TY - GEN
T1 - Anti-jam distributed MIMO decoding using wireless sensor networks
AU - Farahmand, Shahrokh
AU - Cano, Alfonso
AU - Giannakis, Georgios B
PY - 2008/9/16
Y1 - 2008/9/16
N2 - Consider a set of sensors that wish to consent on the message broadcasted by a multi-antenna transmitter in the presence of white-noise jamming. The jammer's interference introduces correlation across receivers and destroys the decomposable form of the maximum-likelihood decoder, thus preventing direct application of known distributed detection algorithms. This paper develops distributed detectors that circumvent this problem. Treating the jammer signal as deterministic, we develop two distributed estimation-decoding algorithms. The first algorithm relies on the generalized likelihood ratio test, whereas the second algorithm relies on semi-definite relaxation techniques and is suitable for large alphabet sizes. Both algorithms feature: (i) distributed implementation requiring only single-hop communications; (ii) no constraints on the network topology so long as it is connected; and (iii) performance close to the optimum centralized detector in the presence of severe jamming.
AB - Consider a set of sensors that wish to consent on the message broadcasted by a multi-antenna transmitter in the presence of white-noise jamming. The jammer's interference introduces correlation across receivers and destroys the decomposable form of the maximum-likelihood decoder, thus preventing direct application of known distributed detection algorithms. This paper develops distributed detectors that circumvent this problem. Treating the jammer signal as deterministic, we develop two distributed estimation-decoding algorithms. The first algorithm relies on the generalized likelihood ratio test, whereas the second algorithm relies on semi-definite relaxation techniques and is suitable for large alphabet sizes. Both algorithms feature: (i) distributed implementation requiring only single-hop communications; (ii) no constraints on the network topology so long as it is connected; and (iii) performance close to the optimum centralized detector in the presence of severe jamming.
KW - Consensus
KW - Distributed algorithm
KW - Generalized likelihood ratio test
KW - Jamming
KW - Semi-definite relaxation
UR - http://www.scopus.com/inward/record.url?scp=51449083750&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51449083750&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2008.4518095
DO - 10.1109/ICASSP.2008.4518095
M3 - Conference contribution
AN - SCOPUS:51449083750
SN - 1424414849
SN - 9781424414840
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2257
EP - 2260
BT - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
T2 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Y2 - 31 March 2008 through 4 April 2008
ER -