Zhiqun Hu

Adaptive network selection based on attractor selection in data offloading

The unforeseen mobile data explosion poses a major challenge to the performance of todays cellular networks, and cellular network is in urgent need of original solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the issue of overload in the cellular networks with a low operational and capital expenditure. In this paper, we study the network selection problem in operator-initiate offloading in ultradense wireless networks. To enhance the mobile data offloading, a dynamic and self-adaptive method for network selection is proposed, using the attractor selection mechanism described in biological system. In our proposed algorithm, the operator enables users to dynamically select an appropriate APs according to the dynamic conditions of various available networks. Simulation results show that the proposed algorithm decreases the service delay and achieve a high offloading efficiency in delay offloading.

Stochastic-Geometry-Based Performance Analysis of Delayed Mobile Data Offloading With Mobility Prediction in Dense IEEE 802.11 Networks

Mobile data offloading through Wi-Fi is a promising solution to alleviate the explosive data increase in cellular network. While extensive attempts have been made at mobile data offloading, previous studies have rarely paid attention to network characteristics (e.g. Wi-Fi deployment density) and its effect on overall mobile offloading performance. In this paper, we propose a stochastic geometry model for the performance analysis of delay-tolerant offloading with mobility prediction. We aim to understand how the network parameters, such as network size and channel condition, affect the long-term offloading potential in the dense Wi-Fi sitting. The deployment of Wi-Fi is modeled as an independent Poisson point process (PPP) to take the effect of interference and CSMA/CA-based medium access control protocol into account. Then the semi-Markov process is used to model the user’s movement taking the sojourn time into account. Based on the PPP deployment and semi-Markov process, we can obtain the potential offloading traffic. Through the above proposed analytical studies, the network providers can easily obtain a rough estimation on the average offloading performance from a given dense network.

An Enhanced MAC Backoff Algorithm for Heavy User Loaded WLANs

As average user load in wireless local area network (WLAN) becomes heavy, the fundamental CSMA#x002F;CA mechanism based on binary exponential backoff (BEB) in the 802.11 protocol is under stress. When a large number of users associate with WLAN, the network suffers from severe throughput deterioration and poor short-term fairness due to the inappropriate BEB algorithm. In this paper, we provide an enhanced backoff (EBO) algorithm to improve the performance of WLANs. Our main motivation is based on the observation that disjointing backoff intervals in different backoff stage can greatly reduce the collision probability. In EBO, the size of backoff interval increases by the initial value of contention window after an unsuccessful transmission, and backoff intervals in different backoff stage are disjoint. Meanwhile, to improve the short-term fairness, we slow down the decrement of contention window by resetting the contention window to initial value after T consecutive successful transmissions. Simulation results show that EBO improves the throughput and short-term fairness effectively comparing with BEB in heavy user loaded WLANs.

Performance analysis of delayed mobile data offloading with multi-level priority

WiFi offloading is a cost-effective and practical solution to alleviate the problem of highly congested cellular networks. Recent theoretical and experimental studies show that delayed WiFi offloading where traffic can be delayed to increase the chance of meeting WiFi can significantly improve offloading efficiency. Nevertheless, there is no exact analytic model to analyze the offloading benefits with different types of traffic. In this paper, we propose a preemptive priority queuing analytic model for delayed offloading with multi-level priority traffic and derive expressions for the average delay and offloading efficiency of traffic with different priorities as a function of the WiFi availability, deadlines, and other key parameters. At last, we validate the accuracy of our queuing model by simulation in different scenarios and clarify how to choose a suitable deadline for traffic of different priorities.

Energy Efficient Channel Sharing and Power Optimization for Device-to-Device Networks

In device-to-device (D2D) networks, the system performance can be significantly improved with a well resource allocation scheme. In this paper, the issue of channel sharing and power allocation for device-to-device (D2D) communications underlaying cellular networks is considered. The users with the same service content are categorized into clusters, with clusters sharing the frequency of the uplink users. With this non-orthogonal frequency sharing, the energy efficiency of different type of users, i.e., the uplink users or the D2D users, is analysed. The energy-efficient resource sharing problem is further formulated into a non-transferable coalition formation game, and several related factors of the game is described. A distributed coalition formation game algorithm based on the merge and split rule is proposed. With numerical results, the effectiveness of the game model and the algorithm is demonstrated.

QoE‐based pseudo heartbeat message compression mechanism for future wireless network

Summary Wireless multimedia services are increasingly becoming popular, which boosts the need for better quality-of-experience (QoE). However, there are many aspects leading to the degradation of real-time video QoE, especially, a large number of always-on-line (AOL) applications existing in future wireless networks transmit heartbeat message periodically to keep always-on, and hence induce heavy signaling costs and overload wireless networks. In this paper, we propose QoE-based pseudo heartbeat message compression mechanism to reduce the number of signaling loads in the radio access network by intercepting the heartbeat message at a certain frequency in the proxy client. To maintain the protocol feature of the AOL applications, the heartbeat messages are reconstructed by the proxy server and sent to the application server. Furthermore, to analyze the influence of this mechanism on the video user, a new QoE perception model is proposed. Finally, combined the QoE perception model for video services with AOL services, the utility function for joint optimization multi-services is developed to determine the optimum compression frequency. The simulation results show that the proposed mechanism greatly alleviates the signaling load and leads to a significant performance improvement on the QoE of video users, while a slight decrease in the QoE of AOL users. Copyright