TY - JOUR

T1 - Improving the generalized likelihood ratio test for unknown linear Gaussian channels

AU - Erez, Elona

AU - Feder, Meir

N1 - Funding Information:
Manuscript received February 26, 2002; revised November 19, 2002. This work was supported in part by a grant from the Israeli Science Foundation. The material in this paper was presented in part at the 38th Allerton Conference on Communication, Control, and Computing, Monticello, IL, October 2000, and at the IEEE International Symposium on Information Theory and Its Applications, Honolulu, HI, November 2000.

PY - 2003/4

Y1 - 2003/4

N2 - In this work, we consider the decoding problem for unknown Gaussian linear channels. Important examples of linear channels are the intersymbol interference (ISI) channel and the diversity channel with multiple transmit and receive antennas employing space-time codes (STC). An important class of decoders is based on the generalized likelihood ratio test (GLRT). Our work deals primarily with a decoding algorithm that uniformly improves the error probability of the GLRT decoder for these unknown linear channels. The improvement is attained by increasing the minimal distance associated with the decoder. This improvement is uniform, i.e., for all the possible channel parameters, the error probability is either smaller by a factor (that is exponential in the improved distance), or for some, may remain the same. We also present an algorithm that improves the average (over the channel parameters) error probability of the GLRT decoder. We provide simulation results for both decoders.

AB - In this work, we consider the decoding problem for unknown Gaussian linear channels. Important examples of linear channels are the intersymbol interference (ISI) channel and the diversity channel with multiple transmit and receive antennas employing space-time codes (STC). An important class of decoders is based on the generalized likelihood ratio test (GLRT). Our work deals primarily with a decoding algorithm that uniformly improves the error probability of the GLRT decoder for these unknown linear channels. The improvement is attained by increasing the minimal distance associated with the decoder. This improvement is uniform, i.e., for all the possible channel parameters, the error probability is either smaller by a factor (that is exponential in the improved distance), or for some, may remain the same. We also present an algorithm that improves the average (over the channel parameters) error probability of the GLRT decoder. We provide simulation results for both decoders.

KW - Diversity channels

KW - Generalized likelihood ratio test (GLRT)

KW - Intersymbol interference (ISI)

KW - Maximum likelihood (ML)

UR - http://www.scopus.com/inward/record.url?scp=0037397997&partnerID=8YFLogxK

U2 - 10.1109/TIT.2003.809598

DO - 10.1109/TIT.2003.809598

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AN - SCOPUS:0037397997

VL - 49

SP - 919

EP - 936

JO - IEEE Transactions on Information Theory

JF - IEEE Transactions on Information Theory

SN - 0018-9448

IS - 4

ER -