Bandwidth Allocation for Handover calls in Mobile Wireless Cellular Networks – Genetic Algorithm Approach

1. Bandwidth Allocation for Handover calls in Mobile Wireless Cellular Networks – Genetic Algorithm Approach KhajaKamaluddin, AbdallaRadwan k_khaja@yahoo.com, Radwan2004@hotmail.com Computer Science Department Faculty of Science Sirte University Libya

2. Acknowledgements We are very much thankful to the management of Sirte University, Libya for supporting and facilitating in this research work. Our sincere thanks to reviewers for providing their valuable comments and suggestions. Sirte University, Libya

3. Objectives • Channel allocation using Genetic Algorithm • Channel allocation based upon fitness score • Minimum allocation in worst case. • Maximum bandwidth utilisation • Avoiding wastage of cell bandwidth. Sirte University, Libya

4. Problems • Cell size is being reduced • Frequent handovers take place • Demand for wireless connectivity is increased • Available resources are limited • Increase of Blocking/Dropping probability Sirte University, Libya

5. Existing solutions • Using Guard channels • Centralized channel allocation • Distributed dynamic channel allocation • Co operative non cooperative resource allocation • Proper utilization of available bandwidth • Utilization by accurate prediction • Online load balancing • Increase system utilization with degradation of QOS. Sirte University, Libya

6. Problems with existing solutions • Reduced dropping probability but wastage of resources in absence of calls • If central system fails whole network is in problem • Mobile tracking and prediction is always may not be correct. • Improper utilization of bandwidth • Degradation of QOS • Channels exhausted Sirte University, Libya

7. Channel Allocation by Genetic Algorithm Population Fitness Function Selection Crossover Mutation New Population Figure1. Genetic Algorithm Solution Sirte University, Libya

8. Generate population Defined Details Create random handover mobile nodes/calls and random time slots Fitness Score 11 Evaluate the fitness Fully utilized Previously used time slot duration full or partial or slot not used 3 10 Partially utilized 2 Selection Random number generation, assignment and ascending order. 01 Not utilized 1 00 Bottlenecked 0 Crossover Node + time slot Mutation Change in time slot duration Elitism Allocate Requested New population Time slot allocated nodes, empty slots if any Proposed System Model Sirte University, Libya

9. Proposed Solution • Bandwidth allocation – GE Approach • Population of chromosomes – Handover calls & Time slots • Genes – Bandwidth requirement (Time slots) • Fitness Value – Previous History of the Call Sirte University, Libya

10. Initialise Population Crossover Fitness Function Selection Discard Elitism Mutation New Fitness Score New Population Figure2. Modified Genetic Algorithm Genetic Bandwidth Allocation Sirte University, Libya

11. Crossover Operation M = {m1, m2, m3, …} ---------------- (1) T = {t1, t2, t3, ..} ------------------------(2) B1 = (∑T)/M -----------------------------(3) Fitness Function Evaluation Fitness Score 3 (Fully utilised BW) --- GROUP – I Fitness Score 2 (Partially utilised BW) ---- GROUP – II Fitness Score 1 & 0 ------ Discarded calls f(Group) = Fitness (Score) Sirte University, Libya

12. Generate of Population • M is set of Randomly generated nodes • M = {1m, 2m, 3m, ……..} • M = {m│m εM} • T is set of randomly generated time slots • T = {t1, t2, t3, ………….} • M1 is set of calls with fitness score 3 • M2 is set of calls with fitness score 2 • M1 εM and M2 εM • M1 = {m | m is Group1call}. • M2 = {m | 0 ≤ Group2 call ≤ t} Sirte University, Libya

13. Fitness score & Calls Arrangement • Fitness score identification • Random number generation • Random number assignment to calls • Arrangement of calls in ascending order based on random number. Sirte University, Libya

14. Selection Process M1 are allocated as per random number M2 are allocated as per random number Mutation M1 = Requested allocation M2 = Requested allocation || Minimum allocation Sirte University, Libya

15. Simulation Scenario • No. of Channels in Cell = 10 • IS – 136 TDMA system, • Each channel = 6 time slots. • Half rate TDMA • One slot per frame per customer is dedicated.   Sirte University, Libya

16. Simulation Scenario • Total Time slots = T • Total Calls = M • M = {M1} + {M2} • Bandwidth allocation for M1 calls = T1 slots • Bandwidth allocation for M2 calls = T2 slots • T2 = T – T1 • Bandwidth allocation for each M2 call = T2 = (T – T1)/M2   • First interval of time: Randomly generated calls and time slots. Fixed 0.1 unit of time slot is the minimum bandwidth Sirte University, Libya

17. Analytical Results • Bandwidth Allocation for all Calls • All handover calls are accommodated with minimum duration time slot. • Bandwidth Allocation for LFCs • Assuming that 20% - 30% are higher fitness value calls and remaining are lower fitness ones. Sirte University, Libya

18. Conclusions • Channel Allocation – Genetic Algorithm • Time slot Allocation – Fitness score • Higher fitness – Priority • Lower fitness – Minimum in worst-case • Maximum – Bandwidth utilization • Efficient – Bandwidth Management • Avoided – wastage of cell bandwidth • Minimum – call dropping Sirte University, Libya

19. Future Work • We are in the process of evaluating and monitoring the behavior, new fitness function and dropping probability for handover calls, which will be published later. Sirte University, Libya

20. Questions Sirte University, Libya

21. Thank you Sirte University, Libya