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

Modeling of complex-valued Wiener systems using B-spline neural network

In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate Bspline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss–Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches

Advanced signal processing techniques for the modeling and linearization of wireless communication systems.

Los nuevos estándares de comunicaciones digitales inalámbricas están impulsando el diseño de amplificadores de potencia con unas condiciones límites en términos de linealidad y eficiencia. Si bien estos nuevos sistemas exigen que los dispositivos activos trabajen cerca de la zona de saturación en busca de la eficiencia energética, la no linealidad inherente puede producir que el sistema muestre prestaciones inadecuadas en emisiones fuera de banda y distorsión en banda. La necesidad de técnicas digitales de compensación y la evolución en el diseño de nuevas arquitecturas de procesamiento de señales digitales posicionan a la predistorsión digital (DPD) como un enfoque práctico. Los predistorsionadores digitales se suelen basar en modelos de comportamiento como el memory polynomial (MP), el generalized memory polynomial (GMP) y el dynamic deviation reduction-based (DDR), etc. Los modelos de Volterra sufren la llamada "maldición de la dimensionalidad", ya que su complejidad tiende a crecer de forma exponencial a medida que el orden y la profundidad de memoria crecen. Esta tesis se centra principalmente en contribuir a la rama de conocimiento que enmarca el modelado y linealización de sistemas de comunicación inalámbrica. Los principales temas tratados son el modelo Volterra-Parafac y el modelo general de Volterra para sistemas complejos, los cuales tratan la estructura del DPD y las series de Volterra estructuradas con compressed-sensing y un método para la linealización en un rango de potencias de operación, que se centran en cómo los coeficientes de los modelos deben ser obtenidos.Premio Extraordinario de Doctorado U

Frequency-domain digital predistortion for Massive MU-MIMO-OFDM Downlink

Digital predistortion (DPD) is a method commonly used to compensate for the nonlinear effects of power amplifiers (PAs). However, the computational complexity of most DPD algorithms becomes an issue in the downlink of massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM), where potentially up to several hundreds of PAs in the base station (BS) require linearization. In this paper, we propose a convolutional neural network (CNN)-based DPD in the frequency domain, taking place before the precoding, where the dimensionality of the signal space depends on the number of users, instead of the number of BS antennas. Simulation results on generalized memory polynomial (GMP)-based PAs show that the proposed CNN-based DPD can lead to very large complexity savings as the number of BS antenna increases at the expense of a small increase in power to achieve the same symbol error rate (SER)

Frequency-domain digital predistortion for Massive MU-MIMO-OFDM Downlink

Digital predistortion (DPD) is a method commonly used to compensate for the nonlinear effects of power amplifiers (sPAs). However, the computational complexity of most DPD algorithms becomes an issue in the downlink of massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM), where potentially up to several hundreds of PAs in the base station (BS) require linearization. In this paper, we propose a convolutional neural network (CNN)-based DPD in the frequency domain, taking place before the precoding, where the dimensionality of the signal space depends on the number of users, instead of the number of BS antennas. Simulation results on generalized memory polynomial (GMP)-based PAs show that the proposed CNN-based DPD can lead to very large complexity savings as the number of BS antenna increases at the expense of a small increase in power to achieve the same symbol error rate (SER).acceptedVersionPeer reviewe

Analysis of the Combinatory Effect of Uniaxial Electrical and Magnetic Anisotropy on the Input Impedance and Mutual Coupling of a Printed Dipole Antenna

The main objective of this work is to investigate the combinatory effects of both uniaxial magnetic and electrical anisotropies on the input impedance, resonant length and the mutual coupling between two dipoles printed on an anisotropic grounded substrate. Three different configurations: broadside, collinear and echelon are considered for the coupling investigation. The study is based on the numerical solution of the integral equation using the method of moments through the mathematical derivation of the appropriate Green's functions in the spectral domain. In order to validate the computing method and evaluated Matlab? calculation code, numerical results are compared with available literature treating particular cases of uniaxial electrical anisotropy; good agreements are observed. New results of dipole structures printed on uniaxial magnetic anisotropic substrates are presented and discussed, with the investigation of the combined electrical and magnetic anisotropies effect on the input impedance and mutual coupling for different geometrical configurations. The combined uniaxial (electric and magnetic) anisotropies provide additional degrees of freedom for the input impedance control and coupling reduction

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

Toward Energy-Efficient Massive MIMO: Graph Neural Network Precoding for Mitigating Non-Linear PA Distortion

Massive MIMO systems are typically designed assuming linear power amplifiers (PAs). However, PAs are most energy efficient close to saturation, where non-linear distortion arises. For conventional precoders, this distortion can coherently combine at user locations, limiting performance. We propose a graph neural network (GNN) to learn a mapping between channel and precoding matrices, which maximizes the sum rate affected by non-linear distortion, using a high-order polynomial PA model. In the distortion-limited regime, this GNN-based precoder outperforms zero forcing (ZF), ZF plus digital pre-distortion (DPD) and the distortion-aware beamforming (DAB) precoder from the state-of-the-art. At an input back-off of -3 dB the proposed precoder compared to ZF increases the sum rate by 8.60 and 8.84 bits/channel use for two and four users respectively. Radiation patterns show that these gains are achieved by transmitting the non-linear distortion in non-user directions. In the four user-case, for a fixed sum rate, the total consumed power (PA and processing) of the GNN precoder is 3.24 and 1.44 times lower compared to ZF and ZF plus DPD respectively. A complexity analysis shows six orders of magnitude reduction compared to DAB precoding. This opens perspectives to operate PAs closer to saturation, which drastically increases their energy efficiency