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

Elastic calls in an integrated services network: the greater the call size variability the better the QoS

We study a telecommunications network integrating prioritized stream calls and delay tolerant elastic calls that are served with the remaining (varying) service capacity according to a processor sharing discipline. The remarkable observation is presented and analytically supported that the expected elastic call holding time is decreasing in the variability of the elastic call size distribution. As a consequence, network planning guidelines or admission control schemes that are developed based on deterministic or lightly variable elastic call sizes are likely to be conservative and inefficient, given the commonly acknowledged property of e.g.\ \textsc{www}\ documents to be heavy tailed. Application areas of the model and results include fixed \textsc{ip} or \textsc{atm} networks and mobile cellular \textsc{gsm}/\textsc{gprs} and \textsc{umts} networks. \u

A Decision-Theoretic Approach to Resource Allocation in Wireless Multimedia Networks

The allocation of scarce spectral resources to support as many user applications as possible while maintaining reasonable quality of service is a fundamental problem in wireless communication. We argue that the problem is best formulated in terms of decision theory. We propose a scheme that takes decision-theoretic concerns (like preferences) into account and discuss the difficulties and subtleties involved in applying standard techniques from the theory of Markov Decision Processes (MDPs) in constructing an algorithm that is decision-theoretically optimal. As an example of the proposed framework, we construct such an algorithm under some simplifying assumptions. Additionally, we present analysis and simulation results that show that our algorithm meets its design goals. Finally, we investigate how far from optimal one well-known heuristic is. The main contribution of our results is in providing insight and guidance for the design of near-optimal admission-control policies.Comment: To appear, Dial M for Mobility, 200

A connection-level call admission control using genetic algorithm for MultiClass multimedia services in wireless networks

Call admission control in a wireless cell in a personal communication system (PCS) can be modeled as an M/M/C/C queuing system with m classes of users. Semi-Markov Decision Process (SMDP) can be used to optimize channel utilization with upper bounds on handoff blocking probabilities as Quality of Service constraints. However, this method is too time-consuming and therefore it fails when state space and action space are large. In this paper, we apply a genetic algorithm approach to address the situation when the SMDP approach fails. We code call admission control decisions as binary strings, where a value of “1” in the position i (i=1,…m) of a decision string stands for the decision of accepting a call in class-i; a value of “0” in the position i of the decision string stands for the decision of rejecting a call in class-i. The coded binary strings are feed into the genetic algorithm, and the resulting binary strings are founded to be near optimal call admission control decisions. Simulation results from the genetic algorithm are compared with the optimal solutions obtained from linear programming for the SMDP approach. The results reveal that the genetic algorithm approximates the optimal approach very well with less complexity