Multicriteria Queuing Model to Improve Intra-User Multi-Flow QoS in Wireless Cellular Networks

Multicriteria Queuing Model to Improve Intra-User Multi-Flow QoS in Wireless Cellular Networks

Mohamed Hanini (Hassan 1st University, Morocco), Abdelkrim Haqiq (Hassan 1st University, Morocco) and Amine Berqia (Mohammed V University, Morocco)
DOI: 10.4018/978-1-4666-4715-2.ch006
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This chapter tackles the challenging task of QoS evaluation and improvement in cellular networks. Initially, the authors provide a classification based on four criteria of the queuing models used in the literature. In a second step, an analysis of technologies shows that combining queue management and scheduling mechanisms may lead to improvement in QoS. Hence, the authors propose design of queuing models that meet all the classification criteria, with the goal of improving the various QoS parameters required by the different classes of flows (real time and non-real time) transmitted to an end user in a cell. These models have been mathematically and numerically analyzed, and their impact on end-to-end QoS in a mobile cellular network has been proved.
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1. Introduction

The evolution of wireless telecommunication networks has experienced during the last decade an unprecedented development. From the third generation, this development has achieved considerable jumps either on bandwidth or on the types of offered services. However, this multitude of services poses incessantly the problem of quality of service (QoS), especially with the increased demand for these services.

Evaluation and improvement of performance in wireless cellular networks is a challenge for researchers. This research topic is approached in two complementary ways:

  • The analysis of technologies to identify needs and opportunities that could lead to proposals for methods and mechanisms to provide a more QoS level.

  • The analysis of the formal models that can represent these methods and evaluate their impact on network performances. In this perspective the theory of queues is a powerful tool.

The main objective of this chapter is to propose models for managing queue to improve performance for a heterogeneous traffic (formed in real-time and non real time flows) to an end user in a cellular network. Moreover, the model studied is situated in relation to a classification of existing queue models in the literature.


2. Quality Of Service Managing In A Cellular Network

2.1 Real Time and Non Real Time Services Classification

In recent years, the performance of mobile cellular telecommunication networks have been growing continuously by increasing the hardware capacity, and new generations of cellular networks offer more bandwidth resources. With this development, new generation of mobile wireless networks have to support multimedia applications that generate traffic having diverse QoS requirements.

We assume that we can always improve performance and guarantee the QoS by increasing microprocessor speed or even building specialized hardware to replace inefficient software components. However, for a predetermined set of available resources, it becomes important to take additional measures to effectively implement the QoS in network.

Typically service classification in new generations of cellular networks is based on the two parameters: delay and packet loss. The UMTS standard, for example, defines four QoS classes (Sanchez & Thioune, 2004) which are the conversational class, streaming class, interactive class and background class. These classes mainly differ in the way they balance between the transmission delay and reliability.

When classifying services according to their delivery requirements, the concept of Real Time (RT) and Non Real Time (NRT) services is introduced. Usually, RT services have been considered to impose strict delay requirements on the end to end communication. As a result, the involved network nodes in the RT traffic have to transfer the packets within a maximum tolerable delay. Due to these severe delay constraints, the error correction possibilities of this type of communication are very limited. On the other hand, NRT traffic is commonly considered as error sensitive, though with less demanding delay constraints than RT traffic. These characteristics of NRT traffic allow for link and also end-to-end level recovery mechanisms, enabling an error free delivery of the payload.

The above mentioned QoS classes in UMTS can be grouped by these two categories. Namely, the conversational and streaming traffic can be identified with RT services, whereas the interactive and background services belong to a NRT traffic pattern. For clarity and structuring purposes, this paper distinguishes between NRT and RT traffic. NRT (i.e. best effort) services require payload to be transferred error free, whereas delay requirements still allow for end-to-end error recovery mechanisms such as carried out by TCP. In contrast, RT services have delay requirements which exclude end-to-end retransmission mechanisms. Hence, they are using unreliable transport protocols like the UDP.

This classification into real time (RT) and non real time traffic can be adopted in all wireless cellular networks.

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