A machine-to-machine (M2M) system composed of low-power embedded devices powered by energy scavenging mechanisms is considered. The data and energy arrival as well as the channel state processes are all modeled as finite-state Markov processes. Assuming that the state transition probabilities characterizing these processes are unknown at the transmitter, a learning theoretic approach is introduced, and it is shown that the transmitter is able to learn the optimal transmission policy that maximizes the expected sum of the data transmitted during the transmitter's lifetime. In addition to the learning theoretic approach, online and offline optimization problems are also studied for the same setup. By characterizing the optimal performance for all three problems we identify the loss due to lack of transmitter's information regarding the behaviors of the underlying processes. Numerical results corroborate theoretical findings and show that, for a given number of learning iterations, the learning theoretic approach reaches a 90% of the performance of the online optimization problem. © 2012 IEEE.
A learning theoretic approach to energy harvesting communication system optimization / Blasco, P.; Gunduz, D.; Dohler, M.. - (2012), pp. 1657-1662. (Intervento presentato al convegno 2012 IEEE Globecom Workshops, GC Wkshps 2012 tenutosi a Anaheim, CA, usa nel 2012) [10.1109/GLOCOMW.2012.6477834].
A learning theoretic approach to energy harvesting communication system optimization
Gunduz D.;
2012
Abstract
A machine-to-machine (M2M) system composed of low-power embedded devices powered by energy scavenging mechanisms is considered. The data and energy arrival as well as the channel state processes are all modeled as finite-state Markov processes. Assuming that the state transition probabilities characterizing these processes are unknown at the transmitter, a learning theoretic approach is introduced, and it is shown that the transmitter is able to learn the optimal transmission policy that maximizes the expected sum of the data transmitted during the transmitter's lifetime. In addition to the learning theoretic approach, online and offline optimization problems are also studied for the same setup. By characterizing the optimal performance for all three problems we identify the loss due to lack of transmitter's information regarding the behaviors of the underlying processes. Numerical results corroborate theoretical findings and show that, for a given number of learning iterations, the learning theoretic approach reaches a 90% of the performance of the online optimization problem. © 2012 IEEE.
Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris
