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- 저자 : Changchuan Yin (검색결과 2 건)
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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.
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Mingzhe Chen,Mozaffari, Mohammad,Saad, Walid,Changchuan Yin,Debbah, Merouane,Choong Seon Hong IEEE 2017 IEEE journal on selected areas in communications Vol.35 No.5
<P>In this paper, the problem of proactive deployment of cache-enabled unmanned aerial vehicles (UAVs) for optimizing the quality-of-experience (QoE) of wireless devices in a cloud radio access network is studied. In the considered model, the network can leverage human-centric information, such as users' visited locations, requested contents, gender, job, and device type to predict the content request distribution, and mobility pattern of each user. Then, given these behavior predictions, the proposed approach seeks to find the user-UAV associations, the optimal UAVs' locations, and the contents to cache at UAVs. This problem is formulated as an optimization problem whose goal is to maximize the users' QoE while minimizing the transmit power used by the UAVs. To solve this problem, a novel algorithm based on the machine learning framework of conceptor-based echo state networks (ESNs) is proposed. Using ESNs, the network can effectively predict each user's content request distribution and its mobility pattern when limited information on the states of users and the network is available. Based on the predictions of the users' content request distribution and their mobility patterns, we derive the optimal locations of UAVs as well as the content to cache at UAVs. Simulation results using real pedestrian mobility patterns from BUPT and actual content transmission data from Youku show that the proposed algorithm can yield 33.3% and 59.6% gains, respectively, in terms of the average transmit power and the percentage of the users with satisfied QoE compared with a benchmark algorithm without caching and a benchmark solution without UAVs.</P>
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