scholarly journals A New Iterated Local Search Algorithm for Solving Broadcast Scheduling Problems in Packet Radio Networks

2010 ◽  
Vol 2010 (1) ◽  
Author(s):  
Chih-Chiang Lin ◽  
Pi-Chung Wang
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Iterated Local Search ◽  
Radio Networks ◽  
Broadcast Scheduling ◽  
Local Search Algorithm ◽  
Scheduling Problems ◽  
Packet Radio Networks ◽  
Packet Radio

Broadcast scheduling in packet radio networks using Harmony Search algorithm

2012 ◽  
Vol 39 (1) ◽  
pp. 1526-1535 ◽  
Author(s):  
Imtiaz Ahmad ◽  
Mohammad Gh. Mohammad ◽  
Ayed A. Salman ◽  
Suha A. Hamdan
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Radio Networks ◽  
Harmony Search Algorithm ◽  
Broadcast Scheduling ◽  
Packet Radio Networks ◽  
Packet Radio

scholarly journals DOA Estimation in Non-Uniform Noise Based on Subspace Maximum Likelihood Using MPSO

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1429
Author(s):  
Jui-Chung Hung
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Doa Estimation ◽  
Iterated Local Search ◽  
Local Search Algorithm ◽  
Noise Variance ◽  
Swarm Optimization ◽  
Noise Effects ◽  
Low Snr ◽  
Uniform Noise

In general, the performance of a direction of arrival (DOA) estimator may decay under a non-uniform noise and low signal-to-noise ratio (SNR) environment. In this paper, a memetic particle swarm optimization (MPSO) algorithm combined with a noise variance estimator is proposed, in order to address this issue. The MPSO incorporates re-estimation of the noise variance and iterated local search algorithms into the particle swarm optimization (PSO) algorithm, resulting in higher efficiency and a reduction in non-uniform noise effects under a low SNR. The MPSO procedure is as follows: PSO is initially utilized to evaluate the signal DOA using a subspace maximum-likelihood (SML) method. Next, the best position of the swarm to estimate the noise variance is determined and the iterated local search algorithm to reduce the non-uniform noise effect is built. The proposed method uses the SML criterion to rebuild the noise variance for the iterated local search algorithm, in order to reduce non-uniform noise effects. Simulation experiments confirm that the DOA estimation methods are valid in a high SNR environment, but in a low SNR and non-uniform noise environment, the performance becomes poor because of the confusion between noise and signal sources. The proposed method incorporates the re-estimation of noise variance and an iterated local search algorithm in the PSO. This method is effectively improved by the ability to reduce estimation deviation in low SNR and non-uniform environments.

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