http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Chunyu Pan (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
Nahida Kiran,Chunyu Pan,Sihua Wang,Chang-chuan Yin 한국통신학회 2020 Journal of communications and networks Vol.22 No.1
The rapid growth of the internet usage and the distributedcomputing resources of edge devices create a necessity tohave a reasonable controller to ensure efficient utilization of distributedcomputing resources in mobile edge computing (MEC). We envision the future MEC services, where quality of experience(QoE) of the services is further enhanced by software definednetworks (SDNs) capabilities to reduce the application-levelresponse time without service disruptions. SDN, which is not proposedspecifically for edge computing, can in fact serve as an enablerto lower the complexity barriers involved and let the realpotential of edge computing be achieved. In this paper, we investigatethe task offloading and resource allocation problem in wirelessMEC aiming to minimize the delay while saving the battery powerof user device simultaneously. However, it is challenging to obtainan optimal policy in such a dynamic task offloading system. Learningfrom experience plays a vital role in time variant dynamic systemswhere reinforcement learning (RL) takes a long term goal intoconsideration besides immediate reward, which is very importantfor a dynamic environment. A novel software defined edge cloudlet(SDEC) based RL optimization framework is proposed to tacklethe task offloading and resource allocation in wireless MEC. Specifically,Q-learning and cooperative Q-learning based reinforcementlearning schemes are proposed for the intractable problem. Simulationresults show that the proposed scheme achieves 31.39% and62.10% reduction on the sum delay compared to other benchmarkmethods such as traditional Q-learning with a random algorithmand Q-learning with epsilon greedy.
-
Tianyi Zhou,Xuehua Li,Chunyu Pan,Mingyu Zhou,Yuanyuan Yao 한국통신학회 2021 Journal of communications and networks Vol.23 No.6
Federated edge learning (FEL) deploys a machinelearning algorithm by using devices distributed on the edge ofa network, trains massive local data, uploads the local modelto update the parameters after training, and performs alternateupdating with global model parameters to reduce the pressurefor uplink data transmission, prevent systematic time delay andensure data security. This paper proposes that an optimal balancebetween time delay and energy consumption be achieved by optimizingthe transmission power and bandwidth allocation basedon user quality of experience (QoE) in a multi-server intelligentedge network. Given the limited computing capability of devicesinvolved in FEL local training, the transmission power is modeledas a quasi-convex uplink power allocation (UPA) problem, anda lower energy consumption bandwidth allocation algorithm isproposed for solution-seeking. The proposed algorithm allocatesappropriate power to the device by adapting the computingpower and channel state of the device, thereby reducing energyconsumption. As the theoretical deduction result suggests thatadditional bandwidth should be allocated to those devices withweak computing capabilities and poor channel conditions torealize minimal energy consumption within the restraint time. The simulation result indicates that, the maximum gain of theproposed algorithm can be optimized by 31% compared with thebaseline.
-
Handover Based on AP Load in Software Defined Wi-Fi Systems
Nahida Kiran,Changchuan Yin,Ying Hu,Zulfiqar Ali Arain,Chunyu Pan,Israr Khan,Yanbin Zhang,G. M. Shafiqur Rahman 한국통신학회 2017 Journal of communications and networks Vol.19 No.6
Existing wireless systems do not work efficiently underchanging environment. Due to its inflexible and proprietary hardwarebased architectural limitations it’s not easy for an operatorto change the network strategy under heavy loads, which enablesvendors to try and implement new networking protocols. The softwaredefined network (SDN) is proposed to bring flexibility andprogrammability, which allows the control plane of switch to becontrolled and managed remotely using open-flow channels. Takingthe advantage of SDN in wireless networks, a new SDN basedWi-Fi architecture is introduced and an access point (AP) load balancebased handover algorithm is proposed. Mininet Wi-Fi emulatoris used to construct the desired topology for experiments andperformance analysis. Simulation results show a successful handoverfrom an overloaded AP to a lightly loaded AP. A significantimprovement observed in the throughput with low latency.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
