Adaptive minimum bit error rate receiver for CDMA-based block transmission systems

This research aims to use the Adaptive Minimum Bit Error Rate (AMBER) for Wideband Code Division Multiple Access (WCDMA). AMBER and Normalized AMBER (NAMBER) algorithms for WCDMA systems are derived and implemented. Its Bit Error Rate (BER) performance is measured using a WCDMA Frequency Division Duplex (FDD) Downlink Physical Layer model. The new methods are compared to the original WCDMA system and the NLMS Adaptive Combiner. Results show around 5 dB performance gain over the single antenna system, and around 2 dB performance gain over the Dual Antenna Normalized Least Mean Squares (NLMS) Adaptive Combiner system in an Additive White Gaussian Noise channel at a BER of 10-2.5. However, complex computation requirements render the algorithms impractical for widespread use. Simplifications and alternative implementations have to be investigated.

The paper considers the problem of constructing adaptive minimum bit error rate (MBER) neural network equalisers for binary signalling. Motivated from a kernel density estimation of the bit error rate (BER) as a smooth function of training data, a stochastic gradient algorithm called the least bit error rate (LBER) is developed for adaptive nonlinear equalisers. This LBER algorithm is applied to adaptive training of a radial basis function (RBF) equaliser in a channel intersymbol interference (ISI) plus co-channel interference setting. A simulation study shows that the proposed algorithm has good convergence speed, and a small-size RBF equaliser trained by the LBER can closely approximate the performance of the optimal Bayesian equaliser. The results also demonstrate that the standard adaptive algorithm, the least mean square (LMS), performs poorly for neural network equalisers because the minimum mean square error (MMSE) is clearly suboptimal in the equalisation setting.

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 first presented at Globecom'98 by Yeh et al.

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.

We considers the beamforming assisted multiple-antenna receiver for space-division multiple-access based multiuser systems that employ high-throughput quadrature amplitude modulation (QAM) signalling. The bit error ratio (BER) expression as the function of the beamformer's weight vector is derived, and the minimum BER (MBER) beamforming receiver is then obtained as the solution of the resulting optimisation problem that minimises the MBER criterion. A simplified conjugate gradient algorithm, which has previously demonstrated its effectiveness in solving the minimum symbol error ratio (MSER) optimisation problem, is employed to solve this MBER optimisation. For high-order QAM, although the bit decision is an inherently more complicated procedure than making a symbol decision, it turns out that the computational complexity of computing the MBER solution is similar to that of computing the MSER solution. As expected, our simulation results show that both the MBER and MSER systems achieve the same BER performance, and they significantly outperform the standard minimum mean squares error based solution.

A novel genetic algorithm (GA) assisted minimum bit error rate (MBER) beamforming technique is introduced. The performance of the proposed GAs is characterised by the probability density function (PDF) and the mean value of the achievable bit error rate (BER) at the beamformer's output. The results are also compared to the theoretical bounds. It is shown that GAs are suitable for MBER beamforming and that a trade-off between the complexity of the GA and its robustness against system parameter variations can be found.

Differential Evolution (DE) is a novel but powerful optimizer. It has been proved that the DE is superior to Genetic Algorithm (GA) in many real life applications. This paper proposed a memetic Differential Evolution assisted Minimum Bit Error Rate (MBER) linear detector for Multiple-input and multiple-output (MIMO) multi-user system. Compared with GA based MBER and other linear MIMO detectors such as minimum mean square error (MMSE) and zero forcing(ZF)detectors, the proposed scheme shows a better performance in terms of the bit error rate.

In this paper, we introduce a novel Markov chain (MC) representation aided minimum bit error rate (MBER) detection method that is applicable to an M-QAM modulated SDM/SDMA uplink system. Compared to the conventional MBER scheme, the proposed MC-MBER scheme is capable of reducing the complexity imposed with the aid of its efficient detection candidate set generation assisted by the Markov chain process. Our performance results demonstrate that the MC-MBER multi-user detection (MUD) is capable of reducing the computational complexity by a factor of eight in comparison to the conventional MBER MUD in a rank-deficient system transmitting four 4-QAM uplink substream with the aid of two receive antennas at the base station (BS), while achieving a BER performance comparable to that of the MBER MUD.

A novel length adaptive method is proposed for time domain equalizer by taking the channel attenuation ratio between different multipath components into account in UAV‐UAV and UAV‐ground channels. Then, considering received image quality, the minimum bit error ratio (MBER) criterion is exploited to design adaptive equalizers for both amplify‐and‐forward (AF) and decode‐and‐forward (DF) relaying systems by the proposed length adaptive method. Results show that proposed MBER adaptive equalizers outperform the traditional ones in both AF relaying and DF relaying as channel attenuation ratio in UAV‐ground channel increases. Moreover, DF outperforms AF as channel attenuation ratio in UAV‐UAV channel increases. Furthermore, bit error ratio (BER) and peak signal‐to‐noise ratio (PSNR) performances in both AF and DF are evaluated to show the enhancement by the proposed MBER adaptive equalizers.

Non-orthogonal multiple access (NOMA) has been gaining notable attention in the context of next generation communication system. The key primary benefit includes the higher spectrum efficiency compared to its various orthogonal counterparts. In this treatise, we consider a power NOMA design based on the minimum bit error ratio (MBER) criterion. Inspiration has been drawn from the MBER based works, which show a considerable performance improvement in terms of bit error ratio (BER) for a system. In this work, we have considered a single-input single-output (SISO) system with quadrature phase shift keying (QPSK) signal constellation. The numerical results demonstrate an overall BER improvement compared to the existing schemes, albeit, the scheme attracts large computational complexity. Traditionally, NOMA increases the spectral efficiency compared to its orthogonal counterpart. Nevertheless, our proposed solution will still hold this feature along with a better BER performance, though its spectral efficiency will be less compared to the traditional sum-rate based power NOMA.

Precoding for multiple-input multiple-output (MIMO) spatial multiplexing generally requires high feedback overhead and/or high-complexity processing. Simultaneous reduction in transmitter complexity and feedback overhead is proposed by imposing a diagonal structural constraint to precoding, i.e., power allocation. Minimum bit-error rate (MBER) is employed as the optimization criterion, and an approximate MBER (AMBER) power-allocation algorithm is proposed for a variety of receivers, including zero-forcing (ZF), successive interference cancellation (SIC), and ordered SIC (OSIC). While previously proposed precoding schemes either require ZF equalization for MBER, or use a minimum mean-squared error (MMSE) criterion, we provide a unified MBER solution to power allocation for ZF, SIC, and OSIC receiver structures. Improved error-rate performance is shown both analytically and by simulation. Simulation results also indicate that SIC and OSIC with AMBER power allocation offer superior performance over previously proposed MBER precoding with ZF equalization, as well as over MMSE precoding/decoding. Performance under noisy channels and power feedback is analyzed. A modified AMBER algorithm that mitigates error propagation in interference cancellation is developed. Compared with existing precoding methods, the proposed schemes significantly reduce both transmit processing complexity and feedback overhead, and improve error-rate performance

Uniform channel decomposition (UCD), as an improvement of geometric mean decomposition (GMD), is capable of decomposing a multiple-input-multiple-output (MIMO) channel into multiple subchannels having an identical capacity. Vector precoding (VP) is a powerful scheme, which is capable of mitigating multiuser interference (MUI) at the transmitter, provided that the channels of the users are known. In this paper, a novel joint transceiver design based on the UCD and the minimum bit error rate (MBER) VP principles is proposed for MIMO systems, in which the precoding and equalization matrices are calculated by UCD, whereas the perturbation vector is chosen to minimize the system's bit error ratio (BER). This UCD-MBER-VP transceiver design outperforms several state-of-the-art benchmark algorithms in terms of achievable BER performance without imposing an increased computational complexity, particularly for rank-deficient systems.

Space-Division Multiple Access-OFDM based wireless communication has the potential to significantly increase the spectral efficiency, system performance and number of users. Research in the development of efficient multiuser signal detection algorithms for such systems has generated much interest in recent years. This research proposes MUD schemes utilizing three neural network (NN) models like Feed Forward NN (FNN) without any hidden layer, FNN with a single hidden layer and Recurrent Neural Network (RNN) as possible alternatives to existing Genetic Algorithm (GA) based Minimum Bit Error Rate (MBER) MUD. Further, these techniques offer low complexity. Extensive simulation based performance study is carried out to prove the efficiency of the proposed techniques. Better performance of FNN with hidden layer and RNN compared to FNN without hidden layer is clearly observed in rank deficient MIMO scenario, where number of users exceeds the number of receiving antennas. The Bit Error Rate (BER) and complexity analysis of the proposed neural MUD schemes are close to optimal ML and show improvement over the previous implemented technique.

Relying on symbol blocking and judicious design of user codes, this paper builds on a generalized multicarrier (GMC) quasi-synchronous CDMA system capable of multiuser interference (MUI) elimination and intersymbol interference (ISI) suppression with guaranteed symbol recovery, regardless of the wireless frequency-selective channels. GMC-CDMA provides a unifying framework for multicarrier (MC) CDMA systems and offers flexibility in full load (maximum number of users allowed by the available bandwidth) as well in reduced load settings. Analytic evaluation and simulations illustrate that GMC-CDMA outperforms competing MC-CDMA alternatives especially in the presence of uplink multipath channels.

This paper concentrates on investigating an enhanced independent component analysis (ICA) method for blind separation of signals corrupted by noise in wireless communications. Because of the traditional classical ICA methods that always have an inadequate capacity of anti-noise or insufficient separable ability in noise circumstance without satisfying practical application requirements. For this reason, two mechanisms are conducted to establish the modified cost function and fulfill the optimization assignment in the process of constituting an enhanced ICA algorithm. This proposed algorithm can benefit tremendously from the derived minimum bit error rate (BER) criterion and the novel adaptive moment estimation (Adam) optimization approach. In this work, firstly, the novel cost function is obtained according to minimum BER fused into maximum likelihood (ML) principle-based ICA cost function. Furthermore, by utilizing Adam processing, the task of blind separation of mixed signals is implemented via optimizing this modified cost function. Lastly, theoretical analysis and experiment results corroborate the better effectiveness and robustness of the proposed enhanced ICA algorithm compared with a series of popular representative ICA methods.