MiCi: A Novel Micro-Level Temporal Channel Imploration for Mobile Hosts

Abstract

– The exponential increase of multimedia services by the mobile users requires seamless connectivity with cost-effective Quality of Service (QoS) provisioning. For providing such on-demand QoS, the network needs to utilize the radio channels among the Mobile Hosts (MHs) effectively. We use vector genetic algorithm (VGA) for temporal imploration of sharable channel(s) from the neighbouring cell(s) to fulfill the needs of a cell. We propose a new micro-level temporal channel imploration mechanism (MiCi), which promptly allocates available borrowing channel(s) of the neighbouring cell(s) to the needy cell. The novelty of MiCi is scalability, high availability, and on-demand allocation of the channels to the desired cells. The performance of our model has been tested by simulation against a standard FCA scheme as well as a Greedy Borrowing Heuristic. In all the test cases MiCi shows promising results in comparison to both the schemes. 1. INTRODUCTION In a mobile cellular network each cell is assigned a set of channels to provide services to the individual MHs. The rapidly increasing demand of multimedia services with on-demand QoS provisioning to the MHs has become a major concern of mobile network designates [1]. The Channel Assignment Problem (CAP) concerns with allocation of available channels among the cells, so as to optimize the channel utilization. Since no adjacent cells can share any channel, CAP can be shown to be equivalent to graph-colouring problem, which is known to be NP-complete [2]. Obtaining an optimal solution for a large search space is impractical due to exponential time complexity. As a result, most of the proposed CAP in the literature is either based on some heuristics [3-5] or some evolutionary approach [6-14]. An inherent demerit of neural networks is convergence to local optima [13] and simulated annealing though guarantees global optimal solution, but suffers from a slow convergence rate [14]. GA-based approaches have been acknowledged to provide near optimal global solution with faster rate of convergence [6-11]. Several researchers have used different GA-based approaches [6-11] on macro-level channel allocation and compared their performance with Fixed Channel Allocation (FCA) [7] and some heuristic methods [6] with different network parameters. However, none of the discussed methods have so far focused on micro-level channel allocation to the cells. Our proposed model Micro-level Channel imploring (MiCi) borrows channel(s) from the neighbouring cell(s) by temporal requesting to the cells in which sharable channels are available. It exploits the inherent potential of GA with the …

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