Pub Date : 2022-05-16DOI: 10.48550/arXiv.2206.05881
Lingling Zhang, Yanxiang Jiang, F. Zheng, M. Bennis, X. You
The fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs). Due to the limited resource of F-APs, it is important to design an efficient task offloading scheme. In this paper, by considering time-varying network environment, a dynamic computation offloading and resource allocation problem in F-RANs is formulated to minimize the task execution delay and energy consumption of MDs. To solve the problem, a federated deep reinforcement learning (DRL) based algorithm is proposed, where the deep deterministic policy gradient (DDPG) algorithm performs computation offloading and resource allocation in each F-AP. Federated learning is exploited to train the DDPG agents in order to decrease the computing complexity of training process and protect the user privacy. Simulation results show that the proposed federated DDPG algorithm can achieve lower task execution delay and energy consumption of MDs more quickly compared with the other existing strategies.
{"title":"Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach","authors":"Lingling Zhang, Yanxiang Jiang, F. Zheng, M. Bennis, X. You","doi":"10.48550/arXiv.2206.05881","DOIUrl":"https://doi.org/10.48550/arXiv.2206.05881","url":null,"abstract":"The fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs). Due to the limited resource of F-APs, it is important to design an efficient task offloading scheme. In this paper, by considering time-varying network environment, a dynamic computation offloading and resource allocation problem in F-RANs is formulated to minimize the task execution delay and energy consumption of MDs. To solve the problem, a federated deep reinforcement learning (DRL) based algorithm is proposed, where the deep deterministic policy gradient (DDPG) algorithm performs computation offloading and resource allocation in each F-AP. Federated learning is exploited to train the DDPG agents in order to decrease the computing complexity of training process and protect the user privacy. Simulation results show that the proposed federated DDPG algorithm can achieve lower task execution delay and energy consumption of MDs more quickly compared with the other existing strategies.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116574790","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814688
Yin-Hwa Huang, Zhaoyang Zhang, Jue Wang, Chongwen Huang, C. Zhong
With the rapid development of intelligent devices, the fifth-generation (5G) mobile wireless networks are envisioned to support massive connections and higher capacity. To confront challenges on link inefficiency in traditional mobile wireless networks, the link adaptation technology is crucial for system capacity improvements and requires coordination with resource allocation strategy. In this paper, we consider a joint adaptive modulation and coding (AMC) and resource allocation (RA) in a wireless network, where multiple users share limited subcarriers and adaptively change modulation levels and transmit power with the target to maximize the long-term system throughput. Instead of using optimization theory-based methods with higher complexity, we propose an intelligent double deep Q-network (DDQN)-based AMC and RA algorithm, which regards users as agents that learn cooperatively from their past experiences and implement their policies distributively. Furthermore, to guarantee fairness among users, we re-design the multi-agent reinforcement learning (MARL) reward function to incorporate the attained proportional fairness of each user at the current cycle into our objective. Simulation results demonstrate that users successfully learn to collaborate in a distributed manner, which leads to improved throughput both of the single link level and the whole system level.
{"title":"Joint AMC and Resource Allocation for Mobile Wireless Networks Based on Distributed MARL","authors":"Yin-Hwa Huang, Zhaoyang Zhang, Jue Wang, Chongwen Huang, C. Zhong","doi":"10.1109/iccworkshops53468.2022.9814688","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814688","url":null,"abstract":"With the rapid development of intelligent devices, the fifth-generation (5G) mobile wireless networks are envisioned to support massive connections and higher capacity. To confront challenges on link inefficiency in traditional mobile wireless networks, the link adaptation technology is crucial for system capacity improvements and requires coordination with resource allocation strategy. In this paper, we consider a joint adaptive modulation and coding (AMC) and resource allocation (RA) in a wireless network, where multiple users share limited subcarriers and adaptively change modulation levels and transmit power with the target to maximize the long-term system throughput. Instead of using optimization theory-based methods with higher complexity, we propose an intelligent double deep Q-network (DDQN)-based AMC and RA algorithm, which regards users as agents that learn cooperatively from their past experiences and implement their policies distributively. Furthermore, to guarantee fairness among users, we re-design the multi-agent reinforcement learning (MARL) reward function to incorporate the attained proportional fairness of each user at the current cycle into our objective. Simulation results demonstrate that users successfully learn to collaborate in a distributed manner, which leads to improved throughput both of the single link level and the whole system level.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124160898","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814636
Bo Che, Xinyi Li, Zhi Chen, Qi He
Terahertz communication is one of the important candidate technologies for 6G in the future. The deep learning (DL)-based transmission method can use the actual transmission data to learn and fit the channel and non-ideal characteristics of devices in real time, and thus is an effective manner to solve the modeling problems of the channel and device responses in the terahertz band. However, the DL-based method has a high complexity which prevents its usage in the high-rate terahertz transmission. This paper proposes a binary neural network based model to reduce complexity, combined with the training method based on generative adversarial network (GAN) to overcome the unknown channel problem. Simulation results show that the proposed method can achieve a similar performance with QAM under the AWGN channel, but performs much better when non-ideal characteristics exist. Besides, the complexity of the proposed method is much less than the existing DL-based method, and the data size to be transmitted back in GAN is also largely reduced. All these results reflect the feasibility of the proposed method in scenarios with significant non-ideal characteristics such as the terahertz communication.
{"title":"A Low-Complexity Transceiver Design for Terahertz Communication based on Deep Learning","authors":"Bo Che, Xinyi Li, Zhi Chen, Qi He","doi":"10.1109/iccworkshops53468.2022.9814636","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814636","url":null,"abstract":"Terahertz communication is one of the important candidate technologies for 6G in the future. The deep learning (DL)-based transmission method can use the actual transmission data to learn and fit the channel and non-ideal characteristics of devices in real time, and thus is an effective manner to solve the modeling problems of the channel and device responses in the terahertz band. However, the DL-based method has a high complexity which prevents its usage in the high-rate terahertz transmission. This paper proposes a binary neural network based model to reduce complexity, combined with the training method based on generative adversarial network (GAN) to overcome the unknown channel problem. Simulation results show that the proposed method can achieve a similar performance with QAM under the AWGN channel, but performs much better when non-ideal characteristics exist. Besides, the complexity of the proposed method is much less than the existing DL-based method, and the data size to be transmitted back in GAN is also largely reduced. All these results reflect the feasibility of the proposed method in scenarios with significant non-ideal characteristics such as the terahertz communication.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130338601","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814548
Yuanhe Wang, Chao Zhang
As the typical physical resource of the electro-magnetic wave, the Orbital Angular Momentum (OAM) provides the wireless transmission system with new dimension. The traditiona~ Shannon capacity just concerns the contribution from the electric field strength, which can not show the new dimension of the OAM. Therefore, the enhanced Shannon capacity with the OAM dimension is proposed in this paper. Specifically, the enhanced Shannon capacity originates from two parts. In the first part, the OAM mode can raise the number of independent parallel channels, which transmit electric field strength signals with the same time and frequency. In the second part, since the physical dimension of OAM is independent of the electric field strength, the OAM can vary over time and carry the information independently, which further increases the capacity. Moreover, the power reuse (i.e., the signal power can contribute to the Signal to Noise Ratio (SNR) at the dedicated dimension) realizes the second part of the above enhanced Shannon capacity, which solves the problem of the marginal effect of the traditional Shannon capacity. Particularly, the enhanced Shannon capacity highlights the potential of OAM transmission.
{"title":"Enhanced Shannon Capacity with Orbital Angular Momentum Dimension","authors":"Yuanhe Wang, Chao Zhang","doi":"10.1109/iccworkshops53468.2022.9814548","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814548","url":null,"abstract":"As the typical physical resource of the electro-magnetic wave, the Orbital Angular Momentum (OAM) provides the wireless transmission system with new dimension. The traditiona~ Shannon capacity just concerns the contribution from the electric field strength, which can not show the new dimension of the OAM. Therefore, the enhanced Shannon capacity with the OAM dimension is proposed in this paper. Specifically, the enhanced Shannon capacity originates from two parts. In the first part, the OAM mode can raise the number of independent parallel channels, which transmit electric field strength signals with the same time and frequency. In the second part, since the physical dimension of OAM is independent of the electric field strength, the OAM can vary over time and carry the information independently, which further increases the capacity. Moreover, the power reuse (i.e., the signal power can contribute to the Signal to Noise Ratio (SNR) at the dedicated dimension) realizes the second part of the above enhanced Shannon capacity, which solves the problem of the marginal effect of the traditional Shannon capacity. Particularly, the enhanced Shannon capacity highlights the potential of OAM transmission.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126342586","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814640
Zhaoyang Su, Liu Liu, Jiachi Zhang, Yuanyuan Fan, Kai Wang, Lingfan Zhuang, Zhiyuan Wang, Shengjie Zheng
Vehicles have been an important role in people's life. However, traffic accidents caused by vehicles are increasing exponentially with the popularization of vehicles. As an important part of the intelligent transportation system (ITS), vehicle-to-everything (V2X) can improve the traffic efficiency and safety through intercommunication in the transportation system. Urban is a typical scenario in V2X communication with numerous vehicles and infrastructures. The urban channels are non-stationary due to the high speed of vehicles and scatterers. Therefore, we carried out measurement campaigns and analyzed the non-stationarity of vehicle-to-vehicle (V2V) as well as vehicle-to-infrastructure (V2I) channels. In this paper, measurement campaigns for the urban channels at 5.9 GHz were performed including 3 scenarios. The local region of stationarity (LRS) method is used to analyze the non-stationary channel based on actual measured channel data. We focus on the power delay profile (PDP), stationarity interval, and temporal PDP correlation coefficient (TPCC) of the channels. In addition, the statistics analysis of stationarity distance is also provided.
{"title":"Analysis of Channel Non-Stationarity for V2V and V2I Communications at 5.9GHz in Urban Scenarios","authors":"Zhaoyang Su, Liu Liu, Jiachi Zhang, Yuanyuan Fan, Kai Wang, Lingfan Zhuang, Zhiyuan Wang, Shengjie Zheng","doi":"10.1109/iccworkshops53468.2022.9814640","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814640","url":null,"abstract":"Vehicles have been an important role in people's life. However, traffic accidents caused by vehicles are increasing exponentially with the popularization of vehicles. As an important part of the intelligent transportation system (ITS), vehicle-to-everything (V2X) can improve the traffic efficiency and safety through intercommunication in the transportation system. Urban is a typical scenario in V2X communication with numerous vehicles and infrastructures. The urban channels are non-stationary due to the high speed of vehicles and scatterers. Therefore, we carried out measurement campaigns and analyzed the non-stationarity of vehicle-to-vehicle (V2V) as well as vehicle-to-infrastructure (V2I) channels. In this paper, measurement campaigns for the urban channels at 5.9 GHz were performed including 3 scenarios. The local region of stationarity (LRS) method is used to analyze the non-stationary channel based on actual measured channel data. We focus on the power delay profile (PDP), stationarity interval, and temporal PDP correlation coefficient (TPCC) of the channels. In addition, the statistics analysis of stationarity distance is also provided.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128936842","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814639
Yuandi Wang, Zhiqing Wei, Wei Zhou, Kaifeng Han, Z. Feng
The integrated sensing and communication (ISAC) technology will support the development of emerging services such as machine-type communication and immersive services toward the future 5th generation advanced (5G-A) and 6th generation (6G) eras. To satisfy the high-precision sensing requirements inspired by 6G emerging services and increase the communication rate, we exploit the advantages of large time-bandwidth product of linear frequency modulation (LFM) and high spectral efficiency of orthogonal frequency division multiplexing (OFDM), proposing an ISAC waveform combining triangular frequency modulation (FM) and OFDM. The delay-Doppler decoupling radar signal processing scheme is proposed, which obtains better sensing performance than the OFDM based ISAC scheme. Simulation results show that the proposed scheme can achieve high accuracy ranging estimation, and the resolution of velocity estimation is doubly improved compared with the OFDM based ISAC scheme.
{"title":"Triangular FM-OFDM Waveform Design for Integrated Sensing and Communication","authors":"Yuandi Wang, Zhiqing Wei, Wei Zhou, Kaifeng Han, Z. Feng","doi":"10.1109/iccworkshops53468.2022.9814639","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814639","url":null,"abstract":"The integrated sensing and communication (ISAC) technology will support the development of emerging services such as machine-type communication and immersive services toward the future 5th generation advanced (5G-A) and 6th generation (6G) eras. To satisfy the high-precision sensing requirements inspired by 6G emerging services and increase the communication rate, we exploit the advantages of large time-bandwidth product of linear frequency modulation (LFM) and high spectral efficiency of orthogonal frequency division multiplexing (OFDM), proposing an ISAC waveform combining triangular frequency modulation (FM) and OFDM. The delay-Doppler decoupling radar signal processing scheme is proposed, which obtains better sensing performance than the OFDM based ISAC scheme. Simulation results show that the proposed scheme can achieve high accuracy ranging estimation, and the resolution of velocity estimation is doubly improved compared with the OFDM based ISAC scheme.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128937185","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814546
WS10 IEEE ICC 2022 Workshop on Scalable, Secure and Intelligent Blockchain for Future Networking and Communications.
面向未来网络和通信的可扩展、安全和智能区块链研讨会。
{"title":"WS10 IEEE ICC 2022 Workshop on Scalable, Secure and Intelligent Blockchain for Future Networking and Communications","authors":"","doi":"10.1109/iccworkshops53468.2022.9814546","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814546","url":null,"abstract":"WS10 IEEE ICC 2022 Workshop on Scalable, Secure and Intelligent Blockchain for Future Networking and Communications.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124482095","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814557
Fabian Kurtz, Robin Wiebusch, Dennis Overbeck, C. Wietfeld
The energy grid is facing a paradigm shift away from traditionally centralized electricity generation towards dis-tributed renewable energy resources. These so-called Smart Grids (SGs) require a mechanism for balancing power consumption and generation. In this context, Blockchain (BC)-based Smart Contracts (SCs) have emerged as a means to facilitate distrib-uted transactions without requiring trust among the involved parties. Yet, resulting communication traffic loads need to be considered. Here, 5G network slicing promises to enable the coexistence of such mission critical services on a single shared physical communication infrastructure. Nevertheless, challenges in terms of latencies and resource efficiency exist. As static slicing mechanisms can be inefficient, we propose a predictive Machine Learning (ML)-driven approach to Resource Block (RB) scheduling by harnessing the Configured Grant (CG) mechanism in the 5G uplink. The developed solution is evaluated on the particularly challenging example of an energy grid driven by SCs. Based on an energy model derived from a real-world setup, we generate corresponding SC communication traffic. For this, predictive 5G slice radio resource allocation is employed to demonstrate significant improvements in terms of latency and spectrum usage efficiency. Thus, ML-enabled 5G network slicing for mission critical SCs is evaluated within large-scalable SGs.
{"title":"Predictive 5G Uplink Slicing for Blockchain-driven Smart Energy Contracts","authors":"Fabian Kurtz, Robin Wiebusch, Dennis Overbeck, C. Wietfeld","doi":"10.1109/iccworkshops53468.2022.9814557","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814557","url":null,"abstract":"The energy grid is facing a paradigm shift away from traditionally centralized electricity generation towards dis-tributed renewable energy resources. These so-called Smart Grids (SGs) require a mechanism for balancing power consumption and generation. In this context, Blockchain (BC)-based Smart Contracts (SCs) have emerged as a means to facilitate distrib-uted transactions without requiring trust among the involved parties. Yet, resulting communication traffic loads need to be considered. Here, 5G network slicing promises to enable the coexistence of such mission critical services on a single shared physical communication infrastructure. Nevertheless, challenges in terms of latencies and resource efficiency exist. As static slicing mechanisms can be inefficient, we propose a predictive Machine Learning (ML)-driven approach to Resource Block (RB) scheduling by harnessing the Configured Grant (CG) mechanism in the 5G uplink. The developed solution is evaluated on the particularly challenging example of an energy grid driven by SCs. Based on an energy model derived from a real-world setup, we generate corresponding SC communication traffic. For this, predictive 5G slice radio resource allocation is employed to demonstrate significant improvements in terms of latency and spectrum usage efficiency. Thus, ML-enabled 5G network slicing for mission critical SCs is evaluated within large-scalable SGs.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"581 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122962817","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814646
Mohammad Dehghani Soltani, Hossein Kazemi, E. Sarbazi, H. Haas, M. Safari
The optical receivers suitable for the next generation of optical wireless networks need to be ultra-high-speed while having a wide field of view (FOV) to accommodate user mobility. The design of such receivers is challenging due to two known trade-offs, namely, the area-bandwidth and the gain-FOV. In this study, we consider these trade-offs and formulate an optimisation problem to design imaging receivers that can achieve maximum high speed while satisfying a minimum FOV requirement. The design will be based on an array of arrays of photodetectors for which we present analytical derivations of signal-to-noise ratio (SNR) assuming maximum ratio combining (MRC). Practical considerations and non-idealities have been considered in our design and the reliability of the analytical model is verified by Optic Studio-based simulations. The optimization problem is solved assuming on-off keying (OOK) modulation. The results show a trade-off between achievable data rate and FOV. For example, it is demonstrated that a data rate of ~23 Gbps is achievable with a receiver of at most 2 cm x 2 cm dimensions with a FOV of 15°. However, a receiver with the same dimensions may only achieve ~8 Gbps if the FOV requirement increases to 20°.
{"title":"Optimal Imaging Receiver Design for High-Speed Mobile Optical Wireless Communications","authors":"Mohammad Dehghani Soltani, Hossein Kazemi, E. Sarbazi, H. Haas, M. Safari","doi":"10.1109/iccworkshops53468.2022.9814646","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814646","url":null,"abstract":"The optical receivers suitable for the next generation of optical wireless networks need to be ultra-high-speed while having a wide field of view (FOV) to accommodate user mobility. The design of such receivers is challenging due to two known trade-offs, namely, the area-bandwidth and the gain-FOV. In this study, we consider these trade-offs and formulate an optimisation problem to design imaging receivers that can achieve maximum high speed while satisfying a minimum FOV requirement. The design will be based on an array of arrays of photodetectors for which we present analytical derivations of signal-to-noise ratio (SNR) assuming maximum ratio combining (MRC). Practical considerations and non-idealities have been considered in our design and the reliability of the analytical model is verified by Optic Studio-based simulations. The optimization problem is solved assuming on-off keying (OOK) modulation. The results show a trade-off between achievable data rate and FOV. For example, it is demonstrated that a data rate of ~23 Gbps is achievable with a receiver of at most 2 cm x 2 cm dimensions with a FOV of 15°. However, a receiver with the same dimensions may only achieve ~8 Gbps if the FOV requirement increases to 20°.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121644720","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814668
Yunan Yan, K. Du, Luhan Wang, Haiwen Niu, X. Wen
Cloud network integration (CNI) has been a new paradigm to better support diverse vertical applications. The virtualized mobile network deployed in private and public clouds is regarded as the trend of future network evolution. However, it is challenging for radio access network (RAN) protocols to be deployed in public clouds because of the strict requirements for stable cloud resources. In a CNI environment, there coexist a large number of services (e.g. network services and cloud services) and frequent task scheduling will result in a great deal of resources fluctuation, thus degrading RAN performance. To the best of our knowledge, current researches in CNI interests ignore the high processing requirements of RAN. Therefore in this paper, we propose a multi-pass deep Q network (MP-DQN) based short term task scheduling strategy to minimize the quality of service (QoS) fluctuation of RAN deployed in public clouds. First, taking into account the differences in the relationships between resources and QoS among various services, we formulated a continuous decision problem of task scheduling. Then, We employ MP-DQN to solve the decision problem, jointly optimizing the services QoS and the task scheduling success rate. We conduct a real-world experiment to obtain the cloud RAN CPU-QoS model. The experimental results reveal that our proposed MP-DQN based task scheduling strategy performs significantly better in minimizing RAN QoS fluctuation than the conventional task scheduling strategy.
{"title":"MP-DQN Based Task Scheduling for RAN QoS Fluctuation Minimizing in Public Clouds","authors":"Yunan Yan, K. Du, Luhan Wang, Haiwen Niu, X. Wen","doi":"10.1109/iccworkshops53468.2022.9814668","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814668","url":null,"abstract":"Cloud network integration (CNI) has been a new paradigm to better support diverse vertical applications. The virtualized mobile network deployed in private and public clouds is regarded as the trend of future network evolution. However, it is challenging for radio access network (RAN) protocols to be deployed in public clouds because of the strict requirements for stable cloud resources. In a CNI environment, there coexist a large number of services (e.g. network services and cloud services) and frequent task scheduling will result in a great deal of resources fluctuation, thus degrading RAN performance. To the best of our knowledge, current researches in CNI interests ignore the high processing requirements of RAN. Therefore in this paper, we propose a multi-pass deep Q network (MP-DQN) based short term task scheduling strategy to minimize the quality of service (QoS) fluctuation of RAN deployed in public clouds. First, taking into account the differences in the relationships between resources and QoS among various services, we formulated a continuous decision problem of task scheduling. Then, We employ MP-DQN to solve the decision problem, jointly optimizing the services QoS and the task scheduling success rate. We conduct a real-world experiment to obtain the cloud RAN CPU-QoS model. The experimental results reveal that our proposed MP-DQN based task scheduling strategy performs significantly better in minimizing RAN QoS fluctuation than the conventional task scheduling strategy.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122657474","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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