Paper Info
Title
Joint Approximation of Information and Distributed Link-Scheduling Decisions in Wireless Networks
Abstract
For a large multi-hop wireless network, nodes are preferable to make distributed and localized link-scheduling decisions with only interactions among a small number of neighbors. However, for a slowly decaying channel and densely populated interferers, a small size neighborhood often results in nontrivial link outages and is thus insufficient for making optimal scheduling decisions. A question arises how to deal with the information outside a neighborhood in distributed link-scheduling. In this work, we develop joint approximation of information and distributed link scheduling. We first apply machine learning approaches to model distributed link-scheduling with complete information. We then characterize the information outside a neighborhood in form of residual interference as a random loss variable. The loss variable is further characterized by either a Mean Field approximation or a normal distribution based on the Lyapunov central limit theorem. The approximated information outside a neighborhood is incorporated in a factor graph. This results in joint approximation and distributed link-scheduling in an iterative fashion. Link-scheduling decisions are first made at each individual node based on the approximated loss variables. Loss variables are then updated and used for next link-scheduling decisions. The algorithm repeats between these two phases until convergence. Interactive iterations among these variables are implemented with a message-passing algorithm over a factor graph. Simulation results show that using learned information outside a neighborhood jointly with distributed link-scheduling reduces the outage probability close to zero even for a small neighborhood.
Year
Venue
Field
2012
CoRR
Factor graph,Convergence (routing),Wireless network,Mathematical optimization,Central limit theorem,Normal distribution,Scheduling (computing),Communication channel,Artificial intelligence,Complete information,Machine learning,Mathematics
DocType
Volume
Citations 
Journal
abs/1201.2575
0
PageRank 
References 
Authors
0.34
14
2
Name
Order
Citations
PageRank
Sung-eok Jeon1406.34
Chuanyi Ji2812124.04