A Coxian Model for Channel Holding Time Distribution for Teletraffic Mobility Modelling

Published versio

Efficient resource allocation and call admission control in high capacity wireless networks

Resource Allocation (RA) and Call Admission Control (CAC) in wireless networks are processes that control the allocation of the limited radio resources to mobile stations (MS) in order to maximize the utilization efficiency of radio resources and guarantee the Quality of Service (QoS) requirements of mobile users. In this dissertation, several distributed, adaptive and efficient RA/CAC schemes are proposed and analyzed, in order to improve the system utilization while maintaining the required QoS. Since the most salient feature of the mobile wireless network is that users are moving, a Mobility Based Channel Reservation (MBCR) scheme is proposed which takes the user mobility into consideration. The MBCR scheme is further developed into PMBBR scheme by using the user location information in the reservation making process. Through traffic composition analysis, the commonly used assumption is challenged in this dissertation, and a New Call Bounding (NCB) scheme, which uses the number of channels that are currently occupied by new calls as a decision variable for the CAC, is proposed. This dissertation also investigates the pricing as another dimension for RA/CAC. It is proven that for a given wireless network there exists a new call arrival rate which can maximize the total utility of users, while maintaining the required QoS. Based on this conclusion, an integrated pricing and CAC scheme is proposed to alleviate the system congestion

Modeling and Analysis of Channel Holding Time and Handoff Rate for Packet Sessions in All-IP Cellular Networks

It is essential to model channel holding time (CHT), cell residence time (CRT), and handoff rate for performance analysis and algorithm evaluation in mobile cellular networks. The problem has been extensively studied in the past for circuit-switched (CS) cellular networks. However, little research has been done on packet-switched (PS) cellular networks. Unlike that a call occupies a dedicated channel during its whole lifetime in CS networks, an active session in PS networks occupies and releases channels iteratively due to discontinuous reception (DRX) mechanism. In this paper, we investigate the key quantities in PS cellular networks. We present a set of comprehensive new models to characterize the quantities and their relationship in PS networks. The models shed light on the relationship between CHT and CRT and handoff rate. The analytical results enable wide applicability in various scenarios and therefore have important theoretical significance. Moreover, the analytical results provide a quick way to evaluate traffic performance and system design in PS cellular networks without wide deployment, which can save cost and time

Discrete event simulation of wireless cellular networks

Postprint (published version

A New CAC Policy Based on Traffic Characterization in Cellular Networks

The Call Admission Control (CAC) method presented in this paper is based on the statistical properties of the network’s traffic variables. It probabilistically estimates the time until the release of a seized channel: the admission control depends on the computed mean remaining time averaged along all channels at a specific instant and on a time threshold. The policy produces a smooth transition between the QoS metrics, giving the operator the freedom to design the network at the desired QoS point. Another valuable property is that the algorithm is straightforward and fed only by simple teletraffic metrics: distribution and the first and second moments of Channel Holding Time (CHT). Simplicity is important for a CAC method because decisions for accepting or rejecting calls must be computed quickly and frequently.Peer Reviewe

MoMo: a group mobility model for future generation mobile wireless networks

Existing group mobility models were not designed to meet the requirements for accurate simulation of current and future short distance wireless networks scenarios, that need, in particular, accurate, up-to-date informa- tion on the position of each node in the network, combined with a simple and flexible approach to group mobility modeling. A new model for group mobility in wireless networks, named MoMo, is proposed in this paper, based on the combination of a memory-based individual mobility model with a flexible group behavior model. MoMo is capable of accurately describing all mobility scenarios, from individual mobility, in which nodes move inde- pendently one from the other, to tight group mobility, where mobility patterns of different nodes are strictly correlated. A new set of intrinsic properties for a mobility model is proposed and adopted in the analysis and comparison of MoMo with existing models. Next, MoMo is compared with existing group mobility models in a typical 5G network scenario, in which a set of mobile nodes cooperate in the realization of a distributed MIMO link. Results show that MoMo leads to accurate, robust and flexible modeling of mobility of groups of nodes in discrete event simulators, making it suitable for the performance evaluation of networking protocols and resource allocation algorithms in the wide range of network scenarios expected to characterize 5G networks.Comment: 25 pages, 17 figure

Mobility modeling and management for next generation wireless networks

Mobility modeling and management in wireless networks are the set of tasks performed in order to model motion patterns, predict trajectories, get information on mobiles\u27 whereabouts and to make use of this information in handoff, routing, location management, resource allocation and other functions. In the literature, the speed of mobile is often and misleadingly referred to as the level of mobility, such as high or low mobility. This dissertation presents an information theoretic approach to mobility modeling and management, in which mobility is considered as a measure of uncertainty in mobile\u27s trajectory, that is, the mobility is low if the trajectory of a mobile is highly predictable even if the mobile is moving with high speed. On the other hand, the mobility is high if the trajectory of the mobile is highly erratic. Based on this mobility modeling concept, we classify mobiles into predictable and non-predictable mobility classes and optimize network operations for each mobility class. The dynamic mobility classification technique is applied to various mobility related issues of the next generation wireless networks such as location management, location-based services, and energy efficient routing in multihop cellular networks