Low-complexity iterative frequency domain decision feedback equalization

Single-carrier transmission with frequency domain equalization (SC-FDE) offers a viable design alternative to the classic orthogonal frequency division multiplexing technique. However, SC-FDE using a linear equalizer may suffer from serious performance deterioration for transmission over severely frequency-selective fading channels. An effective method of solving this problem is to introduce non-linear decision feedback equalization (DFE) to SC-FDE. In this contribution, a low complexity iterative decision feedback equalizer operating in the frequency domain of single-carrier systems is proposed. Based on the minimum mean square error criterion, a simplified parameter estimation method is introduced to calculate the coefficients of the feed-forward and feedback filters, which significantly reduces the implementation complexity of the equalizer. Simulation results show that the performance of the proposed simplified design is similar to the traditional iterative block DFE under various multipath fading channels but it imposes a much lower complexity than the latter

A Suboptimal Receiver with Turbo Block Coding for Ultra-Wideband Communications

In this paper, the performance of adaptive equalization and turbo product coding is investigated for pulse-based UWB communications in short-range indoor environments. The sensitivity of adaptive LMS linear and nonlinear (decision-feedback) equalizers with respect to the number of training symbols and number of taps is considered. To reduce the error performance variation with respect to changing channel conditions, a turbo product code (TPC) with two component (31,26,3) Hamming codes is proposed. We report simulation results showing that channel coding not only improves error performance, but also reduces significantly the sensitivity of UWB systems in short-range indoor wireless communications

Bit error performance of diffuse indoor optical wireless channel pulse position modulation system employing artificial neural networks for channel equalisation

The bit-error rate (BER) performance of a pulse position modulation (PPM) scheme for non-line-of-sight indoor optical links employing channel equalisation based on the artificial neural network (ANN) is reported. Channel equalisation is achieved by training a multilayer perceptrons ANN. A comparative study of the unequalised `soft' decision decoding and the `hard' decision decoding along with the neural equalised `soft' decision decoding is presented for different bit resolutions for optical channels with different delay spread. We show that the unequalised `hard' decision decoding performs the worst for all values of normalised delayed spread, becoming impractical beyond a normalised delayed spread of 0.6. However, `soft' decision decoding with/without equalisation displays relatively improved performance for all values of the delay spread. The study shows that for a highly diffuse channel, the signal-to-noise ratio requirement to achieve a BER of 10−5 for the ANN-based equaliser is ~10 dB lower compared with the unequalised `soft' decoding for 16-PPM at a data rate of 155 Mbps. Our results indicate that for all range of delay spread, neural network equalisation is an effective tool of mitigating the inter-symbol interference

Effective denoising and adaptive equalization of indoor optical wireless channel with artificial light using the discrete wavelet transform and artificial neural network

Indoor diffuse optical wireless (OW) communication systems performance is limited due to a number of effects; interference from natural and artificial light sources and multipath induced intersymbol interference (ISI). Artificial light interference (ALI) is a periodic signal with a spectrum profile extending up to the MHz range. It is the dominant source of performance degradation at low data rates, which can be removed using a high-pass filter (HPF). On the other hand, ISI is more severe at high data rates and an equalizing filter is incorporated at the receiver to compensate for the ISI. This paper provides the simulation results for a discrete wavelet transform (DWT)—artificial neural network (ANN)-based receiver architecture for on-and-off keying (OOK) non-return-to-zero (NRZ) scheme for a diffuse indoor OW link in the presence of ALI and ISI. ANN is adopted for classification acting as an efficient equalizer compared to the traditional equalizers. The ALI is effectively reduced by proper selection of the DWT coefficients resulting in improved receiver performance compared to the digital HPF. The simulated bit error rate (BER) performance of proposed DWT-ANN receiver structure for a diffuse indoor OW link operating at a data range of 10-200 Mbps is presented and discussed. The results are compared with performance of a diffuse link with an HPF-equalizer, ALI with/without filtering, and a line-of-sight (LOS) without filtering. We show that the DWT-ANN display a lower power requirement when compared to the receiver with an HPF-equalizer over a full range of delay spread in presence of ALI. However, as expected compared to the ideal LOS link the power penalty is higher reaching to 6 dB at 200 Mbps data rate

Wavelet transform - artificial neural network receiver with adaptive equalisation for a diffuse indoor optical wireless OOK link

This paper presents an alternative approach for signal detection and equalization using the continuous wavelet transform (CWT) and the artificial neural network (ANN) in diffuse indoor optical wireless links (OWL). The wavelet analysis is used for signal preprocessing (feature extraction) and the ANN for signal detection. Traditional receiver architectures based on matched filter (MF) experience significant performance degradation in the presence of artificial light interference (ALI) and multipath induced intersymbol interference (ISI). The proposed receiver structure reduces the effect of ALI and ISI by selecting a particular scale of CWT that corresponds to the desired signal and classifying the signal into binary 1 and 0 based on an observation vector. By selecting particular scales corresponding to the signal, the effect of ALI is reduced. We show that there is little variation when using 30 and 5 neurons in the first layer, with one layer ANN model showing a consistently worse BER performance than other models, whilst the 15 neuron model show some behaviour anomalies from a BER of approximately 10-3. The simulation results show that the Wavelet-ANN architecture outperforms the traditional MF based receiver even with the filter is matched to the ISI affected pulse shape. The Wavelet-ANN receiver is also capable of providing a bit error rate (BER) performance comparable to the equalized forms of traditional receiver structure

Adaptive Bayesian decision feedback equalizer for dispersive mobile radio channels

The paper investigates adaptive equalization of time dispersive mobile ratio fading channels and develops a robust high performance Bayesian decision feedback equalizer (DFE). The characteristics and implementation aspects of this Bayesian DFE are analyzed, and its performance is compared with those of the conventional symbol or fractional spaced DFE and the maximum likelihood sequence estimator (MLSE). In terms of computational complexity, the adaptive Bayesian DFE is slightly more complex than the conventional DFE but is much simpler than the adaptive MLSE. In terms of error rate in symbol detection, the adaptive Bayesian DFE outperforms the conventional DFE dramatically. Moreover, for severely fading multipath channels, the adaptive MLSE exhibits significant degradation from the theoretical optimal performance and becomes inferior to the adaptive Bayesian DFE

Performance analysis and optimization of DCT-based multicarrier system on frequency-selective fading channels

Regarded as one of the most promising transmission techniques for future wireless communications, the discrete cosine transform (DCT) based multicarrier modulation (MCM) system employs cosine basis as orthogonal functions for real-modulated symbols multiplexing, by which the minimum orthogonal frequency spacing can be reduced by half compared to discrete Fourier transform (DFT) based one. With a time-reversed pre-filter employed at the front of the receiver, interference-free one-tap equalization is achievable for the DCT-based systems. However, due to the correlated pre-filtering operation in time domain, the signal-to-noise ratio (SNR) is enhanced as a result at the output. This leads to reformulated detection criterion to compensate for such filtering effect, rendering minimum-mean-square-error (MMSE) and maximum likelihood (ML) detections applicable to the DCT-based multicarrier system. In this paper, following on the pre-filtering based DCT-MCM model that build in the literature work, we extend the overall system by considering both transceiver perfections and imperfections, where frequency offset, time offset and insufficient guard sequence are included. In the presence of those imperfection errors, the DCT-MCM systems are analysed in terms of desired signal power, inter-carrier interference (ICI) and inter-symbol interference (ISI). Thereafter, new detection algorithms based on zero forcing (ZF) iterative results are proposed to mitigate the imperfection effect. Numerical results show that the theoretical analysis match the simulation results, and the proposed iterative detection algorithms are able to improve the overall system performance significantly