Distributed estimation of channel gains in wireless sensor networks

We consider the problem of distributed channel estimation in a sensor network which employs a random sleep strategy to conserve energy. If the N network nodes are randomly placed at unknown positions, some prior information about the channel gains can be obtained due to the path loss effect. When considered from a single node perspective this prior information is uninformative because there are on the order of N links to estimate, while there are on the order of N parameters to specify the unknown node positions. However, from a network wide channel estimation perspective, there are on the order of N2 channel gains, but these are heavily influenced by only 3N position parameters. We show that expectation propagation (EP) can provide a distributed channel gain estimation algorithm which makes effective use of this prior information together with standard channel training methods. Exploiting prior information significantly improves estimate performance, as is evidenced by comparison with...

• Type: Journal
• Year: 2010
• Where: TSP
• Authors: Sivagnanasundaram Ramanan, John MacLaren Walsh
• Keywords: Artificial Intelligence, Channel Estimation, Channel Gain, Prior Information, TSP 2010