A Distributed Dynamic Channel Allocation Scheme in Cellular Communication Networks

This article presents a description and performance evaluation of an efficient distributed dynamic channels allocation (DDCA) scheme, which can be used for channel allocation in cellular communication networks (CCNs), such as the global system for mobile communication (GSM). The scheme utilizes a well-known distributed artificial intelligence (DAI) algorithm, namely, the asynchronous weak-commitment (AWC) algorithm, in which a complete solution is established by extensive communication among a group of neighbouring 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 simulations. introDuction A number of techniques have been developed to combat impairments in rapidly varying radio channels and to obtain high spectral efficiencies in CCNs. Some of those are channel coding and interleaving, adaptive modulation, transmitterreceiver antenna diversity, spectrum spreading, and dynamic channel allocation (DCA) (Kostic 2002, Kostic 2001). In contrast to static channel allocation (SCA) schemes, DCA schemes provide better utilization of the channels at higher traffic loads albeit at the cost of higher acquisition time and some additional control messages. There are mainly two types of DCA schemes, these are: centralized DCA (CDCA) and distributed DCA (DDCA) (Modi 2001). In this article, we develop and evaluate the performance of a DDCA scheme that convenes all requirements and constraints imposed by the 1648 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 1649 A Distributed Dynamic Channel Allocation Scheme in Cellular Communication Networks user, service providers, and technology of CCNs. The scheme is based on the AWC algorithm, which is, in turn, based on a formalism that is widely used for various application problems in DAI, called distributed constraint satisfaction problem (DCSP) (Yokoo 2000, Yokoo 1998, Yokoo 1994). In order to minimize the data communication overheads of the AWC algorithm, a token-based mechanism is introduced, in which a token is circulating between a group of collaborative cells passing information about the channels that are allocated or in operation within each cell. In addition, the token controls the channels allocation process by only allowing the cell that hold the token to update its channels. The algorithm is characterized by minimizing: channel acquisition time, number of denied or failed calls, control message complexity, communication overheads, and network interruption. The rest of this article is organized as follows. The following section presents a literature review that summarizes the most recent and related work. An introduction to the DCA techniques and to the minimum interference constraints in CCN are given in the respective following sections. Description of the ACW algorithms is presented, followed by the description of the new proposed DDCA scheme. Definitions of parameters that are used in evaluating the performance of the new scheme are also given. The simulations that were performed and the results obtained are described and discussed in a separate section. Finally, conclusions are drawn and recommendations for future work are pointed-out.