Near-Minimum Bit-Error Rate Equalizer Adaptation for PRML Systems

Abstract

Abstract-Receivers for partial response maximum-likelihood systems typically use a linear equalizer followed by a Viterbi detector. The equalizer tries to confine the channel intersymbol interferenceto a short span in order to limit the implementation complexity of the Viterbi detector. Equalization is usually made adaptive in order to compensate for channel variations. Conventional adaptation techniques, e.g., LMS, are, in general, suboptimal in terms of bit-error rate (BER). In this paper, we present a new equalizer adaptation algorithm that seeks to minimize the BER at the Viterbi detector output. The algorithm extracts information from the sequenced amplitude margin (SAM) histogram and incorporates a selection mechanism that focuses adaptation on particular data and noise realizations. The selection mechanism is based on the reliability of the add compare select (ACS) operations in the Viterbi detector. From a complexity standpoint, the algorithm is essentially as simple as the conventional LMS algorithm. Moreover, we present a further simplified version of the algorithm that does not require any hardware multiplications. Simulation results, for an idealized optical storage channel, confirm a substantial performance improvement relative to existing adaptation algorithms.Index Terms-Adaptive equalizers, intersymbol interference, partial response signaling, sequenced amplitude margin (SAM), Viterbi detection.

Adaptive minimum error-rate filtering design: A review

“…The adaptive performance of the LMS and LSER beamformers are also depicted in ig. 16. Locations of the four m-QAM user sources with respect to the three-element array having l=2 element spacing, where l is the wavelength and the minimum angular separation yo60 .…”

“…In the past decade, significant advances have been made in the design of adaptive minimum BER (MBER) filtering for a variety of communication applications, including classical single-user channel equalisation [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], multiuser detection in codedivision multiple-access (CDMA) systems [21][22][23][24][25][26][27][28][29][30], adaptive beamforming assisted receiver for multiple-antenna aided systems [31][32][33][34][35][36][37][38][39], space-time equalisation assisted multiuser detection for spacedivision multiple-access (SDMA) induced multipleinput multiple-output (MIMO) systems [40][41][42][43][44], and orthogonal frequency division multiplexing (OFDM) and other multi-carrier systems [45][46][47][48][49][50]. The ...…”

Nonlinear Equalization for TDMR Channels Using Neural Networks

“…In [13], a noise whitening function based on a hybrid genetic algorithm was incorporated in the neural network equalizer design and detector BER was evaluated. In [14], an equalizer adaptation algorithm was studied that minimizes BER at the Viterbi detector output by considering all relevant bit error sequences when tracing back error paths in the Viterbi detector. Both algorithms have a relatively high implementation complexity.…”

Adaptive Nonlinear Equalizer Using a Mixture of Gaussian-Based Online Density Estimator

“…The MMSE linear equalizer also has a nature link to adaptive filters [10], and admits a very simple yet powerful on-line adaptation by the least mean square (LMS) algorithm [11]. However, the MMSE criterion is not equivalent to the minimum bit error rate (MBER) criterion, and the latter is the ultimate performance criterion for communication channel equalization [12]- [32]. Equalizers based on the MBER criterion are thus of great interests and have attracted considerable attentions [14], [20]- [23], [33]- [35].…”