Pré-distorsion adaptative des non-linéarités HPA dans un système OFDM à l’aide des réseaux de neurones

The aim of the research accomplished in this thesis is to introduce linearization techniques applied to power amplifier for broadband radio- ommunications. In this context, we put forward base-band adaptive pre-distortion techniques using neural networks. After showing the non-linear behavior of power amplifier on both the spectrum and the transmitted signal quality, the classic techniques of linearity improvement are introduced. In the second section, neural networks are presented in particular the multilayer perceptrons as well as their learning techniques. Next, we defined the necessary steps for the establishment of a neural pre-distortion architecture able to offer very good performances within a reasonable complexity forthe non-linearity’s compensation of stationary and memoryless power amplifier, in the context of OFDM systems. The pre-distortion architecture chosen proved good performances in terms of the error rate reducing even with relatively low values of IBO. It equally proved a good correction of the transmitted signal quality in both inside and outside his band. The performance degradation correction following the non-stationary behavior ofpower amplifier has been stressed. Hence, a new architecture of adaptive predistortion has been proposed which allowed to obtain good performances using Levenberg-Marquard algorithm that proved to be the most efficient compared to several other algorithms, in terms of reducing time to adapt to power amplifier behavior changes while noting a reduced complexity. The obtained simulation results justify this hypothesis.Finally, a theoretical analysis allowed to highlight the dispersion origins in the power amplifier response and led to the emergence of new neural architecture of predistortion able to take into account memory effects. This survey concludes with some perspectives for research able to extend the work done during this thesis.Les travaux de recherche menés dans cette thèse de doctorat ont visé l’étude et la mise en œuvre des techniques de linéarisation d’amplificateurs de puissance destinés aux émetteurs radiofréquences à large bande. Dans ce cadre, nous avons proposé des techniques de pré-distorsion adaptative en bande de base en utilisant les réseaux de neurones. Après avoir montré les conséquences du comportement non linéaire de l’amplificateur sur le spectre et la qualité du signal transmis, les techniques classiques d’amélioration de la linéarité sont présentées. Dans une deuxième partie, les réseaux de neurones sont présentés et en particulier les perceptrons multicouches sans oublier également les techniques d’apprentissage de ces derniers. Ensuite, nous avons défini les étapes nécessaires pour l’établissement d’une architecture neuronale de pré- istorsion capable d’offrir de très bonnes performances avec une complexité raisonnable pour la compensation des non-linéarités d’un amplificateur considéré stationnaire et sans mémoire, dans le contexte des systèmes de communication OFDM. L’architecture de pré-distorsion choisie a montré de très bonnes performances en terme de réduction du taux d’erreur dans la transmission même avec des IBO assez faibles. Elle a montré également une bonne correction du signal à l’intérieur de sa bande utile et en dehors de celle-ci. La dégradation des performances de correction suite à la non-stationnarité de l’amplificateur de puissance a été soulignée. Ainsi, une nouvelle architecture de pré-distorsion adaptative a été proposée. Cette architecture a permis d’obtenir de bonnes performances en utilisant l’algorithme de Levenberg-Marquardt qui s’est avéré le plus efficace, par rapport à plusieurs autres algorithmes, en terme de réduction du temps d’adaptation aux changements du comportement de l’amplificateur tout en notant une complexité réduite. Les résultats de simulation obtenus justifient cette hypothèse. Finalement, une analyse théorique a permis de mettre en évidence les origines de dispersion dans la réponse de l’amplificateur de puissance et a conduit à proposer de nouvelles architectures neuronales de pré-distorsion capables de tenir compte des effets de mémoire. Ce mémoire se conclut par quelques perspectives de recherche pouvant prolonger les travaux accomplis durant cette thèse

Energy-Efficient Uplink Cell-Free Massive MIMO through Distributed Cancellation Technique of HWIs

International audienceIn this paper, we examine the uplink data transmission of cell-free massive MIMO orthogonal frequency division multiplexing (CF-mMIMO-OFDM) systems while taking into account the effects of hardware impairments (HWIs). These latter are dominated by the nonlinear distortions (NLD) caused by power amplifiers (PAs) at user equipments (UEs). Considering local receive combining at access points (APs), a distributed approach is introduced to alleviate the impact of HWIs on the system performnce using an iterative NLD cancellation technique. Interestingly, the proposed technique can be implemented in a distributed and scalable manner, demonstrating the benefits of CF-mMIMO-OFDM. The outcomes of the simulations indicate that the proposed technique has a great promise in mitigating the significant impact of HWIs, improving the spectral efficiency (SE) and energy efficiency (EE) performance of the uplink CFmMIMO-OFDM systems

Low‐complexity linear precoding for low‐PAPR massive MU‐MIMO‐OFDM downlink systems

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Towards energy-efficient 6G networks: uplink cell-free massive MIMO with NLD cancellation technique of hardware impairments

International audienceThis paper focuses on investigating uplink (UL) data transmission in cell-free massive MIMO based on orthogonal frequency division multiplexing (CF-mMIMO-OFDM) systems, taking into account the effects of hardware impairments (HWIs). Specifically, the HWIs arise from nonlinear distortions (NLD) caused by power amplifiers (PAs) at user equipment (UEs). These NLDs have a significant impact on both channel estimation and data transmission in UL CF-mMIMO-OFDM. To mitigate NLDs while maintaining a good power-efficiency, we propose a successive NLD approach that is adequate for CF-mMIMO-OFDM. Specifically, a novel frequency domain channel estimation method is introduced that incorporates NLD cancellation. This method aims to accurately estimate the channel despite the presence of NLDs. Additionally, a successive combining-aware NLD cancellation is proposed to mitigate the NLD impact on data detection. Not that three combinng schemes are adopted, namely maximum-ratio (MR), zero-forcing (FZF), and partial-FZF (PFZF). Most-importantly, the proposed techniques are designed to be implemented in a distributed and scalable manner, highlighting the advantages of CF-mMIMO-OFDM systems. The performance of the proposed techniques are evaluated with simulations when considereing the the combining schemes. Results show the capability of our proposed NLD cancellation approach to improve both channel estimation and data detection, especially when levreaging the good features of PFZF combing scheme. For objective comparison purpose, we derived closed-form expressions on UL spectral-efficiency (SE) performance of an UL CF-mMIMO-OFDM system in presence of ideal and nonlinear PA

A Novel Analog Pre-distorter of TWTA Non-linearity in High Power Satellite Transmitters

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Efficient Precoding for Massive MIMO Downlink Under PA Nonlinearities

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An adaptive neural network pre-distorter for non stationary HPA in OFDM systems

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Levenberg-Marquardt learning neural network for adaptive predistortion for time-varying HPA with memory in OFDM systems

International audienceThis paper presents a new adaptive pre-distortion (PD) technique, based on neural networks (NN) with tap delay line for linearization of High Power Amplifier (HPA) exhibiting memory effects. The adaptation, based on iterative algorithm, is derived from direct learning for the NN PD. Equally important, the paper puts forward the studies concerning the application of different NN learning algorithms in order to determine the most adequate for this NN PD. This comparison examined through computer simulation for 64 carriers and 16-QAM OFDM system, is based on some quality measure (Mean Square Error), the required training time to reach a particular quality level and computation complexity. The chosen adaptive pre-distortion (NN structure associated with an adaptive algorithm) have a low complexity, fast convergence and best performance

Crossover Neural Network Predistorter for the Compensation of Crosstalk and Nonlinearity in MIMO OFDM Systems

The combination of Multiple-Input Multiple-Output (MIMO) with Orthogonal Frequency Division Multiplexing (OFDM) is a promising technique for achieving high performance with very high data rates in 4G broadband wireless communications. Nevertheless, one major disadvantage of MIMO-OFDM systems lies in a prohibitively large Peak-to-Average Power Ratio (PAPR) of the transmitted signal on each antenna. Indeed, the performance of the receivers is very sensitive to nonlinear distortions caused by the High Power Amplifier (HPA). Furthermore, Crosstalk can take place before or after the power amplifiers designated herein as nonlinear and linear crosstalk, respectively. In this paper, we extend the efficient Neural Network Predistorter (NNPD) proposed in [1] to MIMO-OFDM systems and equally demonstrate that nonlinear crosstalk significantly affects the performance of NNPD. Along, we propose a new Crossover Neural Network Predistorter (CO-NNPD) model to compensate simultaneously for crosstalk and HPA nonlinearity in MIMO-OFDM systems. The Levenberg-Marquardt algorithm (LM) is used for neural network training, which has proven [3] to exhibit a very good performance with lower computation complexity and faster convergence than other algorithms used in literature. This paper is supported with simulation results for the Alamouti STBC MIMO OFDM system in terms of Bit Error Rate (BER) in Rayleigh fading channel