Adaptive Modulation Schemes for Underwater Acoustic OFDM Communication

High data rate communication is challenging in underwater acoustic (UA) communication as UA channels vary fast along with the environmental factors. A real-time Orthogonal frequency-division multiplexing (OFDM) based adaptive UA communication system is studied in this research employing the National Instruments (NI) LabVIEW software and NI CompactDAQ device. The developed adaptive modulation schemes enhance the reliability of communication, guarantee continuous connectivity, ensure maximum performance under a fixed BER at all times and boost data rate

Machine Learning Aided Methods for Resilient Industrial Wireless Sensor Network

Le Wireless Sensor Network (WSN) possono essere definite come un’ infrastruttura composta da sensori/dispositivi in grado di calcolare comunicare e effettuare sensing sul ambiente che gli circonda processando e analizzando i dati in modo da reagire a eventi e fenomeni che possono occorrere durante la comunicazione. Questo motiva un enorme effort nella ricerca, standardizzazione e investimento industriale in questo campo, nell’ultimo decennio. L’uso delle WSN nell’ambiente industriale è soggetto a diverse problematiche, dovuto all’ostilità dell’ambiente, come rumore, shadwoing, multi-percorso e interferenze. Nel nostro progetto, proponiamo un meccanismo basato sulle condizioni di propagazione del canale e algoritmi di machine learning che ci permettono di classificare lo stato del canale (LOS o NLOS) e migliorare qualità, sicurezza e in particolar modo l’affidabilità del sistema radio link da noi esaminato in differenti ambienti

GNSS-SDR pseudorange quality and single point positioning performance assessment

In recent years, we have witnessed a growing demand for GNSS receiver customization in terms of modification of signal acquisition, tracking, and processing strategies. Such demands may be addressed by software-defined receivers (SDRs) which refers to an ensemble of hardware and software technologies and allows re-configurable radio communication architectures. The crux of the SDRs is the replacement of the hardware components through software modules. In this paper, we assess the quality of GNSS observables acquired by SDR against the selected u-blox low-cost receiver. In the following, we investigate the performance level of single point positioning that may be reached with an ultra-low-cost SDR and compare it to that of the low-cost GNSS receiver. The signal quality assessment revealed a comparable performance in terms of carrier-to-noise density ratio and a significant out-performance of the u-blox over SDR in terms of code pseudorange noise. The experimentation in the positioning domain proved that software-defined receivers may offer a position solution with three-dimensional standard deviation error at the level of 5.2 m in a single point positioning mode that is noticeably poorer accuracy as compared to the low-cost receiver. Such results demonstrate that there is still room for SDR positioning accuracy improvement

Implementation of a Radio Frequency Fingerprint Detector Based on GNSS Signals

Geolocation is one of the most significant manifestations of the current development of information technologies and it is used for multiple applications, such as mobile networks, military systems, or in the stock market. For that reason, it is important to verify the source of this type of signals, as they could be susceptible to being tricked by spoofing attacks, namely fake transmitters. This thesis is based on the development of a GNSS signal type classifier based on radio frequency (RF) fingerprinting methods that will determine if a signal belongs to an authorized transmitter or if it comes from a non-authorized GNSS signal generator/repeater. First, a total of 620 signals have been recorded in lab environments, follows: 40 different scenarios of real GNSS signal (with antennas located on the roof of the university) and 580 scenarios of the generated signal (using a GNSS signal generator). Each of the scenarios contains different types of signals (different GNSS constellations and/or bands, different satellites, etc.). Then, using a MATLAB-based simulator, the recorded signal is read, a certain time-frequency transform is applied (in this case the discrete Wavelet Transform), and an image of the wavelet transform of each sample is saved. These images include the features of the signal's RF fingerprinting. Next, a machine learning algorithm called SVM, also designed in MATLAB, is used. This algorithm classifies two or more different signal classes, and finally evaluate the classification accuracy. We used 80% of the images in each category for training and the remaining 20% for testing. Finally, a confusion matrix is obtained showing the accuracy obtained by the SVM algorithm in the testing phase. The analysis of the results has shown that the SVM classification algorithm can be a very effective model for the identification of GNSS transmitters through the use of fingerprinting features. It has been observed that when the Spectracom scenario is configured with more than one satellite, accuracy is lower compared to being configured with only one. This is because the signal obtained when more than one satellite is configured is more similar to the signal obtained from the antenna in comparison to the single satellite configuration, and for that reason, SVM has more difficulty in classifying it correctly. Another observation is that accuracy is also reduced when more than two categories are classified at the same time compared to a binary classification. Despite this, the accuracy is very high in the scenarios used, with 99.47% being the lowest value obtained and 100% the highest. Therefore, this implementation of RF fingerprinting methods is very promising in the context of determining whether a signal belongs to the actual GNSS satellite constellation or to a signal generator with a high level of accuracy

Antenna Selection And MIMO Capacity Estimation For Vehicular Communication Systems

Vehicular communication is one of the promising prospects of wireless communication capable of addressing the issues related to road safety, providing the framework for smart or intelligent cars. To provide a reliable wireless link for vehicular communication extensive channel modeling and measurements are required. In this thesis a novel cost-effective implementation of vehicular channel capacity measuring system using off-the-shelf devices is proposed. Then using the proposed system, various channel measurements are performed. The measurement results are utilized to examine multi-antenna systems for vehicular communication. The challenge in developing an efficient network between cars is to understand the nature of random channels that changes with the location of antenna, surroundings and obstacles between the transmitting and receiving vehicles. In addition to measurements, in this thesis, the channel behavior has been studied through simulation. Wireless InSite from Remcom was used as a simulation tool to study different vehicular channels in environments with different structures to see the impact of obstacles and surroundings in the performance of the vehicular network. In particular, the behavior of different antenna locations on channel capacity of 2Ã2 Multiple Input Multiple Output (MIMO) systems is investigated. Channel capacities that are obtained from simulation and measurements provide the information about the changing nature of the channel and outline the essential considerations while choosing the antenna positions on the transmitting or receiving vehicles

Waveform Advancements and Synchronization Techniques for Generalized Frequency Division Multiplexing

To enable a new level of connectivity among machines as well as between people and machines, future wireless applications will demand higher requirements on data rates, response time, and reliability from the communication system. This will lead to a different system design, comprising a wide range of deployment scenarios. One important aspect is the evolution of physical layer (PHY), specifically the waveform modulation. The novel generalized frequency division multiplexing (GFDM) technique is a prominent proposal for a flexible block filtered multicarrier modulation. This thesis introduces an advanced GFDM concept that enables the emulation of other prominent waveform candidates in scenarios where they perform best. Hence, a unique modulation framework is presented that is capable of addressing a wide range of scenarios and to upgrade the PHY for 5G networks. In particular, for a subset of system parameters of the modulation framework, the problem of symbol time offset (STO) and carrier frequency offset (CFO) estimation is investigated and synchronization approaches, which can operate in burst and continuous transmissions, are designed. The first part of this work presents the modulation principles of prominent 5G candidate waveforms and then focuses on the GFDM basic and advanced attributes. The GFDM concept is extended towards the use of OQAM, introducing the novel frequency-shift OQAM-GFDM, and a new low complexity model based on signal processing carried out in the time domain. A new prototype filter proposal highlights the benefits obtained in terms of a reduced out-of-band (OOB) radiation and more attractive hardware implementation cost. With proper parameterization of the advanced GFDM, the achieved gains are applicable to other filtered OFDM waveforms. In the second part, a search approach for estimating STO and CFO in GFDM is evaluated. A self-interference metric is proposed to quantify the effective SNR penalty caused by the residual time and frequency misalignment or intrinsic inter-symbol interference (ISI) and inter-carrier interference (ICI) for arbitrary pulse shape design in GFDM. In particular, the ICI can be used as a non-data aided approach for frequency estimation. Then, GFDM training sequences, defined either as an isolated preamble or embedded as a midamble or pseudo-circular pre/post-amble, are designed. Simulations show better OOB emission and good estimation results, either comparable or superior, to state-of-the-art OFDM system in wireless channels

Interference diagnosis in wireless systems by using NI USRP

In both industrial and domestic environments, there will be a large increase of wireless communication systems. The density of wireless devices and need of additional bandwidth is expanding which is increasing the wireless interference. Interference emerges from unintended radiators is the main cause of degrading the Electromagnetic Compatibility and wireless communication performance in near field region and far field region and decrease the robustness and reliability. This paper presents methods of detection and removal of interference to improve EMC performance and wireless communication. We used NI USRP equipment for the diagnosis and provided the solutions to remove interference. The presented methods of interference diagnosis can efficiently optimize the performance of any wireless network