scholarly journals A Genetic Algorithm-based Framework for Soft Handoff Optimization in Wireless Networks

2017 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Daniel E. Asuquo ◽  
Samuel A. Robinson
Keyword(s):  
Genetic Algorithm ◽  
Wireless Networks ◽  
Network Performance ◽  
Heterogeneous Wireless Networks ◽  
Success Probability ◽  
Mobile Terminal ◽  
Soft Handoff ◽  
Global Optima ◽  
Promising Solution ◽  
The One

In this paper, a genetic algorithm (GA)-based approach is used to evaluate the probability of successful handoff in heterogeneous wireless networks (HWNs) so as to increase capacity and network performance. The traditional handoff schemes are prone to ping pong and corner effects and developing an optimized handoff scheme for seamless, faster, and less power consuming handoff decision is challenging. The GA scheme can effectively optimize soft handoff decision by selecting the best fit network for the mobile terminal (MT) considering quality of service (QoS) requirements, network parameters and user’s preference in terms of cost of different attachment points for the MT. The robustness and ability to determine global optima for any function using crossover and mutation operations makes GA a promising solution. The developed optimization framework was simulated in Matrix Laboratory (MATLAB) software using MATLAB’s optima tool and results show that an optimal MT attachment point is the one with the highest handoff success probability value which determines direction for successful handoff in HWN environment. The system maintained a 90%  with 4 channels and more while a 75% was obtained even at high traffic intensity.

scholarly journals Genetic Algorithm for Effective Optimization of Delay Performance in Wireless Sensor Networks

2018 ◽  
Vol 26 (10) ◽  
pp. 281-308
Author(s):  
Saif Khalid Musluh ◽  
Alaa Abid Muslam ◽  
Raid Abd Alreda Shekan
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Wireless Networks ◽  
Sensor Networks ◽  
Network Performance ◽  
Data Transfer ◽  
Relay Node ◽  
Network Capacity ◽  
Wireless Sensor ◽  
Delay Performance

Wireless sensor networks (WSNs) play an important role in many real-world applications like surveillance. Wireless networks are also used to have data transfer. In such cases, there are problems with  resourcece-constraintnednetworks. The problems include a delay in communication and reduction in Quality of Service (QoS). Topology control can solve this problem to some extent. However, the delay performance and QoS need to be improved further to support intended operations in wireless networks. When relay node concept is considered, it is possible to optimize performance in such networks. In this paper, we proposed a Genetic Algorithm (GA) based relay configuration for optimizing delay performance in WSN. Relay nodes compute optimal positions using the proposed algorithm so as to improve QoS and reduce delay as much as possible. We implemented the algorithm using NS2 simulations. The results revealed that the proposed approach is able to improve QoS, reduce delay besides improving network performance in terms of throughput, network capacity, and energy efficiency.

scholarly journals Multi-Objective Tabu Search to Balance Multihoming Loads in Heterogeneous Wireless Networks

2018 ◽  
Vol 13 (6) ◽  
pp. 956-971 ◽  
Author(s):  
Jorge A. Huertas ◽  
Yezid Donoso
Keyword(s):  
Genetic Algorithm ◽  
Wireless Networks ◽  
Tabu Search ◽  
Mobile Devices ◽  
Heterogeneous Wireless Networks ◽  
Access Networks ◽  
Efficient Set ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Key Indicators

The advantages of the increasing usage of mobile devices that operate under the multihoming scheme are changing the communications world drastically. Therefore, next generation networks operators have the challenging task to distribute connections of mobile devices efficiently over their access networks, creating a big heterogeneous wireless network for telecommunications. We present a mixed integerlinear programming (MILP) model to balance the load of multiple services over wireless networks taking into account three key indicators: connection loads of access networks, connection costs, and battery consumption of connections. To solve the multi-objective problem, we propose a multi-objective Tabu Search procedure that is capable to find non-supported solutions in the online efficient set. To test the performance of our multi-objective Tabu Search, we tested it over four instances of the literature. In the first instance, a small instance, our procedure finds the true efficient set of solutions. For the other three instances, large instances with over a thousand mobile devices, our procedure finds good online efficient sets of solutions in less than 30 seconds. Finally, using appropriate multi-objective metrics, we compare the results of our multi-objective Tabu Search against the results of a state of the art multi-objective genetic algorithm in the literature for the same problem, outperforming the genetic algorithm in every instance tested.

scholarly journals Memory-Based LT Codes for Efficient 5G Networks and Beyond

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3169
Author(s):  
Khaled F. Hayajneh
Keyword(s):  
Error Probability ◽  
Success Probability ◽  
Low Complexity ◽  
The Other ◽  
5G Networks ◽  
Lt Codes ◽  
Channel Codes ◽  
Promising Solution ◽  
Fountain Code ◽  
The One

The next-generation networks (5G and beyond) require robust channel codes to support their high specifications, such as low latency, low complexity, significant coding gain, and flexibility. In this paper, we propose using a fountain code as a promising solution to 5G and 6G networks, and then we propose using a modified version of the fountain codes (Luby transform codes) over a network topology (Y-network) that is relevant in the context of the 5G networks. In such a network, the user can be connected to two different cells at the same time. In addition, the paper presents the necessary techniques for analyzing the system and shows that the proposed scheme enhances the system performance in terms of decoding success probability, error probability, and code rate (or overhead). Furthermore, the analyses in this paper allow us to quantify the trade-off between overhead, on the one hand, and the decoding success probability and error probability, on the other hand. Finally, based on the analytical approach and numerical results, our simulation results demonstrate that the proposed scheme achieves better performance than the regular LT codes and the other schemes in the literature.

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