Distributed Learning Strategies for Interference Mitigation in Femtocell Networks

2011 ◽  
Author(s):  
M. Bennis ◽  
S. Guruacharya ◽  
D. Niyato
Keyword(s):  
Learning Strategies ◽  
Interference Mitigation ◽  
Distributed Learning ◽  
Femtocell Networks

scholarly journals A vignette model for distributed teaching and learning

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
Marcel Chaloupka ◽  
Tony Koppi
Keyword(s):  
Learning Strategies ◽  
Teaching And Learning ◽  
Best Practice ◽  
Distributed Learning ◽  
The Internet ◽  
Legacy Systems ◽  
Full Potential ◽  
Use Of The Internet ◽  
Good Learning ◽  
Business Sectors

The notion of convergence of disparate technologies has become popular with governments, computing and business sectors in the 1990s; but how has the convergence been implemented in the educational sector? One evident area of convergence in education has been the use of the Internet. But according to Gosper et al (1996), the most likely strategies for implementation are to use the Internet as a repository of reference, lecture materials and the presentation of the lectures. This could imply that the full potential of distributed learning through convergence might never be achieved. How can we implement good learning strategies following sound educational methodologies today, while not producing legacy systems or piecemeal content that could constrain future developments? In making it possible for distributed learning to occur, there are best-practice considerations applicable to most educational environments.DOI:10.1080/0968776980060107

On the development of cat swarm metaheuristic using distributed learning strategies and the applications

2019 ◽  
Vol 12 (2) ◽  
pp. 224-244 ◽  
Author(s):  
Usha Manasi Mohapatra ◽  
Babita Majhi ◽  
Alok Kumar Jagadev
Keyword(s):  
Nonlinear Systems ◽  
Evolutionary Algorithms ◽  
Learning Strategies ◽  
Input Data ◽  
Distributed Learning ◽  
Identification Problem ◽  
Population Based ◽  
Communication Overhead ◽  
Swarm Optimization ◽  
Content Type

Purpose The purpose of this paper is to propose distributed learning-based three different metaheuristic algorithms for the identification of nonlinear systems. The proposed algorithms are experimented in this study to address problems for which input data are available at different geographic locations. In addition, the models are tested for nonlinear systems with different noise conditions. In a nutshell, the suggested model aims to handle voluminous data with low communication overhead compared to traditional centralized processing methodologies. Design/methodology/approach Population-based evolutionary algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and cat swarm optimization (CSO) are implemented in a distributed form to address the system identification problem having distributed input data. Out of different distributed approaches mentioned in the literature, the study has considered incremental and diffusion strategies. Findings Performances of the proposed distributed learning-based algorithms are compared for different noise conditions. The experimental results indicate that CSO performs better compared to GA and PSO at all noise strengths with respect to accuracy and error convergence rate, but incremental CSO is slightly superior to diffusion CSO. Originality/value This paper employs evolutionary algorithms using distributed learning strategies and applies these algorithms for the identification of unknown systems. Very few existing studies have been reported in which these distributed learning strategies are experimented for the parameter estimation task.

Development of robust distributed learning strategies for wireless sensor networks using rank based norms

2014 ◽  
Vol 101 ◽  
pp. 218-228 ◽  
Author(s):  
Upendra Kumar Sahoo ◽  
Ganapati Panda ◽  
Bernard Mulgrew ◽  
Babita Majhi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Learning Strategies ◽  
Distributed Learning ◽  
Wireless Sensor
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