Peak to average power ratio reduction and error control in MIMO-OFDM HARQ System

Currently, multiple-input multiple-output orthogonal frequency division multiplexing (MIMOOFDM) systems underlie crucial wireless communication systems such as commercial 4G and 5G networks, tactical communication, and interoperable Public Safety communications. However, one drawback arising from OFDM modulation is its resulting high peak-to-average power ratio (PAPR). This problem increases with an increase in the number of transmit antennas. In this work, a new hybrid PAPR reduction technique is proposed for space-time block coding (STBC) MIMO-OFDM systems that combine the coding capabilities to PAPR reduction methods, while leveraging the new degree of freedom provided by the presence of multiple transmit chairs (MIMO). In the first part, we presented an extensive literature review of PAPR reduction techniques for OFDM and MIMO-OFDM systems. The work developed a PAPR reduction technique taxonomy, and analyzed the motivations for reducing the PAPR in current communication systems, emphasizing two important motivations such as power savings and coverage gain. In the tax onomy presented here, we include a new category, namely, hybrid techniques. Additionally, we drew a conclusion regarding the importance of hybrid PAPR reduction techniques. In the second part, we studied the effect of forward error correction (FEC) codes on the PAPR for the coded OFDM (COFDM) system. We simulated and compared the CCDF of the PAPR and its relationship with the autocorrelation of the COFDM signal before the inverse fast Fourier transform (IFFT) block. This allows to conclude on the main characteristics of the codes that generate high peaks in the COFDM signal, and therefore, the optimal parameters in order to reduce PAPR. We emphasize our study in FEC codes as linear block codes, and convolutional codes. Finally, we proposed a new hybrid PAPR reduction technique for an STBC MIMO-OFDM system, in which the convolutional code is optimized to avoid PAPR degradation, which also combines successive suboptimal cross-antenna rotation and inversion (SS-CARI) and iterative modified companding and filtering schemes. The new method permits to obtain a significant net gain for the system, i.e., considerable PAPR reduction, bit error rate (BER) gain as compared to the basic MIMO-OFDM system, low complexity, and reduced spectral splatter. The new hybrid technique was extensively evaluated by simulation, and the complementary cumulative distribution function (CCDF), the BER, and the power spectral density (PSD) were compared to the original STBC MIMO-OFDM signal

A hybrid-structure offset-QAM filter-bank multi-carrier MIMO system

Offset quadrature amplitude modulation (OQAM) filter-bank multi-carrier (FBMC), has great potential for boosting the spectral efficiency (SE) and energy efficiency (EE) of future communication systems. This is due to its superior spectral localization, CP-less transmission and relaxed synchronization requirements. Our research focuses on three main OQAM/FBMC research problems: the computational complexity reduction taking equalization into consideration, its integration with multiple-input multiple-output (MIMO) and its high peak-to-average power ratio (PAPR). OQAM/FBMC systems are mainly implemented either using frequency spreading (FS) or polyphase network (PPN) techniques. The PPN technique is generally less complex, but when using frequency domain equalization (FDE) to equalize multipath channel effects at the receiver, there is a computational complexity overhead when using PPN. A novel hybrid-structure OQAM/FBMC MIMO space-frequency block coding (SFBC) system is proposed, to achieve the lowest possible overall complexity in conjunction with FDE at the receiver in frequency selective Rayleigh fading channel. The Alamouti SFBC block coding is performed on the complex-orthogonal signal before OQAM processing, which resolves the problems of intrinsic interference when integrating OQAM/FBMC with MIMO. In better multipath channel conditions with a line-of-sight (LOS) path, a zero-forcing (ZF) time domain equalization (TDE) is exploited to further reduce the computational complexity with comparable performance bit-error-rate (BER). On the other hand, to tackle the high PAPR problem of the OQAM/FBMC system in the uplink, a novel single carrier (SC)-OQAM/FBMC MIMO system is proposed. The system uses DFT-spreading applied to the OQAM modulated signal, along with interleaved subcarrier mapping to significantly reduce the PAPR and enhance the BER performance over Rayleigh fading channels, with relatively low additional computational complexity compared to the original complexity of the FBMC system and compared to other FBMC PAPR reduction techniques.The proposed hybrid-structure system has shown significant BER performance in frequency-selective Rayleigh fading channels compared to OFDM, with significantly lower OOB emissions in addition to the enhanced SE due to the absence of CP. In mild multipath fading channels with a LOS component, the PPN OQAM/FBMC MIMO using TDE has a comparable BER performance with significantly less computational complexity. As for the uplink, the SC-OQAM/FBMC MIMO system significantly reduces the PAPR and enhances the BER performance, with relatively low additional computational complexity

Future Transportation

Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications