DocumentCode :
395988
Title :
An adaptive energy-efficient link layer protocol using stochastic learning control
Author :
Kiran, S. ; Chandramouli, R.
Author_Institution :
Dept. of Electr. & Comput. Eng., Stevens Inst. of Technol., Hoboken, NJ, USA
Volume :
2
fYear :
2003
fDate :
11-15 May 2003
Firstpage :
1114
Abstract :
We present a computationally simple stochastic learning control protocol framework for an adaptive energy efficient link layer protocol. A stochastic iterative technique is discussed that can produce soft channel state predictions and track slow/rapidly varying bursty, finite state wireless channels. No piori knowledge about the state transition probabilities is needed for this. Theoretical convergence of the proposed technique is shown. The proposed link layer protocol utilizes the channel state predictions from the stochastic learning algorithm while computing energy efficient transmission policies. This entire process is performed on-line with no pilot (training) symbols, etc., thereby improving the throughput and avoiding energy wastage due to pilot symbols. Simulation results show that up to 50% energy savings can be obtained for some channels when compared with a popular link layer protocol. Energy and delay can be trade-off efficiently using the proposed method.
Keywords :
learning systems; prediction theory; protocols; radio networks; stochastic processes; telecommunication channels; telecommunication control; adaptive energy-efficient link layer protocol; energy efficient transmission policies; finite state wireless channels; soft channel state predictions; state transition probabilities; stochastic iterative technique; stochastic learning control; theoretical convergence; Adaptive control; Automatic repeat request; Batteries; Communication system control; Energy efficiency; Programmable control; Protocols; Stochastic processes; Wireless LAN; Wireless networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications, 2003. ICC '03. IEEE International Conference on
Print_ISBN :
0-7803-7802-4
Type :
conf
DOI :
10.1109/ICC.2003.1204535
Filename :
1204535
Link To Document :
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