, disk, and other peripherals, play an important role in the energy efficiency of the platform and need to be taken into account. The availability of different sleep strategies at various levels of the platform makes the energy efficiency issue even more complex. In this paper, we set the view of energy efficiency at the entire platform level and discuss computation offloading as a mechanism to help in reaching the optimal platform energy-efficient state. As an application, we consider object detection performed on several types of images to define when offloading is beneficial to the platform energy efficiency. We survey the energy efficiency of different neural network algorithms in an embedded environment, with the possibility to perform computation offloading, and discuss the obtained results concerning the level of object recognition accuracy provided by different neural networks.","PeriodicalId":254869,"journal":{"name":"2020 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121569170","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

Vehicle Platooning: Sliding Window RLNC for Low Latency and High Resilience 车辆队列:低延迟和高弹性的滑动窗口RLNC

2020 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2020-06-01 DOI: 10.1109/ICCWorkshops49005.2020.9145196

Elif Tasdemir, Christopher Lehmann, David Nophut, Frank Gabriel, F. Fitzek

{"title":"Vehicle Platooning: Sliding Window RLNC for Low Latency and High Resilience","authors":"Elif Tasdemir, Christopher Lehmann, David Nophut, Frank Gabriel, F. Fitzek","doi":"10.1109/ICCWorkshops49005.2020.9145196","DOIUrl":"https://doi.org/10.1109/ICCWorkshops49005.2020.9145196","url":null,"abstract":"Vehicle platooning is one of the challenging communication system due to the mobility of vehicles and wireless channels. Therefore, the channel quality of communication is changing quickly, as a result providing high resilient and low latency vehicle-to-vehicle (V2V) communication is still an open research topic. In order to address the problem, we did a measurement with two transporters driven on a public high way. The distance was between 100-300 m and the speed was in the range of 100-150 km/h. We used IEEE802.11p standard and measured packet reception ratio (PRR) of different data rates. Moreover, we implement systematic sliding window random linear network coding (RLNC) in intermediate nodes in order to transmit packets in platooning for low delay and high resilience. Systematic sliding window RLNC is so far applied only to end-to-end communication. The delay and loss performance of this coding scheme has not been evaluated on multi-hop communication. In this work, we use the measured PRR to mimic the real channel performance as well as apply sliding window coding scheme for a 6 vehicle platooning scenario. Our simulation results show that it is possible to keep the erasure amount below to 15% with 85% probability and overall delay performance below the total transmission times with 75% probability until 4th following vehicle with a constant data rate of 6 MBit/s.","PeriodicalId":254869,"journal":{"name":"2020 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115266847","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: 6

Federated Convolutional Auto-Encoder for Optimal Deployment of UAVs with Visible Light Communications 基于可见光通信的无人机优化部署的联邦卷积自编码器

2020 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2020-06-01 DOI: 10.1109/ICCWorkshops49005.2020.9145090

Yining Wang, Yang Yang, T. Luo

{"title":"Federated Convolutional Auto-Encoder for Optimal Deployment of UAVs with Visible Light Communications","authors":"Yining Wang, Yang Yang, T. Luo","doi":"10.1109/ICCWorkshops49005.2020.9145090","DOIUrl":"https://doi.org/10.1109/ICCWorkshops49005.2020.9145090","url":null,"abstract":"In this paper, the problem of unmanned aerial vehicles (UAV) deployment is investigated for visible light communication (VLC)-enabled UAV networks. Here, UAVs can simul-taneously provide communications and illumination services to ground users. In this model, ambient illumination distribution of the service area must be considered since it can cause interference over the VLC link and affects the illumination requirements of users. This problem is formulated as an optimization problem, which jointly considers UAV deployment, user association, power efficiency, and predictions of the illumination distribution. To solve this problem, we first need to predict illumination distribution to proactively determine the UAV deployment and user association so as to minimize total transmission power of UAVs. To predict the illumination distribution of the entire service area, a federated learning framework based on the machine learning algorithm of convolutional auto-encoder (CAE) is proposed. Compared to the centralized machine learning algorithms that requires complete illumination data for centralized training, the proposed algorithm enables the UAVs to train their local CAE with partial illumination data and cooperatively build a global CAE model that can predict the entire illumination distribution. Using these predictions, the optimal UAV deployment and user association policy that minimizes the total transmission power of UAVs is determined. Simulation results demonstrate that the proposed approach reduces the transmission power of UAVs up to 14.8% and 25.1%, respectively, compared to the local CAE prediction models and the conventional optimal algorithm without illumination distribution predictions.","PeriodicalId":254869,"journal":{"name":"2020 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121186695","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: 4

Channel Statistics-Based Rate Splitting with Spatial Randomness 基于信道统计的空间随机速率分割

2020 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2020-06-01 DOI: 10.1109/ICCWorkshops49005.2020.9145165

Eleni Demarchou, C. Psomas, I. Krikidis

引用次数: 5