Telecommunications Optimization: Heuristic and Adaptive Computation Techniques
Telecommunications Optimization: Heuristic and Adaptive Computation Techniques
IEEE Transactions on Parallel and Distributed Systems
A location-based business service model for mobile commerce
International Journal of Mobile Communications
Key issues for the design and development of mobile commerce services and applications
International Journal of Mobile Communications
A novel location management in IP-based cellular networks
International Journal of Mobile Communications
Mobile communications: global trends in the 21st century
International Journal of Mobile Communications
Location management for wireless networks: issues and directions
International Journal of Mobile Communications
A meta-analysis of Mobile Commerce Research in China (2002 2006)
International Journal of Mobile Communications
Service model and resource allocation scheme for multimedia traffic in 3G wireless systems
International Journal of Mobile Communications
Call admission control for multitier networks with integrated voice and data services
International Journal of Mobile Communications
Multi-component multimedia resource optimisation for 3G and beyond
International Journal of Internet Protocol Technology
The E3 architecture: enabling future cellular networks with cognitive and self-x capabilities
International Journal of Network Management
Particle swarm intelligence for channel assignment problem in mobile cellular communication system
International Journal of Artificial Intelligence and Soft Computing
A study of multi-hop cellular networks
Wireless Communications & Mobile Computing
Metaheuristic Channel Assignment in DVB-T Networks in Conformity with Digital Dividend Requirements
Wireless Personal Communications: An International Journal
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The Channel Assignment Problem is an NP-complete problem to assign a minimum number of channels under certain constraints to requested calls in a cellular radio system. Examples of the many approaches to solve this problem include using neural-networks, simulated annealing, graph colouring, genetic algorithms, and heuristic searches. We present a new heuristic algorithm that consists of three stages: 1) determine-lower-bound cell regular interval assignment; 2) greedy region assignment; and 3) genetic algorithm assignment. Through simulation, we show that our heuristic algorithm achieves lower bound solutions for 11 of the 13 instances of the well known Philadelphia benchmark problem. Our algorithm also has the advantage of being able to find optimum solutions faster than existing approaches that use neural networks.