Title :
Stochastic gradient minimum-BER decision feedback equalisers
Author :
Mulgrew, Bernard ; Chen, Sheng
Author_Institution :
Dept. of Electron. & Electr. Eng., Edinburgh Univ., UK
Abstract :
The problem of constructing adaptive minimum bit error rate (MBER) decision feedback equalisers (DFEs) for binary signaling is considered. Gradient algorithms are developed for both conventional and state (or space) translation forms of the DFE. Kernel density estimation is demonstrated to provide a convenient mechanism for approximating the BER as a smooth function of the available data. This leads to the development of a number of adaptive algorithms. Computer simulation is used to assess the performance of these algorithms
Keywords :
adaptive equalisers; decision feedback equalisers; error statistics; gradient methods; least mean squares methods; stochastic processes; telecommunication signalling; BER approximation; LMS; MMSE cost function; adaptive algorithms; adaptive minimum bit error rate DFE; algorithm performance; binary signaling; computer simulation; decision feedback equalisers; gradient algorithms; kernel density estimation; minimum mean squared error; smooth function; space translation DFE; state translation DFE; stochastic gradient minimum-BER DFE; Adaptive filters; Bit error rate; Computer errors; Computer simulation; Cost function; Decision feedback equalizers; Kernel; Least squares approximation; Least squares methods; Stochastic processes;
Conference_Titel :
Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC. The IEEE 2000
Conference_Location :
Lake Louise, Alta.
Print_ISBN :
0-7803-5800-7
DOI :
10.1109/ASSPCC.2000.882453
