Distributed power control in femto cells using Bayesian density tracking

Hanif, Ahmed Farhan; Tembiné, Hamidou; Assaad, Mohamad; Zeghlache, Djamal

doi:10.1109/allerton.2013.6736689

2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton) 2013

DOI: 10.1109/allerton.2013.6736689

|View full text |Cite

Distributed power control in femto cells using Bayesian density tracking

Ahmed Farhan Hanif

Hamidou Tembiné

Mohamad Assaad

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2017

Publication Types

Select...

Other1

Relationship

Self Cite1

Independent0

Authors

Journals

Cited by 1 publication

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Distributed stochastic optimization in networks with low informational exchange

Assaad

Duhamel

2017

2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

Self Cite

View full text Add to dashboard Cite

We consider a distributed stochastic optimization problem in networks with finite number of nodes. Each node adjusts its action to optimize the global utility of the network, which is defined as the sum of local utilities of all nodes. Gradient descent method is a common technique to solve the optimization problem, while the computation of the gradient may require much information exchange. In this paper, we consider that each node can only have a noisy numerical observation of its local utility, of which the closed-form expression is not available. This assumption is quite realistic, especially when the system is too complicated or constantly changing. Nodes may exchange the observation of their local utilities to estimate the global utility at each timeslot. We propose stochastic perturbation based distributed algorithms under the assumptions whether each node has collected local utilities of all or only part of the other nodes. We use tools from stochastic approximation to prove that both algorithms converge to the optimum. The convergence rate of the algorithms is also derived. Although the proposed algorithms can be applied to general optimization problems, we perform simulations considering power control in wireless networks and present numerical results to corroborate our claim.

show abstract

Distributed stochastic optimization in networks with low informational exchange

Assaad

Duhamel

2017

2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

Self Cite

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Distributed power control in femto cells using Bayesian density tracking

Cited by 1 publication

References 14 publications

Distributed stochastic optimization in networks with low informational exchange

Distributed stochastic optimization in networks with low informational exchange

Contact Info

Product

Resources

About