OF DOCTORAL DISSERTATION HELSINKI UNIVERSITY OF TECHNOLOGY P.O. BOX 1000, FI-02015 TKK http://www.tkk.fi Author Name of the dissertation Manuscript submitted Manuscript revised Date of the defence Monograph Article dissertation (summary + original articles) Faculty Department Field of research Opponent(s) Supervisor Instructor Abstract

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Digital predistortion linearization methods for RF power amplifiers

Digital predistortion (DPD) techniques are widely used to linearize of RF power amplifiers. In this article, a memory polynomial-based power has been modeled with memory order of 5 and nonlinearity order of 9. These specifications had been found suitable for advanced long-term evolution (LTE) wireless systems. A suitable DPD model was created based on the least square error minimization. Results for the complementary cumulative distribution function and normalized mean squared error were also computed. The adjacent channel leakage power ratio and error vector magnitude calculations showed that the proposed DPD for LTE system performed within the required limits as desired.

https://link.springer.com/content/pdf/10.1186%2F1687-1499-2012-330.pdf

Digital predistortion of power amplifiers using look-up table method with memory effects for LTE wireless systems

Tese de doutoramento em Engenharia Electrotecnica e de Computadores (Telecomunicacoes e Electronica), apresentada a Faculdade de Ciencas e Tecnologia da Universidade de Coimbra

/pdf/magnitude-modulation-for-peak-power-control-in-single-32rwugujta.pdf

Magnitude modulation for peak power control in single carrier communication systems

Reduction of Peak to Average Power Ratio (PAPR) in Orthogonal Frequency Division Multiplexing (OFDM) is investigated here. Clipping can be performed in two ways– one is to clip the complex envelopes of OFDM signal and the other is to clip the Inphase (I-signal) and Quadrature phase (Q-signal) independently. The detailed investigation concerning the PAPR reduction and Complementary Cumulative Distribution Function (CCDF) of both the techniques is made. A novel approach based on clipping and amplification is introduced as a mean for reducing PAPR in OFDM signal. Our Proposed scheme combines the effect of clipping the peak signals and amplifying the signals that have low amplitude of OFDM signal, hence the name Clipping and Amplification method. The method is then simulated and detailed comparisons in terms of PAPR reduction with other methods are made. Comparison among different M-array modulation techniques is made to draw the conclusion for the best suited modulation technique for OFDM in terms of PAPR reduction. Finally, relations among PAPR, threshold of clipping, threshold of amplification and CCDF are established. Results show that our proposed approach gives better performance than clipping method at the expense of increased CCDF.

Reduction of Peak to Average Power Ratio (PAPR) in Orthogonal Frequency Division Multiplexing (OFDM): A Novel Approach Based on Clipping and Amplification

In this paper we consider broadband wireless systems with high power efficiency. For this purpose we employ MM (Magnitude Modulation) techniques to reduce the PAPR (Peak-to-Average Power Ratio) of the transmitted signals, allowing efficient power amplification. Designed transceivers combine MM with SC-FDE (Single-Carrier with Frequency-Domain Equalization) methods upon iterative-block decision feedback equalization (IB-DFE). Since IB-DFE schemes designed for conventional SC signals are not suitable when we employ PAPR-reduction techniques, the effect of MM is taken into account on the estimation reliability factor that drives IB-DFE convergence. Results for the new proposed MM- IBDFE transceiver present gains above 1.5dB in the overall power efficiency, even when considering severely time-dispersive channels.

Iterative Frequency Domain Equalization for Single Carrier Signals with Magnitude Modulation Techniques

Many of the modulation methods currently in use suffer from a high Peak-to-Average power Ratio (PAR), also known as the Crest Factor (CF). The Global System for Mobile communication (GSM) is a widespread second-generation (2G) system that uses constant envelope Gaussian minimum shift keying modulation. The advantage achieved by constant envelope modulation is the possibility of using power-efficient power amplifiers (PAs). However, it might be beneficial to combine the carriers in a digital intermediate frequency in order to reduce the number of analogue components. The drawback with this is that the signal is no more a constant envelope signal, but it has a strongly fluctuating envelope with a high CF. Enhanced Data rates for GSM Evolution (EDGE) is an enhancement to the GSM system with the primary objective of tripling the on-air data rate while meeting essentially the same bandwidth occupancy of the original GSM signal. Also in the case of EDGE, if the carriers are combined prior to amplification we would end up with the same high CF problem. Wideband Code Division Multiple Access (WCDMA) has been selected by the European Telecommunications Standards Institute for wideband wireless access to support third-generation (3G) services. Orthogonal Frequency Division Multiplexing (OFDM) is commonly considered to be a technical solution for fourth-generation (4G) services. In both cases, the transmitted signal is generated by adding together a large number of statistically independent signals, which leads to a signal with a high CF. The high CF sets strict requirements for the linearity of the PA. In order to limit the adjacent channel leakage, it is desirable for the PA to operate in its linear region. High linearity requirements for the PA leads to low power efficiency and therefore to high power consumption. An alternative to the expense of a wide-dynamic-range PA is the use of deliberate clipping to digitally distort the signal so that the signal quality is still maintained at a sufficient level. As an extra advantage, the decreased CF gives rise to the possibility of utilizing the dynamic range of the digital circuitry and digital-to-analog converter efficiently. This thesis discusses digital modulator design, concentrating on CF reduction algorithms. Two modulators, one capable of generating GSM, EDGE and WCDMA sig-

/pdf/digital-modulators-with-crest-factor-reduction-techniques-3m4ccx6quv.pdf

Digital modulators with crest factor reduction techniques


Purpose
Minimizing the energy consumption in a wireless sensor node is important for lengthening the lifetime of a battery. Radio transmission is the most energy-consuming task in a wireless sensor node, and by compressing the sensor data in the online mode, it is possible to reduce the number of transmission periods. This study aims to demonstrate that temporal compression methods present an effective method for lengthening the lifetime of a battery-powered wireless sensor node.


Design/methodology/approach
In this study, the energy consumption of LoRa-based sensor node was evaluated and measured. The experiments were conducted with different LoRaWAN data rate parameters, with and without compression algorithms implemented to compress sensor data in the online mode. The effect of temporal compression algorithms on the overall energy consumption was measured.


Findings
Energy consumption was measured with different LoRaWAN spreading factors. The LoRaWAN transmission energy consumption significantly depends on the spreading factor used. The other significant factors affecting the LoRa-based sensor node energy consumption are the measurement interval and sleep mode current consumption. The results show that temporal compression algorithms are an effective method for reducing the energy consumption of a LoRa sensor node by reducing the number of LoRa transmission periods.


Originality/value
This paper presents with a practical case that it is possible to reduce the overall energy consumption of a wireless sensor node by compressing sensor data in online mode with simple temporal compression algorithms.


Efficiency of temporal sensor data compression methods to reduce LoRa-based sensor node energy consumption

Environmental monitoring is a typical Internet of Things (IoT) application. Environmental monitoring plays a significant role, for example, in smart farming and smart city applications. Environmental magnitudes are usually measured using wireless sensor nodes, which are often battery-powered, and the number of sensing nodes can be large. One effective method for reducing the energy consumption of a sensor node is to use data compression to reduce the amount of data required for transmission via a wireless connection. Compressing the sensor data means fewer transmission periods, and thus, lower energy consumption. Compression methods should be effective for compressing environmental magnitudes and be computationally light to be suitable for constrained sensor nodes. A compression algorithm should be able to compress an online data stream. In this paper, we review some compression algorithms suitable for environmental monitoring and present two new versions of those algorithms. The algorithms were evaluated, tested, and compared. The main parameters used for the comparisons were compression ratio, root mean square error, and inherent latency. The simulation results obtained using real datasets demonstrate that simple linearity-based compression algorithms are effective and suitable for compressing environmental data. Two new compression algorithm versions proved to be effective for compressing sensor data with reasonable compression quality and predictable inherent latency.

Linearity-based Sensor Data Online Compression Methods for Environmental Applications

Artificial intelligence is more ubiquitous in multiple domains. Smartphones, social media platforms, search engines, and autonomous vehicles are just a few examples of applications that utilize artificial intelligence technologies to enhance their performance. This study carries out a scoping review of the current state-of-the-art artificial intelligence technologies following the PRISMA framework. The goal was to find the most advanced technologies used in different domains of artificial intelligence technology research. Three recognized journals were used from artificial intelligence and machine learning domain: Journal of Artificial Intelligence Research, Journal of Machine Learning Research, and Machine Learning, and articles published in 2022 were observed. Certain qualifications were laid for the technological solutions: the technology must be tested against comparable solutions, commonly approved or otherwise well justified datasets must be used while applying, and results must show improvements against comparable solutions. One of the most important parts of the technology development appeared to be how to process and exploit the data gathered from multiple sources. The data can be highly unstructured and the technological solution should be able to utilize the data with minimum manual work from humans. The results of this review indicate that creating labeled datasets is very laborious, and solutions exploiting unsupervised or semi-supervised learning technologies are more and more researched. The learning algorithms should be able to be updated efficiently, and predictions should be interpretable. Using artificial intelligence technologies in real-world applications, safety and explainable predictions are mandatory to consider before mass adoption can occur.

Latest Trends in Artificial Intelligence Technology: A Scoping Review

Reliable electric power distribution is crucial in modern society. Modern society is vulnerable to power outages because so many functionalities are using electric power. All the modern communication systems require electric power, also transportation is becoming electrified in near future. It is possible to achieve information from various devices and other sources related to electric power distribution network. One source of information are the protective relays that creates an event recording when the device identify deviation in the network. This event data is not always used effectively and mostly the analysis is done manually and not in real-time. In this study the event recordings are analyzed with deep neural network to find out if it is possible to identify the fault type from the recording automatically. Event recordings are only one source of information from the distribution network. There is a lot of different data available from the network and recently different Internet of Things devices have become available to be used monitoring the network. This other data could be used in combination with event recordings to give more accurate situation analysis of the network.

Olli Väänänen

Papers

Digital modulators with crest factor reduction techniques

Efficiency of temporal sensor data compression methods to reduce LoRa-based sensor node energy consumption

Linearity-based Sensor Data Online Compression Methods for Environmental Applications

Latest Trends in Artificial Intelligence Technology: A Scoping Review

Identifying electric power system fault types with Deep Neural Network