Research On Wireless Resource Management And Optimization In Dense Cloud-WiFi Systems

IEEE 802.11 based wireless local area networks(WLANs) have widespread deployment in a variety of settings in recent years. Due to the increasing number of smart devices, more and more access points(APs) and stations(STAs) are added to WLANs. Dense deployment of devices leads to a huge challenge for wireless resource management. Cloud-WiFi structure can do better in managing and scheduling network’s wireless resources by decoupling the system’s control planefrom the underlying data plane.In this paper, we focus on the research of wireless resource management and optimization in dense cloud-wifi wireless local access networks. To quantity traffic load of APs and STAs, we analyze several different traffic load conditions and then we establish a system model to represent the wireless access proceed. We analyze the widely adopted airtime cost metric to evaluate the nodes’ traffic load, and we optimize the calculation methods and process according to different traffic load conditions, such as in both saturated and unsaturated traffic load conditions, and in both homogeneous and heterogeneous traffic load for different users.Based on the aforementioned calculation conclusions of airtime cost metric, we focus on the study of user association problems, load balancing problems and AP sleep scheduling problems, and we propose several wireless resource management and optimization solutions. Firstly we propose a traffic-load aware user association and load balancing algorithm. The algorithm is based oncentralized control structure and congestion game theory. We compare the performance of our proposed scheme with several existing schemes under different scenarios. The results show that our proposed scheme achieves better performance in terms of traffic load balancing and fairness. We also propose a traffic-load aware AP sleep scheduling algorithm based on centralized control structure, which can turn off APs with low load effectively. Our evaluation demonstrates that our proposed AP sleep scheduling scheme can effectively reduce WLAN’s energy consumption while maintaining a good quality of service.