Computationally feasible bounds for partially observed Markov decision processes
Operations Research
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
The Witness Algorithm: Solving Partially Observable Markov Decision Processes
The Witness Algorithm: Solving Partially Observable Markov Decision Processes
Markov decision process frameworks for cooperative retransmission in wireless networks
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Near-optimal reinforcement learning framework for energy-aware sensor communications
IEEE Journal on Selected Areas in Communications
Markov decision process frameworks for cooperative retransmission in wireless networks
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
An energy-efficient opportunistic relay assignment in wireless cooperative networks
International Journal of Sensor Networks
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A transmitted packet that fails to reach its intended destination may be correctly received by neighbor nodes due to the broadcast nature of the wireless medium. In a cooperative retransmission scheme, these neighbor nodes, known as relays, can retransmit the failed packet on behalf of the original source node. The challenge is that multiple concurrent transmissions may lead to collision at the destination, and thus the problem is to decide which relay should help in retransmitting the failed packet so that the destination can successfully receive it. This paper proposes a decentralized partially observable Markov decision process (DEC-POMDP) model for selecting the relays to perform the cooperative retransmission. The proposed DEC-POMDP model does not require global channel state information (CSI). In addition, it is robust to noise in CSI measurements. Furthermore, the proposed DEC-POMDP scheme utilizes the gradient descent learning method to eliminate the need for a wireless channel model. We show that the proposed learning method based on the DEC-POMDP model can perform near optimally in the absence of a channel model and despite its implementation simplicity.