Adaptive Minimum Bit Error Rate Research Articles

Minimum-Bit-Error Rate Tuning for PDNP Detection

A dominant impediment in magnetic recording is pattern-dependent media noise, and its impact will only grow more severe as areal densities increase. A widely used strategy for mitigating media noise in a trellis-based detector is pattern-dependent noise prediction (PDNP); in this approach, each bit pattern (which determines a trellis branch) will have its own set of branch metric parameters (including the signal levels, noise predictor coefficients, and residual variances). Because the number of states grows exponentially with the number of tracks being detected, a multitrack detector has far more parameters than a single-track detector. In this article, we propose the adaptive minimum-bit-error rate (AMBER) algorithm for adapting these pattern-dependent multitrack detector parameters with the aim of minimizing BER. Numerical results for a 2-D-PDNP multitrack detector based on a quasi-micromagnetic simulated channel show that, when compared to a conventional MMSE criterion, the AMBER algorithm decreases the BER by 17%.

MBER Space Time Equalization assisted Multiuser Detection

A novel minimum bit-error rate (MBER) space–time-equalization (STE)-based multiuser detector (MUD) is proposed for multiple-receive-antenna-assisted space-division multiple-access systems. It is shown that the MBER-STE-aided MUD significantly outperforms the standard minimum mean-square error design in terms of the achievable bit-error rate (BER). Adaptive implementations of the MBER STE are considered, and both the block-databased and sample-by-sample adaptive MBER algorithms are proposed. The latter, referred to as the least BER (LBER) algorithm, is compared with the most popular adaptive algorithm, known as the least mean square (LMS) algorithm. It is shown that in case of binary phase-shift keying, the computational complexity of the LBER-STE is about half of that required by the classic LMS-STE. Our simulation results demonstrate that the MBER ST-DFE assisted MUD is more robust to channel estimation errors as well as to potential error propagation imposed by decision feedback errors, compared to the MMSE ST-DFE assisted MUD.

Reduced symmetric self-constructing fuzzy neural network beamforming detectors

Beamforming technology has been widely used in smart antenna systems that can increase the user's capacity and coverage in modern communication products. In this study, a powerful reduced symmetric self-constructing fuzzy neural network (RS-SCFNN) beamforming detector is proposed for multi-antenna-assisted systems. A novel training algorithm for the RS-SCFNN beamformer is proposed based on clustering of array input vectors and an adaptive minimum bit-error rate method. An inherent symmetric property of the array input signal space is exploited to make training procedure of RS-SCFNN more efficient than that of standard SCFNN. In addition, the required amount of fuzzy rules can be greatly reduced in the RS-SCFNN structure. Simulation results demonstrate that RS-SCFNN beamformer provides superior performance to the classical linear ones and the other non-linear ones (including symmetric radial basis function, SCFNN and S-SCFNN), especially when supporting a large amount of users in the rank-deficient multi-antenna-assisted system.

Adaptive beamforming for binary phase shift keying communication systems

The paper revisits adaptive beamforming assisted receiver for multiple antenna aided multiuser systems that employ binary phase shift keying (BPSK) modulation. The standard minimum mean square error (MMSE) design is based on the criterion of minimising the mean square error (MSE) between the beamformer's desired output and complex-valued beamformer's output. Since the desired output for BPSK systems is real-valued, minimising the MSE between the beamformer's desired output and real-part of the beamformer's output can significantly improve the bit error rate (BER) performance, and we refer to this alternative MMSE design as the real-valued MMSE (RV-MMSE) to contrast to the standard complex-valued MMSE (CV-MMSE) design. The minimum BER (MBER) design however still outperforms the RV-MMSE solution, particularly for overloaded systems where degree of freedom of the antenna array is smaller than the number of BPSK users. Adaptive implementation of this RV-MMSE beamforming design is realised using a least mean square (LMS) type adaptive algorithm, which we refer to as the RV-LMS, in comparison to the standard CV-LMS algorithm. The RV-LMS adaptive beamformer is shown to have a similar computational complexity as the adaptive MBER beamforming implementation known as the least bit error rate (LBER), imposing only half of the computational requirements of the CV-LMS algorithm.

Minimum bit-error rate design for space-time equalization-based multiuser detection

A novel minimum bit-error rate (MBER) space-time-equalization (STE)-based multiuser detector (MUD) is proposed for multiple-receive-antenna-assisted space-division multiple-access systems. It is shown that the MBER-STE-aided MUD significantly outperforms the standard minimum mean-square error design in terms of the achievable bit-error rate (BER). Adaptive implementations of the MBER STE are considered, and both the block-data-based and sample-by-sample adaptive MBER algorithms are proposed. The latter, referred to as the least BER (LBER) algorithm, is compared with the most popular adaptive algorithm,known as the least mean square (LMS) algorithm. It is shown that in case of binary phase-shift keying, the computational complexity of the LBER-STE is about half of that required by the classic LMS-STE. Simulation results demonstrate that the LBER algorithm performs consistently better than the classic LM Salgorithm, both in terms of its convergence speed and steady-state BER performance.

Adaptive minimum bit-error rate beamforming

An adaptive beamforming technique is proposed based on directly minimizing the bit-error rate (BER). It is demonstrated that this minimum BER (MBER) approach utilizes the antenna array elements more intelligently than the standard minimum mean square error (MMSE) approach. Consequently, MBER beamforming is capable of providing significant performance gains in terms of a reduced BER over MMSE beamforming. A block-data adaptive implementation of the MBER beamforming solution is developed based on the Parzen window estimate of probability density function. Furthermore, a sample-by-sample adaptive implementation is considered, and a stochastic gradient algorithm, referred to as the least bit error rate, is derived. The proposed adaptive MBER beamforming technique provides an extension to the existing work for adaptive MBER equalization and multiuser detection.

Adaptive minimum bit-error-rate filtering

Adaptive filtering has traditionally been developed based on the minimum mean square error (MMSE) principle and has found ever-increasing applications in communications. The paper develops adaptive filtering based on an alternative minimum bit error rate (MBER) criterion for communication applications. It is shown that the MBER filtering exploits the non-Gaussian distribution of filter output effectively and, consequently, can provide significant performance gain in terms of smaller bit error rate (BER) over the MMSE approach. Adopting the classical Parzen window or kernel density estimation for a probability density function (pdf), a block-data gradient adaptive MBER algorithm is derived. A stochastic gradient adaptive MBER algorithm is further developed for sample-by-sample adaptive implementation of the MBER filtering. Extension of the MBER approach to adaptive nonlinear filtering is also discussed.

Equalisation based on negentropy minimisation

An equalisation method based on negentropy minimisation is introduced and its characteristics are investigated. Negentropy includes higher order statistical information and its error minimisation provides improved convergence and performance. The bit error ratio of the proposed method has similar characteristics to the adaptive minimum bit error rate (AMBER) equaliser. The main advantage of the proposed equaliser is that it needs drastically fewer training iterations than the AMBER and the MMSE equalisers.

Adaptive minimum-BER linear multiuser detection for DS-CDMA signals in multipath channels

The problem of constructing adaptive minimum bit error rate (MBER) linear multiuser detectors is considered for direct-sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. Based on the approach of kernel density estimation for approximating the bit error rate (BER) from training data, a least mean squares (LMS) style stochastic gradient adaptive algorithm is developed for training linear multiuser detectors. Computer simulation is used to study the convergence speed and steady-state BER misadjustment of this adaptive MBER linear multiuser detector, and the results show that it outperforms an existing LMS-style adaptive MBER algorithm presented by Yeh et al. (see Proc. Globecom, Sydney, Australia, p.3590-95, 1998).

Adaptive minimum bit-error rate equalization for binary signaling

We consider the design and adaptation of a linear equalizer with a finite number of coefficients in the context of a classical linear intersymbol-interference channel with Gaussian noise and a memoryless decision device. If the number of equalizer coefficients is sufficient, the popular minimum mean-squared-error (MMSE) linear equalizer closely approximates the optimal linear equalizer that directly minimizes bit-error rate (BER). However, when the number of equalizer coefficients is insufficient to approximate the channel inverse, the minimum-BER equalizer can outperform the MMSE equalizer by as much as 16 dB in certain cases. We propose a simple stochastic adaptive algorithm for realizing the minimum-BER equalizer. Compared to the least-mean-square algorithm, the proposed algorithm can provide a substantial reduction in BER with no increase in complexity.