A Comparison of Several Gradient Based Optimization Algorithms for PAPR Reduction in OFDM Systems

International audienceThe aim of this paper is to evaluate and compare different optimization algorithms for Peak to Average Power Ratio (PAPR) reduction in Orthogonal Frequency Division Multiplexing (OFDM) systems. Based on Tone Reservation (TR) method, we exploit the unused subcarriers of the studied standard to generate the peak canceling signal without data rate loss. Gradient, Conjugate-Gradient with two directions search and Quasi-Newton methods have been investigated and evaluated on the basis of spectral regrowth, convergence speed and ability to improve the high peak-to-average reduction in multicarriers systems. As an example, the simulations are performed in the case of Local Area Network WLAN (IEEE 802.11a standard). Simulation results show that a PAPR reduction gain around 3 dB can be achieved

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

A Closed Form Selected Mapping Algorithm for PAPR Reduction in OFDM Multicarrier Transmission

Nowadays, the demand for communication multi-carriers' channels, where the sub-channels are made mutually independent by using orthogonal frequency division multiplexing (OFDM), is widespread both for wireless and wired communication systems. Even if OFDM is a spectrally efficient modulation scheme, due to the allowed number of subcarriers, high data rate, and good coverage, the transmitted signal can present high peak values in the time domain, due to inverse fast Fourier transform operations. This gives rise to high peak-to-average power ratio (PAPR) with respect to single carrier systems. These peaks can saturate the transmitting amplifiers, modifying the shape of the OFDM symbol and affecting its information content, and they give rise to electromagnetic compatibility issues for the surrounding electric devices. In this paper, a closed form PAPR reduction algorithm is proposed, which belongs to selected mapping (SLM) methods. These methods consist in shifting the phases of the components to minimize the amplitude of the peaks. The determination of the optimal set of phase shifts is a very complex problem; therefore, the SLM approaches proposed in literature all resort to iterative algorithms. Moreover, as this calculation must be performed online, both the computational cost and the effect on the bit rate (BR) cannot be established a priori. The proposed analytic algorithm finds the optimal phase shifts of an approximated formulation of the PAPR. Simulation results outperform unprocessed conventional OFDM transmission by several dBs. Moreover, the complementary cumulative distribution function (CCDF) shows that, in most of the packets, the proposed algorithm reduces the PAPR if compared with randomly selected phase shifts. For example, with a number of shifted phases U=8, the CCFD curves corresponding to the analytical and random methods intersect at a probability value equal to 10(-2), which means that in 99% of cases the former method reduces the PAPR more than the latter one. This is also confirmed by the value of the gain, which, at the same number of shifted phases and at the probability value equal to 10(-1), changes from 2.09 dB for the analytical to 1.68 dB for the random SLM

A Comparison Study of PAPR Reduction in OFDM Systems Based on Swarm Intelligence Algorithms

Optimization algorithms have been one of the most important research topics in Computational Intelligence Community. They are widely utilized mathematical functions that solve optimization problems in a variety of purposes via the maximization or minimization of a function. The swarm intelligence (SI) optimization algorithms are an active branch of Evolutionary Computation, they are increasingly becoming one of the hottest and most important paradigms, several algorithms were proposed for tackling optimization problems. The most respected and popular SI algorithms are Ant colony optimization (ACO) and particle swarm optimization (PSO). Fireworks Algorithm (FWA) is a novel swarm intelligence algorithm, which seems effective at finding a good enough solution of a complex optimization problem. In this chapter we proposed a comparison study to reduce the high PAPR (Peak-to-Average Power Ratio) in OFDM systems based on the swarm intelligence algorithms like simulated annealing (SA), particle swarm optimization (PSO), fireworks algorithm (FWA), and genetic algorithm (GA). It turns out from the results that some algorithms find a good enough solutions and clearly outperform the others candidates in both convergence speed and global solution accuracy

Optimization of a power line communication system to manage electric vehicle charging stations in a smart grid

In this paper, a procedure is proposed to design a power line communication (PLC) system to perform the digital transmission in a distributed energy storage system consisting of fleets of electric cars. PLC uses existing power cables or wires as data communication multicarrier channels. For each vehicle, the information to be transmitted can be, for example: the models of the batteries, the level of the charge state, and the schedule of charging/discharging. Orthogonal frequency division multiplexing modulation (OFDM) is used for the bit loading, whose parameters are optimized to find the best compromise between the communication conflicting objectives of minimizing the signal power, maximizing the bit rate, and minimizing the bit error rate. The off-line design is modeled as a multi-objective optimization problem, whose solution supplies a set of Pareto optimal solutions. At the same time, as many charging stations share part of the transmission line, the optimization problem includes also the assignment of the sub-carriers to the single charging stations. Each connection between the control node and a charging station has its own frequency response and is affected by a noise spectrum. In this paper, a procedure is presented, called Chimera, which allows one to solve the multi-objective optimization problem with respect to a unique frequency response, representing the whole set of lines connecting each charging station with the central node. Among the provided Pareto solutions, the designer will make the final decision based on the control system requirements and/or the hardware constraints

Low-Complexity Detection/Equalization in Large-Dimension MIMO-ISI Channels Using Graphical Models

In this paper, we deal with low-complexity near-optimal detection/equalization in large-dimension multiple-input multiple-output inter-symbol interference (MIMO-ISI) channels using message passing on graphical models. A key contribution in the paper is the demonstration that near-optimal performance in MIMO-ISI channels with large dimensions can be achieved at low complexities through simple yet effective simplifications/approximations, although the graphical models that represent MIMO-ISI channels are fully/densely connected (loopy graphs). These include 1) use of Markov Random Field (MRF) based graphical model with pairwise interaction, in conjunction with {\em message/belief damping}, and 2) use of Factor Graph (FG) based graphical model with {\em Gaussian approximation of interference} (GAI). The per-symbol complexities are $O(K^2n_t^2)$ and $O(Kn_t)$ for the MRF and the FG with GAI approaches, respectively, where $K$ and $n_t$ denote the number of channel uses per frame, and number of transmit antennas, respectively. These low-complexities are quite attractive for large dimensions, i.e., for large $Kn_t$. From a performance perspective, these algorithms are even more interesting in large-dimensions since they achieve increasingly closer to optimum detection performance for increasing $Kn_t$. Also, we show that these message passing algorithms can be used in an iterative manner with local neighborhood search algorithms to improve the reliability/performance of $M$-QAM symbol detection

저복잡도 후보 OFDM 신호 생성을 이용한 새로운 PTS 방법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 노종선.This dissertation proposes several research results on the peak-to-average power ratio (PAPR) reduction schemes for the orthogonal frequency division multiplexing (OFDM) systems. The PAPR is the one of major drawback of OFDM system which causes signal distortion when OFDM signal passes through nonlinear high power amplifier (HPA). Various schemes have been proposed to reduce the PAPR of OFDM signals such as clipping, selected mapping (SLM), partial transmit sequence (PTS), active constellation extension (ACE), companding, and tone reservation (TR). Among them, PTS scheme can transmit an OFDM signal vector by generating many alternative OFDM signal vectors using the partitioned subblock signals and selecting the optimal OFDM signal vector with the minimum PAPR. However, the PTS scheme requires large computational complexity, because it needs many inverse fast Fourier transforms (IFFTs) of subblock signals and lots of alternative OFDM signal vectors are generated. In this dissertation, we concentrate on reducing the computational complexity of the PTS scheme. In the first part of this dissertation, we propose a new PTS scheme with low computational complexity using two search steps to find a subset of phase rotating vectors showing good PAPR reduction performance. In the first step, sequences with low correlation are used as phase rotating vectors for PTS scheme, which are called the initial phase vectors. Kasami sequence and quaternary sequence are used in this step as the initial phase vectors. In the second step, local search is performed based on the initial phase vectors to find additional phase rotating vectors which show good PAPR reduction performance. Numerical analysis shows that the proposed PTS scheme can achieve almost the same PAPR reduction performance as the conventional PTS scheme with much lower computational complexity than other low-complexity PTS schemes. In the second part of the dissertation, we propose another low-complexity PTS schemes using the dominant time-domain OFDM signal samples, which are only used to calculate PAPR of each alternative OFDM signal vector. In this PTS scheme, we propose efficient metrics to select the dominant time-domain samples. For further lowering the computational complexity, dominant time-domain samples are sorted in decreasing order by the proposed metric values and then the power of each sample is compared with the minimum PAPR of the previously examined alternative OFDM signal vectors. Numerical results confirm that the proposed PTS schemes using new metrics show large computational complexity reduction compared to other existing low-complexity PTS schemes without PAPR degradation. In the last part of the dissertation, for the reduced-complexity PTS scheme, a new selection method of the dominant time-domain samples is proposed by rotating the IFFTed signal samples to the area on which the IFFTed signal sample of the first subblock is located in the signal space. Moreover, the method of pre-exclusion of the phase rotating vectors using the time-domain sample rotation is proposed to reduce the number of alternative OFDM signal vectors. Further, three proposed PTS schemes are introduced to reduce the computational complexity by using simple OFDM signal rotation and pre-exclusion of the phase rotating vectors. Numerical analysis shows that the proposed PTS schemes achieve the same PAPR reduction performance as that of the conventional PTS scheme with the large computational complexity reduction.Docto

Machine-learning assisted optimisation of free-parameters of a dual-input power amplifier for wideband applications

This paper presents an auto-tuning approach for dual-input power amplifiers using a combination of global optimisation search algorithms and adaptive linearisation in the optimisation of a multiple-input power amplifier. The objective is to exploit the extra degrees of freedom provided by dual-input topologies to enhance the power efficiency figures along wide signal bandwidths and high peak-to-average power ratio values, while being compliant with the linearity requirements. By using heuristic search global optimisation algorithms, such as the simulated annealing or the adaptive Lipschitz Optimisation, it is possible to find the best parameter configuration for PA biasing, signal calibration, and digital predistortion linearisation to help mitigating the inherent trade-off between linearity and power efficiency. Experimental results using a load-modulated balanced amplifier as device-under-test showed that after properly tuning the selected free-parameters it was possible to maximise the power efficiency when considering long-term evolution signals with different bandwidths. For example, a carrier aggregated a long-term evolution signal with up to 200 MHz instantaneous bandwidth and a peak-to-average power ratio greater than 10 dB, and was amplified with a mean output power around 33 dBm and 22.2% of mean power efficiency while meeting the in-band (error vector magnitude lower than 1%) and out-of-band (adjacent channel leakage ratio lower than −45 dBc) linearity requirements