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Channel Allocation in Cellular Networks

Channel Allocation in Cellular Networks. Heuristic Methods Jie Chen David Seah Wen Xu. Overview. The Channel Allocation Problem Three Heuristic Algorithms Simulated Annealing Genetic Algorithm Tabu Search Comparison and Observations Conclusion. The Problem.

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Channel Allocation in Cellular Networks

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  1. Channel Allocation in Cellular Networks Heuristic Methods Jie Chen David Seah Wen Xu

  2. Overview • The Channel Allocation Problem • Three Heuristic Algorithms • Simulated Annealing • Genetic Algorithm • Tabu Search • Comparison and Observations • Conclusion

  3. The Problem • Channel Allocation Problem (CAP) • Traffic Vector • T = [32, 26, 14, 32, 18, 20, 14] • Decision Variable

  4. The Problem • Interference Matrix • Co-channel constraint • Co-cell constraint 2 1 0 0 0 0 0 1 2 1 1 1 0 0 0 1 2 0 1 0 0 I = 0 1 0 2 1 1 0 0 1 1 1 2 1 1 0 0 0 1 1 2 1 0 0 0 0 1 1 2 • Cost function

  5. The Problem • Neighborhood definitions • Random Selection • Cycling • Probability-based • GSAT-like

  6. Simulated Annealing • Neighborhood Definition • Random Selection • Initial Temperature • Pinitial = exp(-Δ cost / T0 ) • T0=k σ, where k= - 3/(ln Pinitial) • M, b, a

  7. Genetic Algorithm • Fitness Mapping • Selection • Crossover • Mutation • Standard • Greedy • Population Size • Elitism

  8. Tabu Search • Neighborhood definition • GSAT-like • Tabu Restriction • A swap pair changes from (0,1) to (1,0) • A swap pair changes from (1,0) to (0,1) • At least one of the above occurs • Tabu Tenure & Candidate List Size

  9. Tabu Search • Memory usage • Short term • Aspiration Criterion • Global Aspiration by Objective • Tabu list data structure • Three-dimensional 7*50*50 array

  10. Cost Eval. SA GA TS GS Avg. Best Cost 10,000 81.3 114.13 81.6 90.5 50,000 77.5 87.38 77 89 100,000 76.8 84.25 76.6 89 Std 10,000 1.49 3.44 1.27 4.68 50,000 0.85 2.22 0.95 3.29 100,000 1.32 2.15 0.84 3.29 Best Cost Found 75 78 75 85 Comparisons / Observations • Results

  11. Comparisons / Observations • Results

  12. Comparisons / Observations • Result Summary • SA and TS performed comparably and better than GA • GA performed better than Greedy Search (GS)

  13. Comparisons / Observations • How good are the results acquired? • Compared to GS and RGS • What is the global optimum? • Lower bound is 63 • How many users can actually be supported? • Upper bound is 133 • Lower bound is 130 (priority-based optimization method)

  14. Conclusion • Three major heuristic methods (SA, GA, TS) have been applied to CAP for cellular networks. • TS and SA can achieve good performance, but GA performs worse. • A simple priority-based optimization method is presented to find the optimal channel assignment.

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