Mobility Modeling, Prediction and Resource Allocation in Wireless Networks

Abstract

Rapid increase in the number of mobile and wireless users has created greater loads on legacy networks. Also, increased miniaturization of computing devices has led to an influx of handheld gadgets. Mobile users now wish to have seamless roaming across networks and not experience fluctuations in the quality of service. These wireless devices can overwhelm existing networks since these were traditionally not built to handle heavy user mobility across networks.

User mobility influences the performance seen by a mobile device in a wireless network. Prior knowledge of mobility patterns can be exploited to properly allocate network resources and enhance the performance and quality of service experienced by a mobile device for applications and services. Hence, mobility prediction plays an important role in the efficient operation of wireless networks such as WANs and WLANs. In this work we propose a probabilistic model to effectively predict user mobility using wireless trace sets using a Hidden Markov Model (HMM). Access to mobility related information such as user movement provides an opportunity for networks to efficiently manage resources to satisfy user needs. Also, mobility models for simulation and their performance analysis are investigated rigorously.

Adaptive network resource management based on user mobility can reduce network loads. Towards this goal, we propose a generic methodology based on a control theoretic framework. The feedback based controller effectively uses the predicted mobility to allocate resources effectively for mobile users. Incorporation of this engine in a control theoretic framework with feedback from an adaptive controller permits the efficient allocation of network resources to various applications. The effectiveness of the approach using a prediction engine based on HMM is evaluated through simulation experiments. All simulations use real-world wireless traces. The proposed framework is quite general and the HMM based engine can be replaced by other suitable models such as neural networks and results for these show that the framework is indeed modular as proposed.

Mobility also influences the interference seen by a mobile user. We study the effect of mobility on interference dynamics and the outage perceived by users in a cellular system. Due to the mobility of interfering nodes, the aggregate interference and its statistics are time-varying. Analytical results are obtained for interference statistics by using a Gaussian model and considering the effect of different mobility models. For this purpose, the cross-over probabilities of different edges of a cell and for different mobility models are obtained through simulation and used in the derivation of analytical results.

The main contribution of this dissertation is a generic framework for mobility prediction and resource management. A flexible and generic framework for mobility prediction and resource allocation allows for use of other techniques such as ARMA and machine learning in place of HMM and Neural Networks. The generic model can be used in various network applications such as QoS, seamless handover and jitter-free streaming applications.