Bandwidth Allocation for Handover calls in Mobile Wireless Cellular Networks – Genetic Algorithm Approach Khaja Kamaluddin, Abdalla Radwan Computer Science Department Faculty of Science Sirte University Libya

Khaja.K ACIT – 2010 Sirte University, Libya 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.

Khaja.K ACIT – 2010 Sirte University, Libya 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.

Khaja.K ACIT – 2010 Sirte University, Libya 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

Khaja.K ACIT – 2010 Sirte University, Libya Existing solutions 1. Using Guard channels 2. Centralized channel allocation 3. Distributed dynamic channel allocation 4. Co operative non cooperative resource allocation 5. Proper utilization of available bandwidth 6. Utilization by accurate prediction 7. Online load balancing 8. Increase system utilization with degradation of QOS.

Khaja.K ACIT – 2010 Sirte University, Libya 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

Khaja.K ACIT – 2010 Sirte University, Libya Solution Channel Allocation by Genetic Algorithm Fitness Function Population Selection Crossover Mutation New Population Figure1. Genetic Algorithm

Khaja.K ACIT – 2010 Sirte University, Libya Proposed System Model Generate population Create random handover mobile nodes/calls and random time slots Evaluate the fitness Previously used time slot duration full or partial or slot not used SelectionRandom number generation, assignment and ascending order. CrossoverNode + time slot MutationChange in time slot duration ElitismAllocate Requested New populationTime slot allocated nodes, empty slots if any DefinedDetailsFitness Score 11Fully utilized3 10Partially utilized2 01Not utilized1 00Bottlenecked0

Khaja.K ACIT – 2010 Sirte University, Libya 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

Khaja.K ACIT – 2010 Sirte University, Libya Genetic Bandwidth Allocation Initialise Population Crossover Fitness Function Elitism Selection Mutation New Fitness Score New Population Discard Figure2. Modified Genetic Algorithm

Khaja.K ACIT – 2010 Sirte University, Libya 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 & Discarded calls f(Group) = Fitness (Score)

Khaja.K ACIT – 2010 Sirte University, Libya Generate of Population M is set of Randomly generated nodes M = {1m, 2m, 3m, ……..} M = {mm ε M} 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 ε M and M2 ε M M1 = {m | m is Group1call}. M2 = {m | 0 Group2 call t}

Khaja.K ACIT – 2010 Sirte University, Libya 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.

Khaja.K ACIT – 2010 Sirte University, Libya 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

Khaja.K ACIT – 2010 Sirte University, Libya 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.

Khaja.K ACIT – 2010 Sirte University, Libya 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

Khaja.K ACIT – 2010 Sirte University, Libya 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.

Khaja.K ACIT – 2010 Sirte University, Libya 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

Khaja.K ACIT – 2010 Sirte University, Libya 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.

Khaja.K ACIT – 2010 Sirte University, Libya Questions

Khaja.K ACIT – 2010 Sirte University, Libya Thank you