The Experts below are selected from a list of 43677 Experts worldwide ranked by ideXlab platform

Victor C M Leung - One of the best experts on this subject based on the ideXlab platform.

  • Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching
    IEEE Journal on Selected Areas in Communications, 2021
    Co-Authors: Xiaofei Wang, Chenyang Wang, Tarik Taleb, Victor C M Leung
    Abstract:

    In order to meet the growing demands for Multimedia Service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In this article, based on the flexible trilateral cooperation among user equipment, edge base stations and a cloud server, we propose a D2D-assisted heterogeneous collaborative edge caching framework by jointly optimizing the node selection and cache replacement in mobile networks. We formulate the joint optimization problem as a Markov decision process, and use a deep Q-learning network to solve the long-term mixed integer linear programming problem. We further design an attention-weighted federated deep reinforcement learning (AWFDRL) model that uses federated learning to improve the training efficiency of the Q-learning network by considering the limited computing and storage capacity, and incorporates an attention mechanism to optimize the aggregation weights to avoid the imbalance of local model quality. We prove the convergence of the corresponding algorithm, and present simulation results to show the effectiveness of the proposed AWFDRL framework in reducing average delay of content access, improving hit rate and offloading traffic.

  • collaborative hierarchical caching in cloud radio access networks
    Conference on Computer Communications Workshops, 2017
    Co-Authors: Xiaofei Wang, Zhu Han, Victor C M Leung
    Abstract:

    To deal with the severe challenge from explosively increasing mobile users' Multimedia Service requests in mobile networks, introducing cloud-based computing platform and content caching into radio access networks (RANs) has become an effective emerging technique. In this paper, we propose a collaborative hierarchical caching framework in cloud RANs (C-RANs), which minimizes the access delay of content delivery under certain specific network constraints to improve users' Quality of Service (QoS) as well as offload network traffic. Specifically, based on the formed hierarchical caching topology consisting of two tiers of content caching at the base stations and the cloud central unit (CCU), we decompose the formulated large-scale optimization problem into a series of simpler subproblems, and then propose the corresponding low-complexity distributed heuristic solutions as well as a content request routing scheme. Trace-based evaluation results demonstrate the effectiveness of the proposed framework.

Xiaofei Wang - One of the best experts on this subject based on the ideXlab platform.

  • Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching
    IEEE Journal on Selected Areas in Communications, 2021
    Co-Authors: Xiaofei Wang, Chenyang Wang, Tarik Taleb, Victor C M Leung
    Abstract:

    In order to meet the growing demands for Multimedia Service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In this article, based on the flexible trilateral cooperation among user equipment, edge base stations and a cloud server, we propose a D2D-assisted heterogeneous collaborative edge caching framework by jointly optimizing the node selection and cache replacement in mobile networks. We formulate the joint optimization problem as a Markov decision process, and use a deep Q-learning network to solve the long-term mixed integer linear programming problem. We further design an attention-weighted federated deep reinforcement learning (AWFDRL) model that uses federated learning to improve the training efficiency of the Q-learning network by considering the limited computing and storage capacity, and incorporates an attention mechanism to optimize the aggregation weights to avoid the imbalance of local model quality. We prove the convergence of the corresponding algorithm, and present simulation results to show the effectiveness of the proposed AWFDRL framework in reducing average delay of content access, improving hit rate and offloading traffic.

  • collaborative hierarchical caching in cloud radio access networks
    Conference on Computer Communications Workshops, 2017
    Co-Authors: Xiaofei Wang, Zhu Han, Victor C M Leung
    Abstract:

    To deal with the severe challenge from explosively increasing mobile users' Multimedia Service requests in mobile networks, introducing cloud-based computing platform and content caching into radio access networks (RANs) has become an effective emerging technique. In this paper, we propose a collaborative hierarchical caching framework in cloud RANs (C-RANs), which minimizes the access delay of content delivery under certain specific network constraints to improve users' Quality of Service (QoS) as well as offload network traffic. Specifically, based on the formed hierarchical caching topology consisting of two tiers of content caching at the base stations and the cloud central unit (CCU), we decompose the formulated large-scale optimization problem into a series of simpler subproblems, and then propose the corresponding low-complexity distributed heuristic solutions as well as a content request routing scheme. Trace-based evaluation results demonstrate the effectiveness of the proposed framework.

Youngnam Han - One of the best experts on this subject based on the ideXlab platform.

  • joint resource allocation for parallel multi radio access in heterogeneous wireless networks
    IEEE Transactions on Wireless Communications, 2010
    Co-Authors: Yonghoon Choi, Hoon Kim, Sangwook Han, Youngnam Han
    Abstract:

    Heterogeneous wireless networks where several systems with different bands coexist for Multimedia Service are currently in Service and will be widely adopted to support various traffic demand. Under heterogeneous networks, a mobile station can transmit over multiple and simultaneous radio access technologies (RATs) such as WLAN, HSPA, and WCDMA LTE. Also, cognitive radio for the efficient use of underutilized/unused frequency band is successfully implemented in some networks. In this letter, we address such operational issues as air interface and band selection for a mobile and power allocation to the chosen links. An optimal solution is sought and analyzed and a distributed joint allocation algorithm is proposed to maximize total system capacity. We investigate the benefit of multiple transmissions by multiple RATs over a single transmission by a single RAT at a time, which can be interpreted as network diversity. Numerical results validate the performance enhancement of our proposed algorithm.

Xiaomeng Wang - One of the best experts on this subject based on the ideXlab platform.

  • adaptive joint session scheduling for Multimedia Services in heterogeneous wireless networks
    Vehicular Technology Conference, 2009
    Co-Authors: Dian Fan, Zhiyong Feng, Xiaomeng Wang
    Abstract:

    This paper discusses the joint session scheduling (JOSCH) problem for Multimedia Services in heterogeneous wireless networks. The adaptive JOSCH based on layer-encoded streaming is designed for real-time Multimedia Service simultaneously transmitted by several heterogeneous radio access technologies (RATs). Considering the layered characteristics of the layer-encoded streaming, an adaptive JOSCH mechanism, along with the supporting network architecture, is designed to make efficient usage of the heterogeneous wireless resources and adapt to the dynamic network changes. Simulation results show that the adaptive JOSCH is effective in guaranteeing the QoS of Multimedia and maintaining a high transmitting adaptability in multi-RATs environment.

Gomez-barquero David - One of the best experts on this subject based on the ideXlab platform.

  • 5G Radio Access Network Architecture for Terrestrial Broadcast Services
    'Institute of Electrical and Electronics Engineers (IEEE)', 2020
    Co-Authors: Säily Mikko, Estevan, Carlos Barjau, Gimenez, Jordi Joan, Tesema Fasil, Guo Wei, Gomez-barquero David
    Abstract:

    The 3rd Generation Partnership Project (3GPP) has defined based on the Long Term Evolution (LTE) enhanced Multicast Broadcast Multimedia Service (eMBMS) a set of new features to support the distribution of Terrestrial Broadcast Services in Release 14. On the other hand, a new 5th Generation (5G) system architecture and radio access technology, 5G New Radio (NR), are being standardised from Release 15 onwards, which so far have only focused on unicast connectivity. This may change in Release 17 given a new Work Item set to specify basic Radio Access Network (RAN) functionalities for the provision of multicast/broadcast communications for NR. This work initially excludes some of the functionalities originally supported for Terrestrial Broadcast Services under LTE e.g. free to air, receive-only mode, large-area single frequency networks, etc. This paper proposes an enhanced Next Generation RAN architecture based on 3GPP Release 15 with a series of architectural and functional enhancements, to support an efficient, flexible and dynamic selection between unicast and multicast/broadcast transmission modes and also the delivery of Terrestrial Broadcast Services. The paper elaborates on the Cloud-RAN based architecture and proposes new concepts such as the RAN Broadcast/Multicast Areas that allows a more flexible deployment in comparison to eMBMS. High-level assessment methodologies including complexity analysis and inspection are used to evaluate the feasibility of the proposed architecture design and compare it with the 3GPP architectural requirements.Comment: 12 pages, 10 figures, 2 tables, IEEE Trans. Broadcastin

  • 5G Radio Access Network Architecture for Terrestrial Broadcast Services
    'Institute of Electrical and Electronics Engineers (IEEE)', 2020
    Co-Authors: Säily Mikko, Estevan, Carlos Barjau, Gimenez, Jordi Joan, Tesema Fasil, Guo Wei, Gomez-barquero David
    Abstract:

    The 3rd Generation Partnership Project (3GPP) has defined based on the Long Term Evolution (LTE) enhanced Multicast Broadcast Multimedia Service (eMBMS) a set of new features to support the distribution of Terrestrial Broadcast Services in Release 14. On the other hand, a new 5 th Generation (5G) system architecture and radio access technology, 5G New Radio (NR), are being standardized from Release 15 onwards, which so far have only focused on unicast connectivity. This may change in Release 17 given a new Work Item set to specify basic Radio Access Network (RAN) functionalities for the provision of multicast/broadcast communications for NR. This work initially excludes some of the functionalities originally supported for Terrestrial Broadcast Services under LTE, e.g., free to air, receive-only mode, large-area single frequency networks, etc. This paper proposes an enhanced Next Generation RAN architecture based on 3GPP Release 15 with a series of architectural and functional enhancements, to support an efficient, flexible and dynamic selection between unicast and multicast/broadcast transmission modes and also the delivery of Terrestrial Broadcast Services. The paper elaborates on the Cloud-RAN based architecture and proposes new concepts such as the RAN Broadcast/Multicast Areas that allows a more flexible deployment in comparison to eMBMS. High-level assessment methodologies including complexity analysis and inspection are used to evaluate the feasibility of the proposed architecture design and compare it with the 3GPP architectural requirements