Modeling of cellular communication networks with heterogeneous traffic sources

Author(s):  
S. Tekinay ◽  
B. Jabbari ◽  
A. Kakaes
Author(s):  
Hussein Al-Bahadili ◽  
Arafat Abu Mallouh

This chapter presents a description and performance evaluation of an efficient Distributed Artificial Intelligence (DAI) Dynamic Channels Allocation (DCA) scheme. Therefore, it is referred to as DAI-DCA scheme. It can be used for channel allocation in high traffic cellular communication networks (CCNs), such as the global system for mobile communication (GSM). The scheme utilizes a well-known DAI algorithm, namely, the asynchronous weak-commitment (AWC) algorithm, in which a complete solution is established by extensive communication among a group of neighboring collaborative cells forming a pattern, where each cell in the pattern uses a unique set of channels. To minimize communication overhead among cells, a token-based mechanism was introduced. The scheme achieved excellent average allocation efficiencies of over 85% for a number of realistic operation scenarios.


2005 ◽  
Vol 5 (2) ◽  
pp. 209-217 ◽  
Author(s):  
Ali F. Almutairi ◽  
Awatef K. Ali ◽  
Mehmet Hakan Karaata

Author(s):  
Lain-Chyr Hwang ◽  
San-Yuan Wang

In the multimedia communication networks providing quality of service (QoS), an interface between the signal processing systems and the communication systems is the call admission control (CAC) mechanism. Owing to the heterogeneous traffic produced by diverse signal processing systems in a multimedia communication network, the traditional CAC mechanism that used only one CAC algorithm can no longer satisfy the aim of QoS CAC: Utilize the network resource to the most best and still satisfy the QoS requirements of all connections. For satisfying the aim of QoS CAC in the multimedia communication networks, this study proposed an innovative CAC mechanism called black and white CAC (B&W CAC), which uses two CAC algorithms. One of them is called black CAC controller and is used for the traffic with specifications more uncertain, which is called black traffic here. The other is call white CAC controller and is for the traffic with clearer specifications, which is call white traffic. Because white traffic is simple, an equivalent bandwidth CAC is taken as the white CAC. On the other hand, a neural network CAC (NNCAC) is employed to be the black CAC to overcome the uncertainty of black traffic. Furthermore, owing to more parameters needed in a QoS CAC mechanism, a hierarchical NNCAC is proposed instead of the common used NNCAC. Besides to accommodate more parameters, a hierarchical NNCAC can keep the complexity low. The simulation results show the B&W CAC can obtain higher utilization and still meet the QoS requirements of traffic sources.


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