Experimental Evaluation of Hybrid Fibre−Wireless System for 5G Networks

This article describes a novel experimental study considering a multiband fibre–wireless system for constructing the transport network for fifth-generation (5G) networks. This study describes the development and testing of a 5G new radio (NR) multi-input multi-output (MIMO) hybrid fibre–wireless (FiWi) system for enhanced mobile broadband (eMBB) using digital pre-distortion (DPD). Analog radio over fibre (A-RoF) technology was used to create the optical fronthaul (OFH) that includes a 3 GHz supercell in a long-range scenario as well as a femtocell scenario using the 20 GHz band. As a proof of concept, a Mach Zehnder modulator with two independent radio frequency waveforms modifies a 1310 nm optical carrier using a distributed feedback laser across 10 km of conventional standard single-mode fibre. It may be inferred that a hybrid FiWi-based MIMO-enabled 5G NR system based on OFH could be a strong competitor for future mobile haul applications. Moreover, a convolutional neural network (CNN)-based DPD is used to improve the performance of the link. The error vector magnitude (EVM) performance for 5G NR bands is predicted to fulfil the Third Generation Partnership Project’s (3GPP) Release 17 standards

Learning Maximum Margin Channel Decoders

The problem of learning a channel decoder is considered for two channel models. The first model is an additive noise channel whose noise distribution is unknown and nonparametric. The learner is provided with a fixed codebook and a dataset comprised of independent samples of the noise, and is required to select a precision matrix for a nearest neighbor decoder in terms of the Mahalanobis distance. The second model is a non-linear channel with additive white Gaussian noise and unknown channel transformation. The learner is provided with a fixed codebook and a dataset comprised of independent input-output samples of the channel, and is required to select a matrix for a nearest neighbor decoder with a linear kernel. For both models, the objective of maximizing the margin of the decoder is addressed. Accordingly, for each channel model, a regularized loss minimization problem with a codebook-related regularization term and hinge-like loss function is developed, which is inspired by the support vector machine paradigm for classification problems. Expected generalization error bounds for the error probability loss function are provided for both models, under optimal choice of the regularization parameter. For the additive noise channel, a theoretical guidance for choosing the training signal-to-noise ratio is proposed based on this bound. In addition, for the non-linear channel, a high probability uniform generalization error bound is provided for the hypothesis class. For each channel, a stochastic sub-gradient descent algorithm for solving the regularized loss minimization problem is proposed, and an optimization error bound is stated. The performance of the proposed algorithms is demonstrated through several examples

Communications protocols for wireless sensor networks in perturbed environment

This thesis is mainly in the Smart Grid (SG) domain. SGs improve the safety of electrical networks and allow a more adapted use of electricity storage, available in a limited way. SGs also increase overall energy efficiency by reducing peak consumption. The use of this technology is the most appropriate solution because it allows more efficient energy management. In this context, manufacturers such as Hydro-Quebec deploy sensor networks in the nerve centers to control major equipment. To reduce deployment costs and cabling complexity, the option of a wireless sensor network seems the most obvious solution. However, deploying a sensor network requires in-depth knowledge of the environment. High voltages substations are strategic points in the power grid and generate impulse noise that can degrade the performance of wireless communications. The works in this thesis are focused on the development of high performance communication protocols for the profoundly disturbed environments. For this purpose, we have proposed an approach based on the concatenation of rank metric and convolutional coding with orthogonal frequency division multiplexing. This technique is very efficient in reducing the bursty nature of impulsive noise while having a quite low level of complexity. Another solution based on a multi-antenna system is also designed. We have proposed a cooperative closed-loop coded MIMO system based on rank metric code and max−dmin precoder. The second technique is also an optimal solution for both improving the reliability of the system and energy saving in wireless sensor networks

Performance analysis of energy harvesting relaying

Recently, energy harvesting has been exploited as a key technique in wireless communications. Because conventional wireless systems are powered by batteries and cables, they tend to have restricted lifetime and flexibility. In order to solve these problems, wireless power has been investigated as a replacement for conventional batteries. This thesis focuses on energy harvesting in relaying. The data packet from the source to relay contains three parts: pilot for channel estimation, data symbols and pilots for harvesting. The data packet from the relay to the destination contains two parts: data symbols and pilots for estimation. To study energy harvesting, the performance of wireless powered communications is evaluated in terms of achievable rate and bit error rate, for applications where the downlink and the uplink are correlated, in contrast to previous works that assume independent uplink and downlink. Semi-closed expressions for the achievable rate and series expressions for the bit error rate are derived in Nakagami m fading channels, based on which the effect of link correlation is examined. Numerical results show that the link correlation has a significant impact on the achievable rate. Consequently, the optimum system parameter for correlated links is very different from that for independent links, showing the usefulness of our results. Also, the link correlation has a noticeable effect on the bit error rate, depending on the system parameters considered. Then, performance analysis has been performed for an AF relaying system with pilot-based channel estimation and time switching (TS) energy harvesting is conducted. Numerical results show the existence of the optimal values of the numbers of pilots for channel estimation and for energy harvesting, when the total size is fixed. Next, three novel structures using simultaneous wireless information and power transfer in energy harvesting amplify-and-forward (AF) relaying are investigated. Different combinations of time-switching (TS) and power-splitting (PS) energy harvesting protocols are studied. Closed-form expressions for the cumulative distribution function (CDF) of the end-to-end signal-to-noise ratio (SNR) for the three structures are derived. Using these expressions, achievable rate (AR) and bit-error-rate (BER) are derived. Different parameters are examined. Numerical results show the optimal splitting ratio for channel estimation, energy harvesting and data transmission, when the packet size is fixed. Finally, the energy from the source and the energy from the ambient are merged together. The three ambient structures are studied. The closed-form expressions for the cumulative distribution function (CDF) of the end-to-end signal-to- noise ratio (SNR) for the three ambient structures are derived. Curve fitting has been used to achieve the approximately achievable rate (AR) and bit-error-rate (BER). The results provide the optimal values for channel estimation pilots and power splitting ratio series for these ambient RF added structures

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes