Call Admission Control Using Bio-Geography Based Optimization

Call Admission Control Using Bio-Geography Based Optimization

Sanchita Ghosh (Department of Computer Science and Engineering, Birla Institute of Technology, Kolkata, India) and Amit Konar (Electronics and Telecommunication Engineering Department, Javadpur University, Kolkata, India)
Copyright: © 2015 |Pages: 23
DOI: 10.4018/IJAEC.2015010103
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The chapter proposes a new approach to call admission control in a mobile cellular network using Bio-geography based optimization. Existing algorithms on call admission control either ignore both variation in traffic conditions or velocity of mobile devices, or at most consider one of them. This chapter overcomes the above problems jointly by formulating call admission control as a constrained optimization problem, where the primary objective is to minimize the call drop under dynamic condition of the mobile stations, satisfying the constraints to maximize the channel assignment and minimize the dynamic traffic load in the network. The constrained objective function has been minimized using Bio-geography based optimization. Experimental results and computer simulations envisage that the proposed algorithm outperforms most of the existing approaches on call admission control, considering either of the two issues addressed above.
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1. Introduction

Call Admission Control (CAC) refers to the problem of efficient call management in a mobile cellular network. The primary objective of CAC is to serve as many calls as possible, and prevent dropping of calls in progress (Wang et al., 2002; Hou and Fang, 2001; Sakar and Sivarajan, 2002). Besides this, an efficient call management also aims at satisfying additional (secondary) objective to assign appropriate channels to the incoming/handoff calls, so that the necessary soft constraints for channel assignment are maintained (Wang et al., 2002; Hou and Fang, 2001; Sakar and Sivarajan, 2002; Wang et al., 2006; Chen et al., 2008; Thilakawardana et al., 2004; Kang and Sung, 2000; Xiao et al., 2000). Typically, soft constraints include co-channel, co-site, and adjacent channel constraints, all of which need to be satisfied to serve the secondary objective. In the current literature, Quality of Service (QoS) is often used to measure the quality of CAC with an attempt to maximize call assignment and soft handoff, satisfying the soft constraints. The better the QoS, the better is the CAC.

The concept of biogeography can be traced to the work of nineteenth century naturalists such as Alfred Wallace and Charles Darwin. Robert Macarthur and Edward Wilson began working together on mathematical models of biogeography in 1960. The primary objective was on the distribution of species among neighboring islands. The mathematical models were developed for the extinction and migration of species. The application of biogeography to engineering is similar to what has occurred in the past few decades with genetic algorithms (GA), neural networks, fuzzy logic, particle swarm optimization (PSO), and other areas of computational intelligence.

Biogeography-based optimization (BBO) rests on the migration strategy of animals to solve the problem of global optimization. In general, Biogeography is the study of the geographical distribution of biological organisms. Mathematical equations that govern the distribution of organisms were first discovered and developed during the 1960s. The researchers can learn from nature and it motivates the application of biogeography to optimization problems. This chapter considers the mathematics of biogeography as the basis for the development of a new field for admission of calls in a mobile network using the biogeography-based optimization (BBO).

The BBO migration strategy is similar to the global recombination approach of evolutionary strategies (ES) (Black, 1996), (Black et al., 1997), in which many parents can contribute to a single offspring. Global recombination has also been adapted to GA (Eiben, 2003), (Eiben, 2000), but BBO differs from GAs in one important aspect. In GA recombination is used to create new solutions, while in BBO migration is used to change existing solutions.

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