DocumentCode :
3236599
Title :
A predictive strategy for lifetime maximization in selective relay networks
Author :
Mousavifar, S.A. ; Khattab, T. ; Leung, C.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of British Columbia, Vancouver, BC
fYear :
2009
fDate :
March 30 2009-April 1 2009
Firstpage :
1
Lastpage :
6
Abstract :
Two algorithms based on an energy conserving dynamic transmit power threshold are proposed for improving the lifetime in relay networks utilizing selective relay strategies with amplify-and-forward (AF) relays. the lifetime of the relay network is defined as the maximum number of successfully received messages satisfying a desired SNR at the destination under probability of outage constraints. In the first algorithm, the predicted outage probability, calculated based on the energy conserving dynamic threshold, is constrained at each transmission. In this case, when the number of relays is large, the improvement is substantial. As the number of relays decreases, the method improves the lifetime under the condition of high initial energy levels at the relays. In the second method, targeting applications which are not sensitive to the distribution of outage throughout the lifetime of the relay network, the predicted probability of outage, calculated based on laws-of-physics limitations only, is constrained at each transmission. Using the second method, greater lifetime improvements are achieved and average outage constraints are maintained at the expense of a few instantaneous outage probability violations. Both algorithms are implemented in conjunction with previously proposed energy greedy relay selection strategies such as Minimum Power Transmission (MPT), Maximum Residual Energy (MRE), Minimum Energy Index (MEI), and Maximum Outage Probability (MOP).
Keywords :
probability; radio networks; amplify-and-forward relays; energy conserving dynamic threshold; lifetime maximization; maximum outage probability; minimum energy index; minimum power transmission; selective relay networks; Energy states; Gaussian noise; Noise reduction; Performance gain; Power system modeling; Power system relaying; Power transmission; Prediction algorithms; Probability; Relays;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Sarnoff Symposium, 2009. SARNOFF '09. IEEE
Conference_Location :
Princeton, NJ
Print_ISBN :
978-1-4244-3381-0
Electronic_ISBN :
978-1-4244-3382-7
Type :
conf
DOI :
10.1109/SARNOF.2009.4850300
Filename :
4850300
Link To Document :
بازگشت