Simplified Levenberg-Marquardt Algorithm based PAPR Reduction for OFDM System with Neural Network

In recent years, OFDM is the key transmission technique in the communication system. This is because of the high channel estimation, strong against multipath fading and increased spectral efficiency. Because of the independently modulated subcarriers, the Peak to Average Power Ratio (PAPR) is very high in OFDM systems. Previously we use a number of PAPR reduction schemes using clipping, adding windows etc. But in these methods we cannot achieve the optimum reduction or the BER performance is high or the system is very complex. On considering the BER performance and system complexity we employ a new method based on the Neural Network (NN). In this new method we achieve significant PAPR reduction with great BER improvement and complexity reduction. In the simulations we seen that the PAPR reduction and BER performance are very good. DOI: 10.17762/ijritcc2321-8169.15080

End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints

Orthogonal frequency-division multiplexing (OFDM) is widely used in modern wireless networks thanks to its efficient handling of multipath environment. However, it suffers from a poor peak-to-average power ratio (PAPR) which requires a large power backoff, degrading the power amplifier (PA) efficiency. In this work, we propose to use a neural network (NN) at the transmitter to learn a high-dimensional modulation scheme allowing to control the PAPR and adjacent channel leakage ratio (ACLR). On the receiver side, a NN-based receiver is implemented to carry out demapping of the transmitted bits. The two NNs operate on top of OFDM, and are jointly optimized in and end-to-end manner using a training algorithm that enforces constraints on the PAPR and ACLR. Simulation results show that the learned waveforms enable higher information rates than a tone reservation baseline, while satisfying predefined PAPR and ACLR targets

Reduction of power envelope fluctuations in OFDM signals by using neural networks

One of the main drawbacks of Orthogonal Frequency Division Multiplexing (OFDM) are the large fluctuations of its power envelope. In this letter, a novel and efficient scheme based on Multilayer Perceptron (MLP) Neural Networks (NN) is proposed. The NN synthesizes the Active Constellation Expansion - (ACE) technique which is able to drastically reduce envelope fluctuations. This is achieved with much lower complexity, faster convergence, and better performance compared to previously available methods.This work has been partly funded by the projects MULTI-ADAPTIVE (TEC2008-06327-C03-02), COMONSENS (CSD2008-00010), and the AECI Program of Research Cooperation with Morocco.Publicad

Practical Guidelines for Approaching the Implementation of Neural Networks on FPGA for PAPR Reduction in Vehicular Networks

Nowadays, the sensor community has become wireless, increasing their potential and applications. In particular, these emerging technologies are promising for vehicles' communications (V2V) to dramatically reduce the number of fatal roadway accidents by providing early warnings. The ECMA-368 wireless communication standard has been developed and used in wireless sensor networks and it is also proposed to be used in vehicular networks. It adopts Multiband Orthogonal Frequency Division Multiplexing (MB-OFDM) technology to transmit data. However, the large power envelope fluctuation of OFDM signals limits the power efficiency of the High Power Amplifier (HPA) due to nonlinear distortion. This is especially important for mobile broadband wireless and sensors in vehicular networks. Many algorithms have been proposed for solving this drawback. However, complexity and implementations are usually an issue in real developments. In this paper, the implementation of a novel architecture based on multilayer perceptron artificial neural networks on a Field Programmable Gate Array (FPGA) chip is evaluated and some guidelines are drawn suitable for vehicular communications. The proposed implementation improves performance in terms of Peak to Average Power Ratio (PAPR) reduction, distortion and Bit Error Rate (BER) with much lower complexity. Two different chips have been used, namely, Xilinx and Altera and a comparison is also provided. As a conclusion, the proposed implementation allows a minimal consumption of the resources jointly with a higher maximum frequency, higher performance and lower complexity.This work has been partly funded by projects TERESA-ADA (TEC2017-90093-C3-2-R) (MINECO/AEI/FEDER, UE) and ELISA (TEC2014-59255-C3-3-R)

Spectrally and Energy Efficient Wireless Communications: Signal and System Design, Mathematical Modelling and Optimisation

This thesis explores engineering studies and designs aiming to meeting the requirements of enhancing capacity and energy efficiency for next generation communication networks. Challenges of spectrum scarcity and energy constraints are addressed and new technologies are proposed, analytically investigated and examined. The thesis commences by reviewing studies on spectrally and energy-efficient techniques, with a special focus on non-orthogonal multicarrier modulation, particularly spectrally efficient frequency division multiplexing (SEFDM). Rigorous theoretical and mathematical modelling studies of SEFDM are presented. Moreover, to address the potential application of SEFDM under the 5th generation new radio (5G NR) heterogeneous numerologies, simulation-based studies of SEFDM coexisting with orthogonal frequency division multiplexing (OFDM) are conducted. New signal formats and corresponding transceiver structure are designed, using a Hilbert transform filter pair for shaping pulses. Detailed modelling and numerical investigations show that the proposed signal doubles spectral efficiency without performance degradation, with studies of two signal formats; uncoded narrow-band internet of things (NB-IoT) signals and unframed turbo coded multi-carrier signals. The thesis also considers using constellation shaping techniques and SEFDM for capacity enhancement in 5G system. Probabilistic shaping for SEFDM is proposed and modelled to show both transmission energy reduction and bandwidth saving with advantageous flexibility for data rate adaptation. Expanding on constellation shaping to improve performance further, a comparative study of multidimensional modulation techniques is carried out. A four-dimensional signal, with better noise immunity is investigated, for which metaheuristic optimisation algorithms are studied, developed, and conducted to optimise bit-to-symbol mapping. Finally, a specially designed machine learning technique for signal and system design in physical layer communications is proposed, utilising the application of autoencoder-based end-to-end learning. Multidimensional signal modulation with multidimensional constellation shaping is proposed and optimised by using machine learning techniques, demonstrating significant improvement in spectral and energy efficiencies

High power amplifier pre-distorter based on neural-fuzzy systems for OFDM signals

In this paper, a novel High Power Amplifier (HPA) pre-distorter based on Adaptive Networks - Fuzzy Inference Systems (ANFIS) for Orthogonal Frequency Division Multiplexing (OFDM) signals is proposed and analyzed. Models of Traveling Wave Tube Amplifiers (TWTA) and Solid State Power Amplifiers (SSPA), both memoryless and with memory, have been used for evaluation of the proposed technique. After training, the ANFIS linearizes the HPA response and thus, the obtained signal is extremely similar to the original. An average Error Vector Magnitude (EVM) of 10-6 can be easily obtained with our proposal. As a consequence, the Bit Error Rate (BER) degradation is negligible showing a better performance than what can be achieved with other methods available in the literature. Moreover, the complexity of the proposed scheme is reducedThis work was supported in part by projectsMULTIADAPTIVE (TEC2008-06327-C03-02) and AECI Program of Research Cooperation with MoroccoPublicad

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