Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118819
R. Kataoka, Masahiro Takigawa, T. Ohseki, Taishi Watanabe, Y. Amano
In Japan, the 5th generation mobile communication system (5G) became commercially available in 2020. The millimeter wave bands such as 28GHz is being used to achieve the peak rate of 10 [Gbps] or higher targeted for 5G. In the late 2020s, low latency and high-capacity data transmission over both the up and down links will become important. This is because 5G will be utilized in the late 2020s, and telemedicine and teleoperation using 4K/8K and other high-definition video transmission will become widespread. In this study, we propose a relaying method that converts frequency multiplexing into spatial multiplexing during relaying, with the goal of achieving low latency and high capacity relaying communications. The user equipment, base stations, and relay stations have different conditions in terms of transmission power and number of antennas. Therefore, the proposed method achieves high capacity by frequency multiplexing in the link where the number of antennas is limited. In addition, the proposed method uses spatial multiplexing to achieve high capacity while suppressing the increase in resource usage in the link where multiple antennas are available. The 39 GHz band, which has more frequency resources than the existing 5G bands, is used for the evaluation in the link of frequency multiplexing. Then, the 28 GHz band, which is used commercially in 5G, is used for the evaluation in the link of spatial multiplexing. For low latency relaying, analog circuits are used during the relaying process to convert between frequency-multiplexed and space-multiplexed signals without modulation and demodulation, while maintaining the number of multiplexes. In this paper, simulation evaluations show that the proposed method improves the communication distance where the throughput exceeds 4 [Gbps] to 6.5 times that of 39 GHz band 5G communications without relaying, and to 1.3 times that of RF repeaters in conventional relaying methods that use the 39 GHz band both before and after relaying, indicating that the uplink communication distance can be extended.
{"title":"Basic Performance Evaluation of Low Latency and High Capacity Relay Method in Millimeter-Wave Bands","authors":"R. Kataoka, Masahiro Takigawa, T. Ohseki, Taishi Watanabe, Y. Amano","doi":"10.1109/WCNC55385.2023.10118819","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118819","url":null,"abstract":"In Japan, the 5th generation mobile communication system (5G) became commercially available in 2020. The millimeter wave bands such as 28GHz is being used to achieve the peak rate of 10 [Gbps] or higher targeted for 5G. In the late 2020s, low latency and high-capacity data transmission over both the up and down links will become important. This is because 5G will be utilized in the late 2020s, and telemedicine and teleoperation using 4K/8K and other high-definition video transmission will become widespread. In this study, we propose a relaying method that converts frequency multiplexing into spatial multiplexing during relaying, with the goal of achieving low latency and high capacity relaying communications. The user equipment, base stations, and relay stations have different conditions in terms of transmission power and number of antennas. Therefore, the proposed method achieves high capacity by frequency multiplexing in the link where the number of antennas is limited. In addition, the proposed method uses spatial multiplexing to achieve high capacity while suppressing the increase in resource usage in the link where multiple antennas are available. The 39 GHz band, which has more frequency resources than the existing 5G bands, is used for the evaluation in the link of frequency multiplexing. Then, the 28 GHz band, which is used commercially in 5G, is used for the evaluation in the link of spatial multiplexing. For low latency relaying, analog circuits are used during the relaying process to convert between frequency-multiplexed and space-multiplexed signals without modulation and demodulation, while maintaining the number of multiplexes. In this paper, simulation evaluations show that the proposed method improves the communication distance where the throughput exceeds 4 [Gbps] to 6.5 times that of 39 GHz band 5G communications without relaying, and to 1.3 times that of RF repeaters in conventional relaying methods that use the 39 GHz band both before and after relaying, indicating that the uplink communication distance can be extended.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126594658","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118919
Selma Yahia, Yassine Meraihi, Tu Dac Ho, Hossien B. Eldeeb
The reliability of vehicle-to-vehicle (V2V) Visible Light Communication (VLC) systems is affected by several factors, such as car mobility and optics system design. Therefore, this paper focuses on the cars’ relative positions and the design of the optics on the receiving end. Instead of using the rectangle detector, commonly used in the literature, this paper proposes using the polar detector for V2V-VLC systems. We introduce using an imaging receiver with different kinds of optical lenses, such as Fresnel and Aspherical lenses to improve the performance of a V2V-VLC system. We perform a channel modeling study using the non-sequential ray-tracing approach, considering the possibility of horizontal and vertical movement between vehicles. A comprehensive performance comparison of these lenses assumes different vehicle positions on the road. We further investigate the impact of receiver type and lateral shift on the performance of the considered systems. The obtained results demonstrated that with a carefully chosen system and lens parameters, an enhancement of up to 7 dB in total received power could be achieved compared to the case without the lens.
{"title":"Performance Enhancement of Vehicular VLC Using Spherical Detector and Efficient Lens Design","authors":"Selma Yahia, Yassine Meraihi, Tu Dac Ho, Hossien B. Eldeeb","doi":"10.1109/WCNC55385.2023.10118919","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118919","url":null,"abstract":"The reliability of vehicle-to-vehicle (V2V) Visible Light Communication (VLC) systems is affected by several factors, such as car mobility and optics system design. Therefore, this paper focuses on the cars’ relative positions and the design of the optics on the receiving end. Instead of using the rectangle detector, commonly used in the literature, this paper proposes using the polar detector for V2V-VLC systems. We introduce using an imaging receiver with different kinds of optical lenses, such as Fresnel and Aspherical lenses to improve the performance of a V2V-VLC system. We perform a channel modeling study using the non-sequential ray-tracing approach, considering the possibility of horizontal and vertical movement between vehicles. A comprehensive performance comparison of these lenses assumes different vehicle positions on the road. We further investigate the impact of receiver type and lateral shift on the performance of the considered systems. The obtained results demonstrated that with a carefully chosen system and lens parameters, an enhancement of up to 7 dB in total received power could be achieved compared to the case without the lens.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125858686","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10119063
H. He, Yang Xu, Jia Liu, Hiroki Takakura, Zhao-Zhe Li, N. Shiratori
In the era of big data, the unprecedented growth of data has spawned the commercial application of data trading markets in the Internet of Vehicles (IoV), while also posing challenges to their economic feasibility. In this paper, we propose a data-energy trading architecture in IoV consisting of a market operator, electric vehicles (EVs), and roadside units (RSUs), where RSUs exchange energy for data collected by EVs, and the market operator solves the data/energy allocation problem to maximize social welfare. However, due to the information asymmetry and fragmentation in the market, it is difficult to determine the optimal data and energy trading amount. To this end, we design an iterative double-sided auction (IDA) mechanism to regulate the interactive behaviors among the trading entities, where the market operator gathers local information from RSUs and EVs, and gradually adjusts the submitted bids of two sides to reach the desired payment and reward rules. The proposed IDA-based data-energy trading algorithm is convergent and satisfies the economic properties of efficiency, incentive compatibility, individual rationality, and budget balance. Numerical results demonstrate the performance of the proposed IDA-based data-energy trading architecture in IoV.
{"title":"Double-Sided Auction based Data-Energy Trading Architecture in Internet of Vehicles","authors":"H. He, Yang Xu, Jia Liu, Hiroki Takakura, Zhao-Zhe Li, N. Shiratori","doi":"10.1109/WCNC55385.2023.10119063","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10119063","url":null,"abstract":"In the era of big data, the unprecedented growth of data has spawned the commercial application of data trading markets in the Internet of Vehicles (IoV), while also posing challenges to their economic feasibility. In this paper, we propose a data-energy trading architecture in IoV consisting of a market operator, electric vehicles (EVs), and roadside units (RSUs), where RSUs exchange energy for data collected by EVs, and the market operator solves the data/energy allocation problem to maximize social welfare. However, due to the information asymmetry and fragmentation in the market, it is difficult to determine the optimal data and energy trading amount. To this end, we design an iterative double-sided auction (IDA) mechanism to regulate the interactive behaviors among the trading entities, where the market operator gathers local information from RSUs and EVs, and gradually adjusts the submitted bids of two sides to reach the desired payment and reward rules. The proposed IDA-based data-energy trading algorithm is convergent and satisfies the economic properties of efficiency, incentive compatibility, individual rationality, and budget balance. Numerical results demonstrate the performance of the proposed IDA-based data-energy trading architecture in IoV.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126096827","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118764
Hongzhi Chen, Lifu Liu, Songyan Xue, Y. Sun, Jiyong Pang
Millimeter wave (mmWave) systems rely on predefined codebooks for both initial access and data transmission. To compensate the high pathloss of mmWave signal, base station(BS) and user equipment(UE) to be equipped with large antenna arrays which make those codebooks consist of a large number of candidate narrow beams. Both the BS and UE needs to search for a optimal beam from their codebooks that provides maximum received power, such procedure may cause huge beam training overhead. Besides, codebook based beam management limits the maximum beamforming gain as it is bounded by the spatial granularity of the codewords. To overcome these limitations, in the paper, we design a deep learning (DL) based beam training method with partial codebook sweeping. Unlike the existing works using machine learning (ML) or DL to predict the best beam ID from the codebook, the DL model directly outputs the beamforming weights of the analog phase shifters which maximize certain metric, e.g. received signal to noise ratio (SNR). The neural network (NN) is trained offline using simulated environments according to the 3GPP channel models and is then deployed online to predict the optimal beamforming vector with partial beams sensing. Simulation results show that our proposed model outperforms the standard DFT-based codebook with significantly reduced beam training overhead, and enhance the beamforming gain which reflects on the achievable rates.
{"title":"Active Sensing for Beam Management: A Deep-Learning Approach","authors":"Hongzhi Chen, Lifu Liu, Songyan Xue, Y. Sun, Jiyong Pang","doi":"10.1109/WCNC55385.2023.10118764","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118764","url":null,"abstract":"Millimeter wave (mmWave) systems rely on predefined codebooks for both initial access and data transmission. To compensate the high pathloss of mmWave signal, base station(BS) and user equipment(UE) to be equipped with large antenna arrays which make those codebooks consist of a large number of candidate narrow beams. Both the BS and UE needs to search for a optimal beam from their codebooks that provides maximum received power, such procedure may cause huge beam training overhead. Besides, codebook based beam management limits the maximum beamforming gain as it is bounded by the spatial granularity of the codewords. To overcome these limitations, in the paper, we design a deep learning (DL) based beam training method with partial codebook sweeping. Unlike the existing works using machine learning (ML) or DL to predict the best beam ID from the codebook, the DL model directly outputs the beamforming weights of the analog phase shifters which maximize certain metric, e.g. received signal to noise ratio (SNR). The neural network (NN) is trained offline using simulated environments according to the 3GPP channel models and is then deployed online to predict the optimal beamforming vector with partial beams sensing. Simulation results show that our proposed model outperforms the standard DFT-based codebook with significantly reduced beam training overhead, and enhance the beamforming gain which reflects on the achievable rates.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120948073","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118590
Lorena Chinchilla-Romero, Jonathan Prados-Garzon, P. Muñoz, P. Ameigeiras, J. Ramos-Muñoz
Multi-Wireless Access Technology (WAT) Radio Access Networks (RANs) are becoming a key enabler in 5G and beyond networks due to the public spectrum scarcity, the level of signal confinement and security offered by some wireless technologies (e.g., Light Fidelity (Li-Fi)), and the reduction of the deployment and operational costs. For instance, Wireless Fidelity (Wi-Fi) technology is cheaper and easier to manage than 5G, and leveraging their already deployed infrastructures contributes to capital expenditures saving. Developing autonomous radio resource provisioning (RRP) solutions is fundamental to cost-effectively achieve the zero-touch management in private 5G networks while fulfilling the service requirements. However, modelling the Key Performance Indicators of the radio interface in 5G and beyond is a complex task that requires high-domain knowledge. Furthermore, the resulting models, as well as solving the respective RRP optimization problem using exact methods usually offer a high computational complexity, especially in multi-WAT scenarios. In order to cope with these issues, in this work, we propose an initial design of a Deep Reinforcement Learning-assisted solution for the RRP in a multi-WAT private 5G network. Furthermore, we contex-tualize the solution in the Open RAN architecture framework. A simulation-based proof-of-concept validates the proposal’s proper design and operation considering a realistic private 5G network scenario.
{"title":"Autonomous Radio Resource Provisioning in Multi-WAT Private 5G RANs based on DRL","authors":"Lorena Chinchilla-Romero, Jonathan Prados-Garzon, P. Muñoz, P. Ameigeiras, J. Ramos-Muñoz","doi":"10.1109/WCNC55385.2023.10118590","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118590","url":null,"abstract":"Multi-Wireless Access Technology (WAT) Radio Access Networks (RANs) are becoming a key enabler in 5G and beyond networks due to the public spectrum scarcity, the level of signal confinement and security offered by some wireless technologies (e.g., Light Fidelity (Li-Fi)), and the reduction of the deployment and operational costs. For instance, Wireless Fidelity (Wi-Fi) technology is cheaper and easier to manage than 5G, and leveraging their already deployed infrastructures contributes to capital expenditures saving. Developing autonomous radio resource provisioning (RRP) solutions is fundamental to cost-effectively achieve the zero-touch management in private 5G networks while fulfilling the service requirements. However, modelling the Key Performance Indicators of the radio interface in 5G and beyond is a complex task that requires high-domain knowledge. Furthermore, the resulting models, as well as solving the respective RRP optimization problem using exact methods usually offer a high computational complexity, especially in multi-WAT scenarios. In order to cope with these issues, in this work, we propose an initial design of a Deep Reinforcement Learning-assisted solution for the RRP in a multi-WAT private 5G network. Furthermore, we contex-tualize the solution in the Open RAN architecture framework. A simulation-based proof-of-concept validates the proposal’s proper design and operation considering a realistic private 5G network scenario.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116547152","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}
With the deployment of 5G and large-scale Internet of Things (IoT), the equipment identification and authentication scheme based on RF fingerprint shows unique advantages in terms of lightweight and uniqueness. However, traditional RF fingerprint identification scheme based on machine learning has the disadvantages of high computational complexity and low accuracy. Meanwhile, this scheme requires large-scale labeled datasets to realize network learning, and due to the nonlinearity of the cascade, we can not well understand the properties and optimal configurations of these networks. To solve above problems, in this paper, we propose an RF fingerprint identification method based on wavelet scattering network in the small-scale dataset. Specifically, in this method, we first design a hybrid network model of wavelet scattering network combined with deep residual network (Resnet18). Then, since one of the main problems of RF fingerprinting is the diversity of signal information at different time scales, we choose to use the construction of scattering network based on wavelet basis to complete the accurate feature decomposition of the nonlinear features of RF fingerprint. These features are stable against deformations and retain high frequency information for identification. Finally, we can use the obtained detailed features to realize the accurate identification of RF radiation source equipments. The experimental results show that our scheme can better suppress the interference of noise in the signal, improve the feature representation ability, and it can obtain higher identification accuracy than other comparison schemes.
{"title":"Deep Radio Frequency Fingerprinting Based on Wavelet Scattering Network","authors":"Jing Ma, Pinyi Ren, Tiantian Zhang, Zhanyi Ren, Dongyang Xu","doi":"10.1109/WCNC55385.2023.10119009","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10119009","url":null,"abstract":"With the deployment of 5G and large-scale Internet of Things (IoT), the equipment identification and authentication scheme based on RF fingerprint shows unique advantages in terms of lightweight and uniqueness. However, traditional RF fingerprint identification scheme based on machine learning has the disadvantages of high computational complexity and low accuracy. Meanwhile, this scheme requires large-scale labeled datasets to realize network learning, and due to the nonlinearity of the cascade, we can not well understand the properties and optimal configurations of these networks. To solve above problems, in this paper, we propose an RF fingerprint identification method based on wavelet scattering network in the small-scale dataset. Specifically, in this method, we first design a hybrid network model of wavelet scattering network combined with deep residual network (Resnet18). Then, since one of the main problems of RF fingerprinting is the diversity of signal information at different time scales, we choose to use the construction of scattering network based on wavelet basis to complete the accurate feature decomposition of the nonlinear features of RF fingerprint. These features are stable against deformations and retain high frequency information for identification. Finally, we can use the obtained detailed features to realize the accurate identification of RF radiation source equipments. The experimental results show that our scheme can better suppress the interference of noise in the signal, improve the feature representation ability, and it can obtain higher identification accuracy than other comparison schemes.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116694128","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118750
Jianwen Shang, Wenbin Liu, Yongjian Yang
Mobile Edge Computing (MEC) is a promising distributed computing paradigm, where the service providers deploy their computing power at the communication base stations close to mobile users. By providing task offloading at the network edge devices, the edge service providers can significantly reduce end-to-end latency and improve user satisfaction. However, there usually exists multiple providers in one edge, mobile users will face the choice of which edge service provider to offload their computing tasks after user allocation and offloading decision to a certain base station. In this study, from the perspective of MEC system, we investigate the online task offloading with edge service provider selection problem. We model it as a stochastic optimization problem, aiming to maximize the long-term time average user utility under limited resources, while ensuring the stability of the MEC system. We propose an online algorithm based on Lyapunov optimization to achieve a good trade-off between user satisfaction, energy consumption and system stability, then give theoretical proof of its performance bound. Experiments have been conducted based on a real-world dataset, and the results show that OEPS shows full-range performance advantages over three baseline methods.
{"title":"Online Task Offloading with Edge Service Providers Selection for Mobile Edge Computing","authors":"Jianwen Shang, Wenbin Liu, Yongjian Yang","doi":"10.1109/WCNC55385.2023.10118750","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118750","url":null,"abstract":"Mobile Edge Computing (MEC) is a promising distributed computing paradigm, where the service providers deploy their computing power at the communication base stations close to mobile users. By providing task offloading at the network edge devices, the edge service providers can significantly reduce end-to-end latency and improve user satisfaction. However, there usually exists multiple providers in one edge, mobile users will face the choice of which edge service provider to offload their computing tasks after user allocation and offloading decision to a certain base station. In this study, from the perspective of MEC system, we investigate the online task offloading with edge service provider selection problem. We model it as a stochastic optimization problem, aiming to maximize the long-term time average user utility under limited resources, while ensuring the stability of the MEC system. We propose an online algorithm based on Lyapunov optimization to achieve a good trade-off between user satisfaction, energy consumption and system stability, then give theoretical proof of its performance bound. Experiments have been conducted based on a real-world dataset, and the results show that OEPS shows full-range performance advantages over three baseline methods.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116697597","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10119013
Junjie Qi, Heli Zhang, Xi Li, Hong Ji, Xun Shao
With the sixth generation (6G) proposal, collaboration at the edge of the Internet of Things (IoT) has been widely studied to coordinate limited edge resources. Kubernetes has emerged as a promising solution for flexible and efficient resource scheduling. However, the default scheduler of Kubernetes only allocates pods separately according to the resource utilization condition of the cluster, which ignores the effect of the correlation between micro-services on latency. Under this circumstance, we propose a micro-service deployment strategy based on edgeedge collaboration, which takes the correlation between micro-services into account and models it as Service Function Chain (SFC), aiming to reduce the delay and balance the utilization rate in the edge cluster. Furthermore, we propose a model-free Distributed Deep Reinforcement Learning Deployment (DDRLD) algorithm to solve the multi-objective optimization problem. The master node trains the Q network and updates the parameters to the other nodes in the cluster, where each node can determine the deploying decision separately. Simulation results show that the proposed scheduling strategy can reduce user delay while ensuring the balance of the utilization rate.
随着第六代(6G)的提出，物联网(IoT)边缘协作被广泛研究，以协调有限的边缘资源。Kubernetes已经成为灵活高效的资源调度解决方案。但是，Kubernetes的默认调度器只是根据集群的资源利用情况单独分配pod，忽略了微服务之间的相关性对延迟的影响。在这种情况下，我们提出了一种基于边缘协作的微服务部署策略，该策略考虑了微服务之间的相关性，并将其建模为服务功能链(Service Function Chain, SFC)，旨在减少边缘集群中的延迟和平衡利用率。此外，我们提出了一种无模型分布式深度强化学习部署(DDRLD)算法来解决多目标优化问题。主节点训练Q网络并向集群中的其他节点更新参数，其中每个节点可以单独确定部署决策。仿真结果表明，该调度策略能够在保证利用率平衡的同时减少用户延迟。
{"title":"Edge-edge Collaboration Based Micro-service Deployment in Edge Computing Networks","authors":"Junjie Qi, Heli Zhang, Xi Li, Hong Ji, Xun Shao","doi":"10.1109/WCNC55385.2023.10119013","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10119013","url":null,"abstract":"With the sixth generation (6G) proposal, collaboration at the edge of the Internet of Things (IoT) has been widely studied to coordinate limited edge resources. Kubernetes has emerged as a promising solution for flexible and efficient resource scheduling. However, the default scheduler of Kubernetes only allocates pods separately according to the resource utilization condition of the cluster, which ignores the effect of the correlation between micro-services on latency. Under this circumstance, we propose a micro-service deployment strategy based on edgeedge collaboration, which takes the correlation between micro-services into account and models it as Service Function Chain (SFC), aiming to reduce the delay and balance the utilization rate in the edge cluster. Furthermore, we propose a model-free Distributed Deep Reinforcement Learning Deployment (DDRLD) algorithm to solve the multi-objective optimization problem. The master node trains the Q network and updates the parameters to the other nodes in the cluster, where each node can determine the deploying decision separately. Simulation results show that the proposed scheduling strategy can reduce user delay while ensuring the balance of the utilization rate.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122535621","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118822
A. Fominykh, A. Frolov, Kangjian Qin
Iterative detection and decoding (IDD) induces higher performance than separate detection and decoding schemes. However, IDD requires the exchange of soft information between the detector and decoder, thus decoders should be able to process soft input and provide soft output (SISO). Existing SISO polar decoders such as belief propagation (BP) and soft cancellation (SCAN) give poor block error rate (BLER) performance compared with cyclic redundancy check (CRC)-aided successive cancellation list (CA-SCL) decoders, whereas other more sophisticated SISO decoders have high complexity albeit good BLER performance. In this paper, an efficient SISO decoder that provides both high BLER performance and low complexity is proposed for polar codes in IDD system. Specifically, the proposed SISO decoder employs the sum-product algorithm over the sparse parity-check matrix representation of polar codes for lower complexity and uses the best candidates from list decoders for good error correction performance. We comprehensively evaluate the proposed SISO decoder performance in MIMO iterative system. Simulation results show that the proposed SISO decoder outperforms the existing SCAN and BP decoders and achieves similar BLER performance, but lower complexity compared with the state-of-the-art Soft List decoder.
{"title":"An Efficient Soft-Input Soft-Output Decoder for Polar Codes in MIMO Iterative Detection System","authors":"A. Fominykh, A. Frolov, Kangjian Qin","doi":"10.1109/WCNC55385.2023.10118822","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118822","url":null,"abstract":"Iterative detection and decoding (IDD) induces higher performance than separate detection and decoding schemes. However, IDD requires the exchange of soft information between the detector and decoder, thus decoders should be able to process soft input and provide soft output (SISO). Existing SISO polar decoders such as belief propagation (BP) and soft cancellation (SCAN) give poor block error rate (BLER) performance compared with cyclic redundancy check (CRC)-aided successive cancellation list (CA-SCL) decoders, whereas other more sophisticated SISO decoders have high complexity albeit good BLER performance. In this paper, an efficient SISO decoder that provides both high BLER performance and low complexity is proposed for polar codes in IDD system. Specifically, the proposed SISO decoder employs the sum-product algorithm over the sparse parity-check matrix representation of polar codes for lower complexity and uses the best candidates from list decoders for good error correction performance. We comprehensively evaluate the proposed SISO decoder performance in MIMO iterative system. Simulation results show that the proposed SISO decoder outperforms the existing SCAN and BP decoders and achieves similar BLER performance, but lower complexity compared with the state-of-the-art Soft List decoder.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122647555","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118923
Ilias Chatzistefanidis, N. Makris, Virgilios Passas, T. Korakis
As networks become denser and more heterogeneous different paths can be considered in order to reach each multi-homed UE, offering optimal performance. 5G and beyond networks feature contributions related to the dynamic programming of the network, from the operator side, in order to optimally allocate resources in the network. In this work, we consider such a case, where network access is provided to the end-users via heterogeneous (3GPP and non-3GPP) Distributed Units (DUs), converging to a single Central Unit (CU), and programmable on the fly with external interfaces. We employ Machine Learning (ML) methods in order to forecast the Quality of Service (QoS) that a wireless client will get from the network in the near future based on the Channel State Information (CSI) metric. Subsequently, we appropriately steer the traffic over the different heterogeneous DUs for ensuring that the network meets the needs of the UEs. We design, develop, deploy and evaluate our method in a real testbed environment, using emulated mobility. Our results show that the overall throughput of each UE can be drastically improved compared to existing allocation mechanisms.
{"title":"ML-based Traffic Steering for Heterogeneous Ultra-dense beyond-5G Networks","authors":"Ilias Chatzistefanidis, N. Makris, Virgilios Passas, T. Korakis","doi":"10.1109/WCNC55385.2023.10118923","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118923","url":null,"abstract":"As networks become denser and more heterogeneous different paths can be considered in order to reach each multi-homed UE, offering optimal performance. 5G and beyond networks feature contributions related to the dynamic programming of the network, from the operator side, in order to optimally allocate resources in the network. In this work, we consider such a case, where network access is provided to the end-users via heterogeneous (3GPP and non-3GPP) Distributed Units (DUs), converging to a single Central Unit (CU), and programmable on the fly with external interfaces. We employ Machine Learning (ML) methods in order to forecast the Quality of Service (QoS) that a wireless client will get from the network in the near future based on the Channel State Information (CSI) metric. Subsequently, we appropriately steer the traffic over the different heterogeneous DUs for ensuring that the network meets the needs of the UEs. We design, develop, deploy and evaluate our method in a real testbed environment, using emulated mobility. Our results show that the overall throughput of each UE can be drastically improved compared to existing allocation mechanisms.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131127699","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}
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