APPLICATIONS OF PARTICLE SWARM OPTIMIZATION IN WIRELESS COMMUNICATION SYSTEM

2020 ◽  
Vol 8 (4) ◽  
pp. 15
Author(s):  
THEOPHILUS ANIEMEKA ENEM ◽  
DEPO OLUKAYODE OYAJIDE ◽  
◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Communication ◽  
Communication System ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wireless Communication System

scholarly journals Atmospheric refractivity estimation from AIS signal power using the quantum-behaved particle swarm optimization algorithm

2019 ◽  
Vol 11 (1) ◽  
pp. 542-548
Author(s):  
Wenlong Tang ◽  
Hao Cha ◽  
Min Wei ◽  
Bin Tian ◽  
Xichuang Ren
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Communication System ◽  
Particle Swarm Optimization Algorithm ◽  
Estimation Method ◽  
Particle Swarm ◽  
Automatic Identification ◽  
Identification System ◽  
Signal Power ◽  
Swarm Optimization

Abstract This paper proposes a new refractivity profile estimation method based on the use of AIS signal power and quantum-behaved particle swarm optimization (QPSO) algorithm to solve the inverse problem. Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency mobile band and was developed primarily for collision avoidance. Since AIS is a one-way communication system which does not need to consider the target echo signal, it can estimate the atmospheric refractivity profile more accurately. Estimating atmospheric refractivity profiles from AIS signal power is a complex nonlinear optimization problem, the QPSO algorithm is adopted to search for the optimal solution from various refractivity parameters, and the inversion results are compared with those of the particle swarm optimization algorithm to validate the superiority of the QPSO algorithm. In order to test the anti-noise ability of the QPSO algorithm, the synthetic AIS signal power with different Gaussian noise levels is utilized to invert the surface-based duct. Simulation results indicate that the QPSO algorithm can invert the surface-based duct using AIS signal power accurately, which verify the feasibility of the new atmospheric refractivity estimation method based on the automatic identification system.

Accurate Detection of Fault Signal in Large-Scale Communication

2014 ◽  
Vol 989-994 ◽  
pp. 3802-3805
Author(s):  
Na Shu
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Detection ◽  
Signal Detection ◽  
Communication System ◽  
Communication Systems ◽  
Large Scale ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Mining Methods ◽  
Reaction Speed

Deep data-mining methods of fault signal in large-scale communication system are researched. Although with the characteristic of frequency uniformity as signals distribute in each reaction zone, common method of fault signal detection based on shortwave dispersing is invalid employing in large-scale communication system, which presents the absence or instability of fault signal. For this reason, a method based on particle swarm optimization is proposed for fault signal detection in large-scale communication system. As reaction speed and activity scope within the whole particle swarm are replaced, accurate results are achieved. Taking particle swarm optimization, it is detected that whether there is a fault in communication systems. The experimental results show that proposed method in signal fault detection process can greatly increase accuracy of signal fault detection, as plays a greater role in future.

Particle Swarm Optimization-Based Session Key Generation for Wireless Communication (PSOSKG)

2015 ◽  
pp. 640-677 ◽  
Author(s):  
Arindam Sarkar ◽  
Jyotsna Kumar Mandal
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Communication ◽  
Velocity Vector ◽  
Particle Swarm ◽  
Threshold Value ◽  
Key Generation ◽  
Session Key ◽  
Swarm Optimization ◽  
Minimum Probability ◽  
Velocity Level

In this chapter, a Particle Swarm Optimization-Based Session Key Generation for wireless communication (PSOSKG) is proposed. This cryptographic technique is solely based on the behavior of the particle swarm. Here, particle and velocity vector are formed for generation of keystream by setting up the maximum dimension of each particle and velocity vector. Each particle position and probability value is evaluated. Probability value of each particle can be determined by dividing the position of a particular particle by its length. If probability value of a particle is less than minimum probability value then a velocity is applied to move each particle into a new position. After that, the probability value of the particle at the new position is calculated. A threshold value is selected to evaluate against the velocity level of each particle. The particle having the highest velocity more than predefined threshold value is selected as a keystream for encryption.

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