Single-carrier frequency-domain equalization with hybrid decision feedback equalizer for Hammerstein channels containing nonlinear transmit amplifier

We propose a nonlinear hybrid decision feedback equalizer (NHDFE) for single-carrier (SC) block transmission systems with nonlinear transmit high power amplifier (HPA), which significantly outperforms our previous nonlinear SC frequency-domain equalization (NFDE) design. To obtain the coefficients of the channel impulse response (CIR) as well as to estimate the nonlinear mapping and the inverse nonlinear mapping of the HPA, we adopt a complex-valued (CV) B-spline neural network approach. Specifically, we use a CV B-spline neural network to model the nonlinear HPA, and we develop an efficient alternating least squares scheme for estimating the parameters of the Hammerstein channel, including both the CIR coefficients and the parameters of the CV B-spline model. We also adopt another CV B-spline neural network to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can be estimated using the least squares algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. The effectiveness of our NHDFE design is demonstrated in a simulation study, which shows that the NHDFE achieves a signal-to-noise ratio gain of 4dB over the NFDE at the bit error rate level of 10−4

Comparative performance of complex-valued B-spline and polynomial models applied to iterative frequency-domain decision feedback equalization of Hammerstein channels

Complex-valued (CV) B-spline neural network approach offers a highly effective means for identifying and inverting practical Hammerstein systems. Compared with its conventional CV polynomial-based counterpart, a CV B-spline neural network has superior performance in identifying and inverting CV Hammerstein systems, while imposing a similar complexity. This paper reviews the optimality of the CV B-spline neural network approach. Advantages of B-spline neural network approach as compared with the polynomial based modeling approach are extensively discussed, and the effectiveness of the CV neural network-based approach is demonstrated in a real-world application. More specifically, we evaluate the comparative performance of the CV B-spline and polynomial-based approaches for the nonlinear iterative frequency-domain decision feedback equalization (NIFDDFE) of single-carrier Hammerstein channels. Our results confirm the superior performance of the CV B-spline-based NIFDDFE over its CV polynomial-based counterpart

Equalization in Dispersion-Managed Systems Using Learned Digital Back-Propagation

In this paper, we investigate the use of the learned digital back-propagation (LDBP) for equalizing dual-polarization fiber-optic transmission in dispersion-managed (DM) links. LDBP is a deep neural network that optimizes the parameters of DBP using the stochastic gradient descent. We evaluate DBP and LDBP in a simulated WDM dual-polarization fiber transmission system operating at the bitrate of 256 Gbit/s per channel, with a dispersion map designed for a 2016 km link with 15% residual dispersion. Our results show that in single-channel transmission, LDBP achieves an effective signal-to-noise ratio improvement of 6.3 dB and 2.5 dB, respectively, over linear equalization and DBP. In WDM transmission, the corresponding $Q$-factor gains are 1.1 dB and 0.4 dB, respectively. Additionally, we conduct a complexity analysis, which reveals that a frequency-domain implementation of LDBP and DBP is more favorable in terms of complexity than the time-domain implementation. These findings demonstrate the effectiveness of LDBP in mitigating the nonlinear effects in DM fiber-optic transmission systems

Performance Evaluation of MC-CDMA Systems with Single User Detection Technique using Kernel and Linear Adaptive Method, Journal of Telecommunications and Information Technology, 2021, nr 4

Among all the techniques combining multi-carrier modulation and spread spectrum, the multi-carrier code division multiple access (MC-CDMA) system is by far the most widely studied. In this paper, we present the performance of the MC-CDMA system associated with key single-user detection techniques. We are interested in problems related to identification and equalization of mobile radio channels, using the kernel method in Hilbert space with a reproducing kernel, and a linear adaptive algorithm, for MC-CDMA systems. In this context, we tested the efficiency of these algorithms, considering practical frequency selective fading channels, called broadband radio access network (BRAN), standardized for MC-CDMA systems. As far as the equalization problem encountered after channel identification is concerned, we use the orthogonality restoration combination (ORC) and the minimum mean square error (MMSE) equalizer techniques to correct the distortion of the channel. Simulation results demonstrate that the kernel algorithm is efficient for practical channel

Améliorations des transmissions VLC (Visible Light Communication) sous contrainte d'éclairage : études théoriques et expérimentations

Abstract : Indoor visible light communication (VLC) networks based on light-emitting diodes (LEDs) currently enjoy growing interest thanks in part to their robustness against interference, wide license-free available bandwidth, low cost, good energy efficiency and compatibility with existing lighting infrastructure. In this thesis, we investigate spectral-efficient modulation techniques for the physical layer of VLC to increase throughput while considering the quality of illumination as well as implementation costs. Numerical and experimental studies are performed employing pulse amplitude modulation (PAM) and carrierless amplitude and phase (CAP) modulation under illumination constraints and for high modulation orders. Furthermore, the impact of LED nonlinearity is investigated and a postdistortion technique is evaluated to compensate these nonlinear effects. Within this framework, transmission rates in the order of a few hundred Mb/s are achieved using a test bench made of low-cost components. In addition, an imaging multiple input multiple-output (MIMO) system is developed and the impact on performance of imaging lens misalignment is theoretically and numerically assessed. Finally, a polynomial matrix decomposition technique based on the classical LU factorization method is studied and applied for the first time to MIMO VLC systems in large space indoor environments.Les réseaux de communication en lumière visible (VLC) s’appuyant sur l’utilisation de diodes électroluminescentes (LED) bénéficient actuellement d’un intérêt grandissant, en partie grâce à leur robustesse face aux interférences électromagnétiques, leur large bande disponible non-régulée, leur faible coût, leur bonne efficacité énergétique, ainsi que leur compatibilité avec les infrastructures d’éclairage déjà existantes. Dans cette thèse, nous étudions des techniques de modulation à haute efficacité spectrale pour la couche physique des VLC pour augmenter les débits tout en considérant la qualité de l’éclairage ainsi que les coûts d’implémentation. Des études numériques et expérimentales sont réalisées sur la modulation d’impulsion d’amplitude (PAM) et sur la modulation d’amplitude et de phase sans porteuse (CAP) sous des contraintes d’éclairage et pour des grands ordres de modulation. De plus, l’impact des non-linéarités de la LED est étudié et une technique de post-distorsion est évaluée pour corriger ces effets non-linéaires. Dans ce cadre, des débits de plusieurs centaines de Mb/s sont atteints en utilisant un banc de test réalisé à partir de composants à bas coûts. Par ailleurs, un système multi-entrées multi-sorties (MIMO) imageant est également développé et l’impact du désaxage de l’imageur sur les performances est étudié. Finalement, une technique de décomposition polynomiale basée sur la méthode de factorisation classique LU est étudiée et appliquée aux systèmes MIMO VLC dans des grands espaces intérieurs

Coherent Optical OFDM Modem Employing Artificial Neural Networks for Dispersion and Nonlinearity Compensation in a Long-Haul Transmission System

In order to satisfy the ever increasing demand for the bandwidth requirement in broadband services the optical orthogonal frequency division multiplexing (OOFDM) scheme is being considered as a promising technique for future high-capacity optical networks. The aim of this thesis is to investigate, theoretically, the feasibility of implementing the coherent optical OFDM (CO-OOFDM) technique in long haul transmission networks. For CO-OOFDM and Fast-OFDM systems a set of modulation formats dependent analogue to digital converter (ADC) clipping ratio and the quantization bit have been identified, moreover, CO-OOFDM is more resilient to the chromatic dispersion (CD) when compared to the bandwidth efficient Fast-OFDM scheme. For CO-OOFDM systems numerical simulations are undertaken to investigate the effect of the number of sub-carriers, the cyclic prefix (CP), and ADC associated parameters such as the sampling speed, the clipping ratio, and the quantisation bit on the system performance over single mode fibre (SMF) links for data rates up to 80 Gb/s. The use of a large number of sub-carriers is more effective in combating the fibre CD compared to employing a long CP. Moreover, in the presence of fibre non-linearities identifying the optimum number of sub-carriers is a crucial factor in determining the modem performance. For a range of signal data rates up to 40 Gb/s, a set of data rate and transmission distance-dependent optimum ADC parameters are identified in this work. These parameters give rise to a negligible clipping and quantisation noise, moreover, ADC sampling speed can increase the dispersion tolerance while transmitting over SMF links. In addition, simulation results show that the use of adaptive modulation schemes improves the spectrum usage efficiency, thus resulting in higher tolerance to the CD when compared to the case where identical modulation formats are adopted across all sub-carriers. For a given transmission distance utilizing an artificial neural networks (ANN) equalizer improves the system bit error rate (BER) performance by a factor of 50% and 70%, respectively when considering SMF firstly CD and secondly nonlinear effects with CD. Moreover, for a fixed BER of 10-3 utilizing ANN increases the transmission distance by 1.87 times and 2 times, respectively while considering SMF CD and nonlinear effects. The proposed ANN equalizer performs more efficiently in combating SMF non-linearities than the previously published Kerr nonlinearity electrical compensation technique by a factor of 7

Enhanced Multicarrier Techniques for Professional Ad-Hoc and Cell-Based Communications (EMPhAtiC) Document Number D3.3 Reduction of PAPR and non linearities effects

Livrable d'un projet Européen EMPHATICLike other multicarrier modulation techniques, FBMC suffers from high peak-to-average power ratio (PAPR), impacting its performance in the presence of a nonlinear high power amplifier (HPA) in two ways. The first impact is an in-band distortion affecting the error rate performance of the link. The second impact is an out-of-band effect appearing as power spectral density (PSD) regrowth, making the coexistence between FBMC based broad-band Professional Mobile Radio (PMR) systems with existing narrowband systems difficult to achieve. This report addresses first the theoretical analysis of in-band HPA distortions in terms of Bit Error Rate. Also, the out-of band impact of HPA nonlinearities is studied in terms of PSD regrowth prediction. Furthermore, the problem of PAPR reduction is addressed along with some HPA linearization techniques and nonlinearity compensation approaches

Energy-Efficient Distributed Estimation by Utilizing a Nonlinear Amplifier

abstract: Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given parameter. It is frequently implemented using wireless sensor networks. There have been several studies on optimizing power allocation in wireless sensor networks used for distributed estimation, the vast majority of which assume linear radio-frequency amplifiers. Linear amplifiers are inherently inefficient, so in this dissertation nonlinear amplifiers are examined to gain efficiency while operating distributed sensor networks. This research presents a method to boost efficiency by operating the amplifiers in the nonlinear region of operation. Operating amplifiers nonlinearly presents new challenges. First, nonlinear amplifier characteristics change across manufacturing process variation, temperature, operating voltage, and aging. Secondly, the equations conventionally used for estimators and performance expectations in linear amplify-and-forward systems fail. To compensate for the first challenge, predistortion is utilized not to linearize amplifiers but rather to force them to fit a common nonlinear limiting amplifier model close to the inherent amplifier performance. This minimizes the power impact and the training requirements for predistortion. Second, new estimators are required that account for transmitter nonlinearity. This research derives analytically and confirms via simulation new estimators and performance expectation equations for use in nonlinear distributed estimation. An additional complication when operating nonlinear amplifiers in a wireless environment is the influence of varied and potentially unknown channel gains. The impact of these varied gains and both measurement and channel noise sources on estimation performance are analyzed in this paper. Techniques for minimizing the estimate variance are developed. It is shown that optimizing transmitter power allocation to minimize estimate variance for the most-compressed parameter measurement is equivalent to the problem for linear sensors. Finally, a method for operating distributed estimation in a multipath environment is presented that is capable of developing robust estimates for a wide range of Rician K-factors. This dissertation demonstrates that implementing distributed estimation using nonlinear sensors can boost system efficiency and is compatible with existing techniques from the literature for boosting efficiency at the system level via sensor power allocation. Nonlinear transmitters work best when channel gains are known and channel noise and receiver noise levels are low.Dissertation/ThesisPh.D. Electrical Engineering 201

Machine learning techniques for self-interference cancellation in full-duplex systems

Full-duplex (FD), enabling remote parties to transfer information simultaneously in both directions and in the same bandwidth, has been envisioned as an important technology for the next-generation wireless networks. This is due to the ability to leverage both time and frequency resources and theoretically double the spectral efficiency. Enabling the FD communications is, however, highly challenging due to the self-interference (SI), a leakage signal from the FD transmitter (Tx) to its own receiver (Rx). The power of the SI is significantly higher when compared with the signal of interest (SoI) from a remote node due to the proximity of the Tx to its co-located Rx. The SI signal is thus swamping the SoI and degrading the FD system's performance. Traditional self-interference cancellation (SIC) approaches, spanning the propagation, analog, and/or digital domains, have been explored to cancel the SI in FD transceivers. Particularly, digital domain cancellation is typically performed using model-driven approaches, which have proven to be effective for SIC; however, they could impose additional cost, hardware, memory, and/or computational requirements. Motivated by the aforementioned, this thesis aims to apply data-driven machine learning (ML)-assisted SIC approaches to cancel the SI in FD transceivers|in the digital domain|and address the extra requirements imposed by the traditional methods. Specifically, in Chapter 2, two grid-based neural network (NN) structures, referred to as ladder-wise grid structure and moving-window grid structure, are proposed to model the SI in FD transceivers with lower memory and computational requirements than the literature benchmarks. Further reduction in the computational complexity is provided in Chapter 3, where two hybrid-layers NN structures, referred to as hybrid-convolutional recurrent NN and hybrid-convolutional recurrent dense NN, are proposed to model the FD SI. The proposed hybrid NN structures exhibit lower computational requirements than the grid-based structures and without degradation in the SIC performance. In Chapter 4, an output-feedback NN structure, referred to as the dual neurons-` hidden layers NN, is designed to model the SI in FD transceivers with less memory and computational requirements than the grid-based and hybrid-layers NN structures and without any additional deterioration to the SIC performance. In Chapter 5, support vector regressors (SVRs), variants of support vector machines, are proposed to cancel the SI in FD transceivers. A case study to assess the performance of SVR-based approaches compared to the classical and other ML-based approaches, using different performance metrics and two different test setups, is also provided in this chapter. The SVR-based SIC approaches are able to reduce the training time compared to the NN-based approaches, which are, contrarily, shown to be more efficient in terms of SIC, especially when high transmit power levels are utilized. To further enhance the performance/complexity of the ML approaches provided in Chapter 5, two learning techniques are investigated in Chapters 6 and 7. Specifically, in Chapter 6, the concept of residual learning is exploited to develop an NN structure, referred to as residual real-valued time-delay NN, to model the FD SI with lower computational requirements than the benchmarks of Chapter 5. In Chapter 7, a fast and accurate learning algorithm, namely extreme learning machine, is proposed to suppress the SI in FD transceivers with a higher SIC performance and lower training overhead than the benchmarks of Chapter 5. Finally, in Chapter 8, the thesis conclusions are provided and the directions for future research are highlighted

Compensation of nonlinear distortion in RF amplifiers for mobile communications

Compensation of nonlinear distortion of power amplifiers in mobile communications is an important requirement for improving power consumption performance while maintaining efficiency, since mobile phone became an essential accessory for everyone nowadays. This problem demands a good power amplifier model, in order to develop an effective predistortion system. Current researches are focused on modelling and predistortion of power amplifiers with memory, as well as memoryless ones. Different methods for modelling are used, as the Volterra series, polynomial models, look-up tables, the Hammerstein models, the Wiener models, and artificial intelligence systems. For predistortion feedback, feedforward and digital predistortion techniques are used. Among digital predistortion methods there are artificial intelligence systems, used in this thesis for linearization of power amplifier. This thesis presents developed robust method for modelling power amplifiers without memory effects and gives a comparison of proposed method with least squares method. Also, this research presents two novel techniques based on artificial intelligence systems for modelling and predistortion of highly nonlinear power amplifier with memory. The first approach is based on artificial neural networks, while the second one uses adaptive fuzzy logic systems. Forward and inverse models of power amplifier are created with both proposed methods. Superiority of artificial intelligence systems over partial least squares method is presented. Developed models are employed in a cascade to make a linearized system. Verification of proposed methods is carried out through the signal performance parameters and spectra of measured signal and signal from predistortion system. The feasibility and performances of the proposed digital predistortions are examined by simulations and experiments. The comparison of proposed methods is given to present advantages/disadvantages of both methods. The achieved distortion suppression from 72.2% to 93.6% and spectral regrowth improvement from 11.4 dB to 16.2 dB prove that the proposed methods have great ability to compensate the nonlinear distortion in power amplifier