Hu Zhiqun

Improving throughput through dynamically tuning contention window size in dense wireless network

Abstract With the boom of wireless devices, the number of wireless users under wireless local area networks (WLANs) has increased dramatically. However, the standard backoff mechanism in IEEE 802.11 adopts fixed initial contention window (CW) size without considering changes of network load, which leads to a high collision probability and low channel utilization in bursty arrivals. In this paper, a novel CW dynamic adjustment scheme is proposed to achieve high throughput performance in dense user environment. In the proposed scheme, the initial CW size is dynamically adjusted to optimum according to the measured packet collision probability. Simulation results show that the proposed scheme can significantly improve the throughput performance.

QoE based power control scheme for interference mitigation in high-density WLANs

Abstract Mobile data traffic is going through an explosive growth recently as mobile smart devices become more and more ubiquitous, causing huge pressure on cellular network. Taking advantage of its low cost and easy-to-deploy feature, wireless local-area networks (WLAN) becomes increasingly popular to offload data streams from cellular network, followed by higher and higher density of its deployment. However, the high density of WLAN will cause more interference, which results in degradation of its performance. Therefore, in order to enhance the performance of the network, we aim to minimize the interference caused by high density of WLAN. In this paper, we propose a novel power control scheme to achieve the above aim. We use the quality of experience (QoE) evaluation to coordinate the power of each access point (AP) and finally realize the optimization of the entire network. According to the simulation results, our scheme improves the performance of the network significantly in many aspects, including throughput and QoE.

Adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks

Abstract The unforeseen mobile data explosion poses a major challenge to the performance of todays cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment.