OPTIMAL RESOURCE ALLOCATION IN MACROCELL-FEMTOCELL NETWORK USING BACTERIAL FORAGING OPTIMIZATION ALGORITHM

HENG, LALIN, I Wayan Mustika and Noor Akhmad Setiawan

2017 | Tesis | S2 Teknik Elektro

Femtocell network has emerged as the promising, energy-efficient and cost-effective communication technology and femtocell symbolizes the terrific solution to bolster network coverage and capacity in next-generation wireless communication. Despite promising features of next-generation wireless communication, femtocell terribly suffers from a wide variety of great challenges, such as interference both femtocell-to-femtocell and macrocell-to-femtocell interference, arisen from the employment of spectrum reuse technique. Thus, it is possible that the resource block of one user might fairly or severely overlap that of the other users in different FBSs. Nevertheless, the optimal solution to the interference problem can be achieved by searching for the most appropriate combination of resource blocks allocated for each femto user equipment (FUE). In this research, we address the problem of interference by allocating the most appropriate resource blocks based on the Bacterial Foraging Optimization (BFO). In this research, we propose the discretely modified BFO algorithm called DBFO to maximize SINR value, increasing system performance as the interference can be reduced significantly due to appropriate resource block allocation. The simulation results show that the proposed algorithm performs more significantly in comparison to the random resource allocation. The cumulative distribution function of interference, throughput and SINR shows that DBFO improves the over performance of femtocell network. When compared to DPSO, the results also illustrate that DBFO can improve the system performance when there are less chemotaxis steps or steps taken by each particle or bacterium. However, with more chemotaxis steps or steps taken by each particle or bacterium, DPSO can increase the performance slightly better. We can conclude that there is the trade-off between these two algorithms because DBFO can produce the significant results with less computation time while DPSO can do better while consuming more time to compute. In conclusion, DBFO is capable of enhancing the fitness value (SINR) of femtocell network and significantly mitigate the interference under certain number of chemotactic steps in comparison to DPSO.

Femtocell network has emerged as the promising, energy-efficient and cost-effective communication technology and femtocell symbolizes the terrific solution to bolster network coverage and capacity in next-generation wireless communication. Despite promising features of next-generation wireless communication, femtocell terribly suffers from a wide variety of great challenges, such as interference both femtocell-to-femtocell and macrocell-to-femtocell interference, arisen from the employment of spectrum reuse technique. Thus, it is possible that the resource block of one user might fairly or severely overlap that of the other users in different FBSs. Nevertheless, the optimal solution to the interference problem can be achieved by searching for the most appropriate combination of resource blocks allocated for each femto user equipment (FUE). In this research, we address the problem of interference by allocating the most appropriate resource blocks based on the Bacterial Foraging Optimization (BFO). In this research, we propose the discretely modified BFO algorithm called DBFO to maximize SINR value, increasing system performance as the interference can be reduced significantly due to appropriate resource block allocation. The simulation results show that the proposed algorithm performs more significantly in comparison to the random resource allocation. The cumulative distribution function of interference, throughput and SINR shows that DBFO improves the over performance of femtocell network. When compared to DPSO, the results also illustrate that DBFO can improve the system performance when there are less chemotaxis steps or steps taken by each particle or bacterium. However, with more chemotaxis steps or steps taken by each particle or bacterium, DPSO can increase the performance slightly better. We can conclude that there is the trade-off between these two algorithms because DBFO can produce the significant results with less computation time while DPSO can do better while consuming more time to compute. In conclusion, DBFO is capable of enhancing the fitness value (SINR) of femtocell network and significantly mitigate the interference under certain number of chemotactic steps in comparison to DPSO.

Kata Kunci : Femtocell, SINR, Bacterial Foraging Optimization