Channel Assignment for Cellular Radio Using Particle Swarm Optimization

2006 ◽  
Author(s):  
Hassan M. Elkamchouchi ◽  
Hassan M. Elragal ◽  
Mina A. Makar
Keyword(s):  
Particle Swarm Optimization ◽  
Channel Assignment ◽  
Particle Swarm ◽  
Cellular Radio ◽  
Swarm Optimization

scholarly journals NLOS Error Mitigation in Cellular Positioning using PSO Optimization Algorithm

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Stevo Lukić ◽  
Mirjana Simić
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Mobile Station ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Cellular Radio ◽  
Object Function ◽  
Swarm Optimization ◽  
Station Location

Non-Line-Of-Sight conditions pose a major challenge to cellular radio positioning. Such conditions, when the direct Line-Of-Sight path is blocked, result in additional propagation delay for the signal, additional attenuation, and an angular bias. Therefore,many researchers have proposed various algorithms to mitigate the measured error caused by this phenomenon. This paper presentsthe procedure for improving accuracy of determining the mobile station location in cellular radio networks in Non-Line-of-Sightpropagation environment, based on the Time Of Arrival oriented estimator using the Particle Swarm Optimization algorithm. Incomputer science, Particle Swarm Optimization is an evolutionary computational method that optimizes a problem by iteratively tryingto improve a candidate solution with regard to a given measure of quality. The proposed algorithm uses the repeating Time-Of-Arrivaltest measurements using the four base stations and for simulation selects the measurement combination that give the smallest regionenclosed by the overlap of four circles. In this way, the smallest intersect area of the four Time-Of-Arrival circles is obtained, andtherefore the smallest positioning error. After that, we consider the complete problem as a combinatorial optimization problem withthe corresponding object function that represents the nonlinear relationship between the intersection of the four circles and the mobilestation location. The Particle Swarm Optimization finds the optimal solution of the object function and efficiently determines themobile station location. The simulation results show that the proposed method outperforms conventional algorithms such as theWeighted Least Squares and the Levenberq-Marquardt method.

scholarly journals Whether and How to Select Inertia and Acceleration of Discrete Particle Swarm Optimization Algorithm: A Study on Channel Assignment

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Min Jin ◽  
Xiangyuan Zhong ◽  
Xudong Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Channel Assignment ◽  
Particle Swarm ◽  
Motion Equation ◽  
Experimental Result ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Acceleration Coefficient ◽  
Convergence Performance

There is recently a great deal of interest and excitement in understanding the role of inertia and acceleration in the motion equation of discrete particle swarm optimization (DPSO) algorithms. It still remains unknown whether the inertia section should be abandoned and how to select the appropriate acceleration in order for DPSO to show the best convergence performance. Adopting channel assignment as a case study, this paper systematically conducts experimental filtering research on this issue. Compared with other channel assignment schemes, the proposed scheme and the selection of inertia and acceleration are verified to have the advantage to channel assignment in three respects of convergence rate, convergence speed, and the independency of the quality of initial solution. Furthermore, the experimental result implies that DSPO might have the best convergence performance when its motion equation includes an inertia section in a less medium weight, a bigger acceleration coefficient for global-search optimum, and a smaller acceleration coefficient for individual-search optimum.

Partially Overlapped Channel Assignment Using Discrete Particle Swarm Optimization

2014 ◽  
Vol 614 ◽  
pp. 550-553
Author(s):  
Ji Hong Wang ◽  
Wen Xiao Shi ◽  
Ke Qiang Cui ◽  
Feng Jin
Keyword(s):  
Particle Swarm Optimization ◽  
Channel Assignment ◽  
Mesh Networks ◽  
Particle Swarm ◽  
Network Capacity ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Assignment Algorithm ◽  
Orthogonal Channels

Wireless mesh networks are multi-hop wireless networks whose capacity decreases very fast due to interference from parallel transmissions on the same channel when using orthogonal channels. In order to further eliminate network interference and improve network capacity, partially overlapped channels are used to perform channel assignment, and a discrete particle swarm optimization (DPSO) based channel assignment algorithm using partially overlapped channels is proposed in this paper. Channel assignment for all links is mapped to position of particle, and DPSO is used to evolve and produce better channel assignment solutions. Simulation results show that network performances can be dramatically improved by properly utilizing partially overlapped channels, for example, the average end-to-end delay and the average packet loss ratio both can be decreased by at least 15 percent, and the network throughput can be improved by 12 percent or more.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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