Adaptive minimum-BER decision feedback equalisers for binary signalling

Mulgrew, Bernard; Chen, Sheng

doi:10.1016/s0165-1684(01)00035-4

Cited by 41 publications

(32 citation statements)

References 8 publications

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“…Furthermore, at the time of writing there exists no theoretical result for analysing the steady-state BER misadjustment of the LBER algorithm, but in practice we have observed that the steady-state BER misadjustment can often be made very small by carefully tuning the two algorithmic parameters. Convergence behaviour and steady-state BER misadjustment of the LBER algorithm have been extensively investigated in the previous publications [11,14,18,20,25,26,29,31,35,36,38,39,[41][42][43][44].…”

Section: Adaptive Mber Filteringmentioning

confidence: 99%

“…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 ...…”

Section: Introductionmentioning

confidence: 99%

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Adaptive minimum error-rate filtering design: A review

Chen

Tan

et al. 2008

Signal Processing

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Adaptive filtering has been an enabling technology and has found ever-increasing applications in various state-of-the-art communication systems. Traditionally, adaptive filtering has been developed based on the Wiener or minimum mean square error (MMSE) approach, and the famous least mean square algorithm with its low computational complexity readily meets the fast real-time computational constraint of modern high-speed communication systems. For a communication system, however, it is the system's bit error rate (BER), not the mean square error (MSE), that really matters. It has been recognised that minimising the MSE criterion does not necessarily produce the minimum BER (MBER) performance. The introduction of the novel MBER design has opened up a whole new chapter in the optimisation of communication systems, and its design trade-offs have to be documented in contrast to those of the classic but actually still unexhausted MMSE and other often-used optimisation criteria. This contribution continues this theme, and we provide a generic framework for adaptive minimum error-probability filter design suitable for the employment in a variety of communication systems. Advantages and disadvantages of the adaptive minimum error-probability filter design are analysed extensively, in comparison with the classic Wiener filter design. r

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Section: Adaptive Mber Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive minimum error-rate filtering design: A review

Chen

Tan

et al. 2008

Signal Processing

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“…For the linear equalizer or multiuser detector with binary signalling, it is now well-known that the bit error rate (BER) difference between the MMSE solution and the minimum BER (MBER) one can be large in certain situations [17][18][19][20][21][22][23][24][25][26][27][28][29]. Recent research has aimed to develop adaptive linear equalizer and multiuser detector based on the MBER criterion [20,22,[25][26][27][28][29]. For nonlinear classifiers, the relationship between the MSE and the error rate is more dubious, and the MMSE solution does not necessarily correspond to a small error rate.…”

Section: Introductionmentioning

confidence: 99%

Adaptive near minimum error rate training for neural networks with application to multiuser detection in CDMA communication systems

2005

Self Cite

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Adaptive training of neural networks is typically done using some stochastic gradient algorithm that aims to minimize the mean square error (MSE). For many classification applications, such as channel equalization and codedivision multiple-access (CDMA) multiuser detection, the goal is to minimize the error probability. For these applications, adopting the MSE criterion may lead to a poor performance. A nonlinear adaptive near minimum error rate algorithm called the nonlinear least bit error rate (NLBER) is developed for training neural networks for these kinds of applications. The proposed method is applied to downlink multiuser detection in CDMA communication systems. Simulation results show that the NLBER algorithm has a good convergence speed and a small-size radial basis function network trained by this adaptive algorithm can closely match the performance of the optimal Bayesian multiuser detector. The results also confirm that training the neural network multiuser detector using the least mean square algorithm, although generally converging well in the MSE, can produce a poor error rate performance. r

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“…As the MMSE solution is not optimal [1]- [3], research has been looking into LMS-style adaptive algorithms based on the MSER or minimum bit error rate (MBER) criterion. For bi nary schemes, two such algorithms have emerged, called the approximate MBER (AMBER) [4], [5] and the least bit error rate (LBER) [6], [7], respectively. The AMBER is simpler than the LBER, although the complexity of LBER is still lin ear in the equalizer length.…”

Section: Introductionmentioning

confidence: 99%

“…This paper presents an adaptive MSER algorithm for lin ear equalizer and DFE with M -PAM symbols. We adopt the approach used in deriving the LBER algorithm [6], [7], namely using a kernel density estimation [8], [9] to approx imate the: SER from training data and to derive a stochastic gradient algorithm for sample-by-sample adaptation. The re sulting algorithm is therefore called the LSER.…”

Section: Introductionmentioning

confidence: 99%

Stochastic least-symbol-error-rate adaptive equalization for pulse-amplitude modulation

Chen¹,

Mulgrew²,

Hanzo³

2002

IEEE International Conference on Acoustics Speech and Signal Processing

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Abstract-The paper derives a stochastic-gradient minimum symbol error-rate (MSER) algorithm, caned the least symbol error rate (LSER), for training the linear equalizer and Iinear-eombiner decision feedback equalizer (DFE) with M -PAM signalling. Tbis LSER algo rithm has some performance advantages, In terms of faster convergence rate and smaller steady-state symbol error rate (SER) misadjustment, over an existing simpler stochastlc:-gradient adaptive MSER algorithm called the approximate MSER (AMSER).

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Adaptive minimum-BER decision feedback equalisers for binary signalling

Cited by 41 publications

References 8 publications

Adaptive minimum error-rate filtering design: A review

Adaptive minimum error-rate filtering design: A review

Adaptive near minimum error rate training for neural networks with application to multiuser detection in CDMA communication systems

Stochastic least-symbol-error-rate adaptive equalization for pulse-amplitude modulation

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