Title :
Orthogonal-transformed variable-gain least mean squares (OVLMS) algorithm for fractional tap-spaced adaptive MLSE equalizers
Author :
Denno, Satoshi ; Saito, Yoichi
Author_Institution :
NTT Wireless Syst. Labs., Kanagawa, Japan
fDate :
8/1/1999 12:00:00 AM
Abstract :
A fast channel-estimation scheme for adaptive maximum-likelihood sequence-estimation (MLSE) equalizers called the orthogonal-transformed variable-gain least mean squares (OVLMS) algorithm is proposed. This algorithm requires only as many operations as the least mean squares algorithm in spite of its excellent performance. Furthermore, an operational complexity reduction method is proposed in which the orthogonal matrix is reconfigured as eigenvectors with valid eigenvalues. The OVLMS algorithm is theoretically analyzed and is shown to have both a fast acquisition and a good tracking performance. An equalizer using OVLMS (OVLMS-MLSE) experimentally attains a 5-dB improvement in bit-error rate (BER) performance at BER of 1.0×10 -4 over coherent detection. The OVLMS-MLSE is found to be free of the degradation caused by sampling phase error. Finally, the OVLMS-MLSE equalizer is experimentally verified to synchronize within five symbols
Keywords :
adaptive equalisers; digital radio; eigenvalues and eigenfunctions; error statistics; land mobile radio; least mean squares methods; matrix algebra; maximum likelihood sequence estimation; synchronisation; tracking; BER performance; OVLMS algorithm; OVLMS-MLSE; bit-error rate; coherent detection; digital mobile radio; eigenvalues; eigenvectors; fast acquisition; fast channel-estimation; fractional tap-spaced adaptive MLSE equalizers; least mean squares; maximum-likelihood sequence-estimation; operational complexity reduction method; orthogonal matrix; orthogonal-transformed variable-gain LMS algorithm; sampling phase error; synchronization; tracking performance; Algorithm design and analysis; Bit error rate; Degradation; Eigenvalues and eigenfunctions; Equalizers; Least mean square algorithms; Maximum likelihood detection; Maximum likelihood estimation; Performance analysis; Sampling methods;
Journal_Title :
Communications, IEEE Transactions on
