2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops)最新文献_第3页

Deep Reinforcement Learning Based Task Offloading and Resource Allocation for MEC-Enabled IoT Networks 基于深度强化学习的mec支持物联网网络任务卸载和资源分配

2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops) Pub Date : 2023-08-10 DOI: 10.1109/ICCCWorkshops57813.2023.10233832

Ze Wei, Rongxi He, Yunuo Li

{"title":"Deep Reinforcement Learning Based Task Offloading and Resource Allocation for MEC-Enabled IoT Networks","authors":"Ze Wei, Rongxi He, Yunuo Li","doi":"10.1109/ICCCWorkshops57813.2023.10233832","DOIUrl":"https://doi.org/10.1109/ICCCWorkshops57813.2023.10233832","url":null,"abstract":"Mobile Edge Computing (MEC) is emerging as a paradigm for meeting the ever-increasing demands of Internet of Things (IoT) applications in real time. Furthermore, the incorporation of renewable energy harvesting capabilities into base stations or IoT devices has the potential to reduce grid energy consumption. However, it is critical to make an efficient decision for task offloading and resource allocation in order to fully utilize system potential and reduce carbon emissions. In this paper, we propose a carbon-aware MEC framework for a hybrid renewable and grid-energy MEC system. In particular, we aim to optimize the system’s task queue length and carbon emissions in an unpredictable environment with stochastic tasks and renewable energy arrivals. We first formulate the joint task offloading and resource allocation problem by optimizing the total system cost (including task queue length and carbon emissions) and then propose a DDPG-based joint optimization strategy, eventually obtaining an effective resolution through continuous action space learning in the changing environment. Numerical results confirm that our proposal can yield efficient offloading and reduce carbon emissions for the proposed MEC network.","PeriodicalId":201450,"journal":{"name":"2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops)","volume":"300 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123122960","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

Downlink Multiple Access in Optical Mobile Communications: NOMA and OMA Comparison 光移动通信下行链路多址:NOMA与OMA的比较

2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops) Pub Date : 2023-08-10 DOI: 10.1109/ICCCWorkshops57813.2023.10233731

J. Dang, Shicheng Gao, Zaichen Zhang, Liang Wu, Bin Zhu

引用次数: 0

Blind Identification of Channel Code Parameters on Burst Error Channels 突发错误信道中信道码参数的盲识别

2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops) Pub Date : 2023-08-10 DOI: 10.1109/ICCCWorkshops57813.2023.10233756

Guishuai Wang, Chen Yi, Jun Liu, Jihua Zhou, Tao Zhao, Yong Li

引用次数: 0

A Multi-layer Superposition Modulation Scheme to Improve the Data Rate for IoT Communications 一种提高物联网通信数据速率的多层叠加调制方案

2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops) Pub Date : 2023-08-10 DOI: 10.1109/ICCCWorkshops57813.2023.10233794

Jin-yu Luo, Yang Bai, B. Bai, Chao Chen, Wenkun Wen

引用次数: 0

A Cooperative Relay Network for Semantic Communication with Network Coding 基于网络编码的语义通信协同中继网络

2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops) Pub Date : 2023-08-10 DOI: 10.1109/ICCCWorkshops57813.2023.10233769

Qikun Yin, Haotai Liang, Zhicheng Bao, Wannian An

引用次数: 0

Spectrum Requirement Prediction for Future WLAN based IoT Applications 未来基于WLAN的物联网应用的频谱需求预测

2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops) Pub Date : 2023-08-10 DOI: 10.1109/ICCCWorkshops57813.2023.10233830

Chonghe Liu, Shengli Liu, Guanding Yu

{"title":"Spectrum Requirement Prediction for Future WLAN based IoT Applications","authors":"Chonghe Liu, Shengli Liu, Guanding Yu","doi":"10.1109/ICCCWorkshops57813.2023.10233830","DOIUrl":"https://doi.org/10.1109/ICCCWorkshops57813.2023.10233830","url":null,"abstract":"With the explosion of emerging Internet of Things (IoT) applications based on WLAN such as AR/VR and 8K video streaming, the expected growth in performance and data traffic places greater demands on WLAN. The existing 2.4GHz and 5GHz unlicensed spectrum cannot meet the growth in demand and the expected quality of service (QoS) for current applications. Considering the scarcity of spectrum resources, it is more important for the relevant regulators to accurately predict the spectrum requirement over the next five years. This paper analyzes and predicts spectrum requirement for two standards of WLAN, i.e., IEEE 802.11ax and IEEE 802.11ay. Specifically, for IEEE 802.11ax, we first predict the busy hour data traffic of WLAN over the next five years. Then, the WLAN Toolbox is adopted to simulate the data traffic of WLAN in a scenario of the indoor office building. We predict the total bandwidth for an 802.11ax network to reach the required rate and latency metric and come to the conclusion that extra three channels on 6GHz should be made available for unlicensed use, in addition to the unlicensed channels on 5GHz. In terms of IEEE 802.11ay enabled by millimeter wave, we use ns-3 to evaluate its performance with different channels on 60GHz. According to the requirement of these applications enabled by millimeter wave, it is concluded that four channels on 60GHz should be available for IEEE 802.11ay.","PeriodicalId":201450,"journal":{"name":"2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130711241","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

Extending 5G NR V2X Mode 2 to Enable Integrated Sensing and Communication for Vehicular Networks 扩展5G NR V2X模式2，实现车载网络的集成传感和通信

2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops) Pub Date : 2023-08-10 DOI: 10.1109/ICCCWorkshops57813.2023.10233829

Haoyi Liang, Like Wang, Yue Zhang, Hangguan Shan, Zhiguo Shi

{"title":"Extending 5G NR V2X Mode 2 to Enable Integrated Sensing and Communication for Vehicular Networks","authors":"Haoyi Liang, Like Wang, Yue Zhang, Hangguan Shan, Zhiguo Shi","doi":"10.1109/ICCCWorkshops57813.2023.10233829","DOIUrl":"https://doi.org/10.1109/ICCCWorkshops57813.2023.10233829","url":null,"abstract":"In the scenario of the coexistence of millimeter wave (mmWave) radar and communication in vehicular networks, the spectrum resources of the two functions may conflict, causing performance degradation. In this paper, we propose an implementation method of integrated sensing and communication (ISAC) system for vehicular networks by extending the 5G NR V2X Mode 2 protocol. Specifically, the proposed method adopts semi-persistent scheduling (SPS) resource selection to facilitate periodic communication message dissemination and radar detection. To ensure vehicle safety in ISAC vehicular networks, the proposed method dynamically adjusts each vehicle’s radar scanning period and transmit power based on its priority measured by velocity and channel congestion state reported by neighbors, thus enabling high-priority vehicles to occupy spectrum resources preferentially. Simulation results show that the proposed method improves radar and communication performance compared with those based on traditional SPS and time-division-based methods.","PeriodicalId":201450,"journal":{"name":"2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops)","volume":"5 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131438276","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

Few-shot UAV Signal Recognition based on Transfer Meta-Learning 基于迁移元学习的无人机小弹信号识别

2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops) Pub Date : 2023-08-10 DOI: 10.1109/ICCCWorkshops57813.2023.10233743

Hongchen Sun, Zhenyu Na, Liuyang Cheng, Lin Yun, Bin Lin

{"title":"Few-shot UAV Signal Recognition based on Transfer Meta-Learning","authors":"Hongchen Sun, Zhenyu Na, Liuyang Cheng, Lin Yun, Bin Lin","doi":"10.1109/ICCCWorkshops57813.2023.10233743","DOIUrl":"https://doi.org/10.1109/ICCCWorkshops57813.2023.10233743","url":null,"abstract":"In recent years, traditional deep learning (DL) has faced significant technical challenges in Unmanned Aerial Vehicle (UAV) signal recognition due to the lack of large-scale datasets. As a result, it has become difficult to train DL algorithms effectively. In order to improve the generalization ability of the network to unknown data and the accuracy of signal recognition with only a small number of training samples, an Model-agnostic Meta- Learning (MAML) algorithm is proposed in this paper, which can train the network by learning to quickly adapt to different classification tasks, thereby improving its generalization ability and extending to new datasets. Specifically, the deep neural network is pre-trained to reduce the training difficulty during the meta-learning stage. Additionally, both learnable scaling and offset parameters are introduced to transfer the pre-trained network parameters, thereby reducing the amount of network parameters required to learn new classes of signals. In view of the long UAV signals and poor recognition under low signal-to-noise ratio, both spatial and channel attention mechanisms are also added to the feature extraction network to better capture UAV signal features. Experimental results demonstrate that the propose algorithm can achieve a maximum recognition accuracy of 98% with only 5 signal training samples.","PeriodicalId":201450,"journal":{"name":"2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129398200","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

Resource Allocation based on Computing Modeling in Computing Power Network 计算能力网络中基于计算建模的资源分配

2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops) Pub Date : 2023-08-10 DOI: 10.1109/ICCCWorkshops57813.2023.10233759

Haonan Huang, Zheyan Qu

引用次数: 0

RIS-Integrated Near-Space Information Network: A Promising Network Paradigm for URLLC Services ris集成近空间信息网络:一种有前途的URLLC服务网络范例

2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops) Pub Date : 2023-08-10 DOI: 10.1109/ICCCWorkshops57813.2023.10233762

Puguang An, Peng Yang, Xianbin Cao, Chaoqun You, Tony Q. S. Quek

{"title":"RIS-Integrated Near-Space Information Network: A Promising Network Paradigm for URLLC Services","authors":"Puguang An, Peng Yang, Xianbin Cao, Chaoqun You, Tony Q. S. Quek","doi":"10.1109/ICCCWorkshops57813.2023.10233762","DOIUrl":"https://doi.org/10.1109/ICCCWorkshops57813.2023.10233762","url":null,"abstract":"The integration of a near-space information network (NSIN) with the reconfigurable intelligent surface (RIS) is envisioned to significantly enhance the communication performance of future wireless communication systems by proactively altering wireless channels. This paper investigates the problem of deploying a RIS-integrated NSIN to provide energy-efficient, ultra-reliable and low-latency communications (URLLC) services for remote Internet of Things (IoT) devices. We mathematically formulate this problem as a resource optimization problem, aiming to maximize the effective throughput and minimize the system power consumption, subject to URLLC and physical resource constraints. We propose a joint resource allocation algorithm to solve this problem. In this algorithm, we discuss the optimization of phase shifts of RIS reflecting elements, derive an analysis-friendly expression of decoding error probability, and decompose the problem into two-layered optimization problems by analyzing the monotonicity, which makes the formulated problem analytically tractable. Simulation results show that the proposed algorithm is 34.14% more energy-efficient than diverse benchmark algorithms.","PeriodicalId":201450,"journal":{"name":"2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122676966","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