A technique for wall compensation in the ultra-wideband (UWB) through-wall imaging radar is presented. The UWB system can be utilize in high precision measurements, but due to phase distortion and amplitude attenuation caused by the wall the precision is limited, the target is displaced, and the image is defocused. 

In order to mitigate the wall effects, two methods are applied in this project. First, the unknown wall transfer function is estimated using real data measurements to design the inverse filter. Secondly, FIR Wiener filter is designed to improve the received m-sequence. After all, each method is tested using three parameters, the signal to noise ratio (SNR), the signal to clutter ratio (SCR), and the relative position error (RPE). 

The inverse filter can eliminate the wall effects very well; it could correct not only the position of the target but also the image defocus. The new method can give improve the image quality and that can extend the use of UWB radar in many applications. 

Wireless systems have become an inevitable part of modern technologies serving humankind. The rapid growth towards large dimensional systems, e.g. 5th generation (5G) technologies, incurs needs for improving the performance of the systems and considering aspects to make them as far as possible environmentally friendly in terms of power efficiency, cost, and so on. One of the key parts of every wireless communication system is the radio frequency (RF) power amplifier (PA), which consumes the largest percentage of the total energy. Hence, accurate models of RF PAs can be used to optimize their design and to compensate for signal distortions. This thesis starts with two methods for frequency-domain characterisation to analyse the dynamic behaviour of PAs in 3rd-order non-linear systems. Firstly, two-tone signals superimposed on large-signals are used to analyse the frequency-domain symmetry properties of inter-modulation (IM) distortions and Volterra kernels in different dynamic regions of RF PAs in a single-input single-output (SISO) system. Secondly, three-tone signals are used to characterise the 3rd-order self- and cross-Volterra kernels of RF PAs in a 3 × 3 multiple-input multiple-output (MIMO) system. The main block structures of the models are determined by analysing the frequency-domain symmetry properties of the Volterra kernels in different three-dimensional (3D) frequency spaces. This approach significantly simplifies the structure of the 3rd-order non-linear MIMO model.

The following parts of the thesis investigate techniques for behavioural modelling and linearising RF PAs. A piece-wise modelling technique is proposed to characterise the dynamic behaviour and to mitigate the impairments of non-linear RF PAs at different operating points (regions). A set of thresholds decompose the input signal into several sub-signals that drive the RF PAs at different operating points. At each operating point, the PAs are modelled by one sub-model, and hence, the complete model consists of several sub-models. The proposed technique reduces the model errors compared to conventional piece-wise modelling techniques.

A block structure modelling technique is proposed for RF PAs in a MIMO system based on the results of the three-tone characterisation technique. The main structures of the 3rd- and higher-order systems are formulated based on the frequency dependence of each block. Hence, the model can describe more relevant interconnections between the inputs and outputs than conventional polynomial-type models.

This thesis studies the behavioural modelling and compensation techniques in both the time and the frequency domains for RF PAs in a 3 × 3MIMO system. The 3D time-domain technique is an extension of conventional 2D generalised memory polynomial (GMP) techniques. To reduce the computational complexity, a frequency-domain technique is proposed that is efficient and feasible for systems with long memory effects. In this technique, the parameters of the model are estimated within narrow sub-bands. Each sub-band requires only a few parameters, and hence the size of the model for each sub-band is reduced.

Multiple-input multiple-output (MIMO) frequency-domain Volterra kernels of nonlinear order 3 are experimentally determined in bandwidth-limited frequency regions. How the effect of higher nonlinear orders can be reduced and how this affects the estimated errors are discussed. The magnitude and phase of the kernels areKramers-Kronig consistent. The self- and cross-kernels have different symmetries and the kernels are therefore determined and analyzed in different regions in the 3D frequency space. By analyzing the properties along certain paths in the 3D frequency space, the block structures for the respective kernels are determined. These block structures contain the significant blocks of the general block structures for third-order kernels. The device under test is a MIMO transmitter for radio frequency signals.

In this paper, a new approach is proposed to decompose the basis functions in a piece-wise modeling technique for nonlinear radio frequency (RF) power amplifiers. The proposed technique treats the discontinuity problem of the model output at the joint points between different operating points, whereas preserves the linear and nonlinear properties of the original model within each region. Experimental results show that the proposed technique outperforms the conventional piece-wise model in terms of model errors.

Modern telecommunications are moving towards (massive) multi-input multi-output systems in 5th generation (5G) technology, increasing the dimensionality of the system dramatically. In this paper, the impairments of radio frequency (RF)power amplifiers (PAs) in a 3x3 MIMO system are compensated in both time and frequency domains. A three-dimensional(3D) time-domain memory polynomial-type model is proposed as an extension of conventional 2D models. Furthermore, a 3D frequency-domain technique is formulated based on the proposed time-domain model to reduce the dimensionality of the model, while preserving the performance in terms of model errors. In the 3D frequency-domain technique, the bandwidth of a system is split into several narrow sub-bands, and the parameters of the system are estimated for each subband. This approach requires less computational complexity, and also the procedure of the parameters estimation for each sub-band can be implemented independently. The device-under-test (DUT) consists of three RF PAs including input and output cross-talk channels. The proposed techniques are evaluated in both behavioural modelling and digital pre-distortion(DPD) perspectives. The results show that the proposed DPD technique can compensate the errors of non-linearity and memory effects by about 23.5 dB and 7 dB in terms of the normalized mean square error and adjacent channel leakage ratio, respectively.

A new two-tone test method for radio frequency power amplifiers is presented. The test signal is a two-tone probing-signal superimposed on large-signals of different amplitude. The amplifier is, thus, excited in different amplitude regions. The amplitude and phase of the 3rd order intermodulation (IM) products are measured vs. frequency spacing and probing-signal amplitude in each region. The IM magnitude is a measure of the nonlinearity, while the frequency dependence and asymmetry are measures of the memory effects in the different regions. A Doherty and a class-AB amplifier were tested. For both amplifiers the IM magnitude increased by ∼15 dB from the lowest to the highest amplitude region. For the Doherty amplifier the behavior of the IM products vs. frequency spacing was similar in all regions, indicating similar memory effects. For the class-AB amplifier the IM vs. frequency spacing was significantly different in the different regions, which indicates different memory effects.

A new block-structure behavioral model is proposed for radio frequency power amplifiers in a 2x2 multiple-input multiple-output system including input cross-talk. The proposed model forms kernels of blocks of different nonlinear order that correspond to the significant frequency response of measured frequency domain Volterra kernels. The model can therefore well describe the input-output relationships of the nonlinear dynamic behavior of PAs. The proposed model outperforms conventional models in terms of model errors.

The World today is deeply transformed by the advancement in wireless technology. The envision of a smart society where interactions between physical and virtual dimensions of life are intertwined and where human interaction is mediated by machines, e.g., smart phones, demands increasingly more data traffic. This continual increase in data traffic requires re-designing of the wireless technologies for increased system capacity and flexibility. In this thesis, aspects related to behavioral modeling, characterization, and linearization of multi-channel/band power amplifiers (PAs) are discussed.

When building a model of any system, it is advantageous to take into account the knowledge of the physics of the system and include into the model. This approach could help to improve the model performance. In this context, three novel behavioral models and DPD schemes for nonlinear MIMO transmitters are proposed.

To model and compensate distortions in GaN based RF PAs in presence of long-term memory effects, novel models for SISO and concurrent dual-band PAs are proposed. These models are based on a fixed pole expansion technique and have infinite impulse response. They show substantial performance improvement. A behavioral model based on the physical knowledge of the concurrent dual-band PA is derived, and its performance is investigated both for behavioral modeling and compensation of nonlinear distortions.

Two-tone characterization is a fingerprint method for the characterization of memory effects in dynamic nonlinear systems. In this context, two novel techniques are proposed. The first technique is a dual two-tone characterization technique to characterize the memory effects of self- and cross-modulation products in concurrent dual-band transmitter. The second technique is for the characterization and analysis of self- and cross-Volterra kernels of nonlinear 3x3 MIMO systems using three-tone signals.

The demands for high data rates and broadband wireless access requirethe development of wireless systems that can support wide and multi-bandsignals. To deploy these signals, new radio frequency (RF) front-ends are requiredwhich impose new challenges in terms of power consumption efficiencyand sources of distortion e.g., nonlinearity. These challenges are more pronouncedin power amplifiers (PAs) that degrade the overall performance ofthe RF transmitter.Since it is difficult to optimize the linearity and efficiency characteristicsof a PA simultaneously, a trade-off is needed. At high input power, a PAexhibits high efficiency at the expense of linearity. On the other hand, atlow input power, a PA is linear at the expense of the efficiency. To achievelinearity and efficiency at the same time, digital pre-distortion (DPD) is oftenused to compensate for the PA nonlinearity at high input power. In case ofmulti-channel PAs, input and output signals of different channels interactwith each other due to cross-talk. Therefore, these PAs exhibit differentnonlinear behavior than the single-input single-output (SISO) PAs. The DPDtechniques developed for SISO PAs do not result in adequate performancewhen used for multi-channel PAs. Hence, an accurate behavioral modeling isessential for the development of DPD for multi-channel RF PAs.In this thesis, we propose three novel behavioral models and DPD schemesfor nonlinear multiple-input multiple-output (MIMO) transmitters in presenceof cross-talk. A study of the source of cross-talk in MIMO transmittershave been investigated to derive simple and powerful modeling schemes.These models are extensions of a SISO generalized memory polynomial model.A comparative study with a previously published MIMO model is also presented.The effect of coherent and partially non-coherent signal generationon DPD performance is also highlighted. It is shown experimentally thatwith partially non-coherent signal generation, the performance of the DPDdegrades compared to coherent signal generation.In context of multi-channel RF transmitters, PA behavioral models andDPD schemes suffer from a large number of model parameters with the increasein nonlinear order and memory depth. This growth leads to highcomplexity model identification and implementation. We have designed aDPD scheme for MIMO PAs using a sparse estimation technique for reducingmodel complexity. This technique also increases the numerical stability whenlinear least square estimation model identification is used.A method to characterize the memory effects in a nonlinear concurrentdual-band PAs is also presented. Compared to the SISO PAs, concurrentdual-band PAs are not only affected by intermodulation distortions but alsoby cross-modulation distortions. The characterization of memory effects inconcurrent dual-band transmitter is performed by injecting a two-tone testsignal in each input channel of the transmitter. Asymmetric energy surfacesare introduced for the intermodulation and cross-modulation products, whichcan be used to identify the power and frequency regions where the memory effects are dominant.

A characterization technique for RF PAs excited with multiple signals is presented. The technique can be used for characterization of memory effects in IM/CM products. The extracted information can in return be of use for modifying behavioral models to better capture memory in IM/CM products.

Electrical anomalies due to trapping effects in Gallium Nitride (GaN) power amplifiers (PAs)give rise to long-term or strong memory effects. We propose novel models based on infinite impulse response (IIR) fixed pole expansion techniques for the behavioural modeling and digital pre-distortion of single-input-single-output (SISO) and concurrent dual-bandGaN PAs. Experimental results show that the proposed models outperform the corresponding finite impulse response (FIR) models by up to 17 dB for the same number of model parameters. For the linearization of a SISO GaN PA the proposed models give adjacent channel power ratios (ACPRs) that are 7 to 17 dBlower than the FIR models. For the concurrent dual-band case, the proposed models give ACPRs that are 9to 14 dB lower than the FIR models.

A dual two-tone technique for the characterization of memory effects in concurrent dual-band transmitters is revisited to modify a 2D-DPD model for the linearization of concurrent dual-band transmitters. By taking into account the individual nonlinear memory effects of the self- and cross-kernels, a new 2D modified digital pre-distortion (2D-MDPD) model is proposed, which not only supersedes the linearization performance but also reduces the computational complexity compared to the 2D-DP Dmodel in terms of a number of floating point operations (FLOPs). Experimental results show an improvement of 1.7 dBin normalized mean square error (NMSE) and a 58% reduction in the number of FLOPs.

This paper proposes three novel models for behavioral modeling and digital pre-distortion (DPD) of nonlinear 2$,times,$ 2 multiple-input multiple-output (MIMO) transmitters in the presence of crosstalk. The proposed models are extensions of the single-input single-output generalized memory polynomial model. Three types of crosstalk effects were studied and characterized as linear, nonlinear, and nonlinear & linear crosstalk. A comparative study was performed with previously published models for the linearization of crosstalk in a nonlinear 2 $,times,$2 MIMO transmitter. The experiments indicate that, depending on the type of crosstalk, the selection of the correct model in the transmitter is necessary for behavioral modeling and sufficient DPD performance. The effects of coherent and partially noncoherent signal generation on the performance of DPD were also studied. For crosstalk levels of ${-}{hbox{30}}$ dB, the difference in the normalized mean square error and adjacent channel power ratio was found to be 3–4 dB between coherent and partially noncoherent signal generation.

The paper presents a 2D extended envelope memory polynomial (2D-EEMP) model for concurrent dual-band radio frequency (RF) power amplifiers (PAs). The model is derived based on the physical knowledge of a dual-band RF PA. The derived model contains cross-modulation terms not included in previously published models; these terms are found to be of importance for both behavioral modeling and digital pre-distortion (DPD). The performance of the derived model is evaluated both as the behavioral model and DPD, and the performance is compared with state-of-the-art2D-DPD and dual-band generalized memory polynomial (DB-GMP) models. Experimental result shows that the proposed model resulted in normalized mean square error (NMSE) of -51.7/-51.6dB and adjacent channel error power ratio (ACEPR) of -63.1/-63.4 dB, for channel 1/2, whereas the 2D-DPD resulted in the largest model error and DB-GMP resulted in model parameters that are 3 times more than those resulted with the proposed model with the same performance. As pre-distorter, the proposed model resulted in adjacent channel power ratio (ACPR) of -55.8/ -54.6 dB for channel 1/2 and is 7-10 dB lower than those resulted with the 2D-DPD model and2-4 dB lower compared to the DB-GMP model.

A method to characterize the memory effects in a nonlinear concurrent dual-band transmitter is presented. It is an extension of the conventional two tone test for power amplifiers to concurrent dual band transmitters. The output signal of a concurrent dual-band transmitter is affected not only by intermodulation products but also by cross-modulation products. In one frequency band, the transmitter is excited by a two tone signal which frequency separation is swept. In the second band the transmitter is concurrently excited by an other two tone signal with slightly wider frequency separation. The frequency difference of the two signals is fixed during the frequency sweep. The two tone test is made at different power levels. The upper and lower third-order inter- and cross-modulation products are measured. The asymmetry between the upper and lower third-order inter- and cross-modulation products are measures of the transmitter's memory effects. The measurement results show that the memory effects are more dominant in the third-order intermodulation products than in the cross modulation products. An error analysis and system calibration was performed and measurement results for two different devices are presented.

The indirect learning architecture (ILA) is the mostused methodology for the identification of Digital Pre-distorter (DPD) functions for nonlinear systems, particularly for high power amplifiers. The ILA principle works in black box modeling relying on the inversion of input and output signals of the nonlinear system, such that the inverse is estimated. This paper presents the impact of disturbances, such as noise in the DPD identification. Experiments were performed with a state-of-art Doherty power amplifier intended for base station operation in current telecommunication wireless networks. As expected, a degradation in the performance of the DPD (measured innormalized mean square error (NMSE)) is found in our experiments. However, adjacent channel power ratio (ACPR) can be a misleading figure of merit showing improvement in the performance for wrongly estimated DPD functions.

The spectrum analyzer is a standard tool used to measure signals in the frequency domain. Traditional spectrum analyzers are based on sweeping a local oscillator and using this to mix signals down to an intermediate frequency (IF) and, subsequently filter them with a filter of settable characteristics, called the Resolution Bandwidth (RBW). This is still the preferred method when the requirement on dynamic range of the signals being measured is large. However, this approach has the drawbacks of being relatively slow, not adaptive and flexible for some specific need and certain special measurement functionalities cannot be done due to the sweeping. Due to this, Ericsson production test development would like to perform software-based spectrum analysis on sampled In-phase and Quadrature (IQ) signals.

In this thesis, the introduction of IQ-signals and synthetic spectrum analysis (SSA) are presented. The statistical properties of root mean-square (RMS) and sample detectors for standard spectrum analyzer are investigated. The effect of swept time on statistical properties of the RMS and sample detectors were investigated and the results are presented in this work. The results of swept time effect for sample detector show the change in the variance of the statistical properties when continuous wave (CW) and two-tone test signals were used, however, for bandlimited Gaussian test signal, the variance of the statistical properties is not changed. For RMS detector, the swept time using two-tone and Gaussian test signals show the change in the variance of the statistical properties. Whereas, for CW test signal the statistical properties result in shift from higher power distribution level to lower power distribution level with increase in sweep-time.

The emulation of spectrum analysis functionalities (RBW, envelope detector and 0detectors) for IQ-signal has been implemented in MATLAB. The verification of the implemented functionalities has been done by investigating the statistical properties of RMS and sample detectors for SSA for various test signals. These were found to agree with standard spectrum analyzer results.

Moreover, the comparison of spectral traces and statistical properties between implemented functionality and standard spectrum analyzer have done. The results are showing agreement with industrial standard spectrum analyzer results.

Through leakage in the building envelope there is a penetration of air, water vapor and particles. The degree of leakage of air can be quantified by existing methods. However, the location of adventitious openings is often not known. In order to overcome the limitations in existing methods, a non-contact and non-destructive method based on ultra-wide bandwidth radar technology is suggested. A test-bed is designed that can measure with different polarization to be able to detect flaws in different directions. Initial measurements shows promising results for further development of the method of using radar images to find leaks in building elements.

Satellite image processing of an ecosystem allows us to understand it and know, prevent, and investigate the events that take place in the environment. For this we need cheap and simple image reception systems, which include antennas, receivers and hardware/software for signal processing. In this work a turnstile antenna, a quadrifilar helical antenna and a V-dipole antenna were designed, constructed and used to obtain automatic NOAA image transmission signal and convert to a NOAA satellite image.

Remote sensing observations of Essential Climate Variables (ECVs) provide a means of studying their global and regional impacts. Coastal GNSS stations measure water levels using GNSS Reflectometry (GNSS-R) technique by determining the vertical distance between the antenna and the water surface. In this study, GNSS-R data from four stations over three months were used to estimate sea surface heights (SSH) and assess accuracy using nearest tide gauge observations. Results showed that GNSS signals from GPS, GLONASS, Galileo, and BeiDou were accurate for the SSH estimation. In addition, 145 significant tidal frequencies were extracted using the GNSS-R and tide gauge time series by employing the Least Square Harmonic Estimation (LS-HE) approach. The study demonstrates the usefulness of GNSS-R for tide studies and its potential use alongside tide gauge measurements in coastal locations.

Determination of the Earth’s gravity field and geopotential value is one of the fundamental topics in physical geodesy. Traditional terrestrial gravity and precise leveling measurements can be used to determine the geopotential values at a local or regional scale. However, recent developments in optical atomic clocks have not only rapidly improved fundamental science but also contributed to applied research. The latest generation of optical clocks is approaching the accuracy level of 10−18 when facilitating atomic clock networks. These systems allow examining fundamental theories and many research applications, such as atomic clocks applications in relativistic geodesy, to precisely determine the Earth’s gravity field parameters (e.g., geopotential values). According to the theory of relativistic geodesy, the frequency difference measured by an optical clock network is related to the gravity potential anomaly, provided that the effects of disturbing signals (i.e., tidal and non-tidal contributions) are filtered out. The relativistic geodesy principle could be used for a practical realization of global geodetic infrastructure, most importantly, a vertical datum unification or realization of height systems. This paper aims to review the background of relativistic (clock-based) geodesy and study the variations of optical atomic clock measurements (e.g., due to hydrology loading and land motion).

The general approach for modeling systems assumes that the measured signals are (weakly) stationary, i.e., the power spectrum is time invariant. However, the stationarity assumption is violated when: 1) transient effects due to experimental conditions are dominant; 2) data are missing due to, for instance, sensor failure; or 3) the amplitude of the excitation signals smoothly varies over time due to, for instance, actuator problems. Although different methods exist to deal with each of these nonstationary effects specifically, no unified approach is available. In this paper, a new and general technique is presented to handle nonstationary effects, based on processing overlapping subrecords of the measured data. The proposed method is a simple preprocessing step where the user does not need to specify which nonstationary effect is present, nor the time interval where the nonstationary effect appears. The merits of the proposed approach are demonstrated on an operational wireless system suffering from interrupted link effects.

The postprocessing of functional magnetic resonance imaging (fMRI) data to study the brain functions deals mainly with two objectives: signal detection and extraction of the haemodynamic response. Signal detection consists of exploring and detecting those areas of the brain that are triggered due to an external stimulus. Extraction of the haemodynamic response deals with describing and measuring the physiological process of activated regions in the brain due to stimulus. The haemodynamic response represents the change in oxygen levels since the brain functions require more glucose and oxygen upon stimulus that implies a change in blood flow. In the literature, different approaches to estimate and model the haemodynamic response have been proposed. These approaches can be discriminated in model structures that either provide a proper representation of the obtained measurements but provide no or a limited amount of physiological information, or provide physiological insight but lacks a proper fit to the data. In this paper, a novel model structure is studied for describing the haemodynamics in fMRI measurements: fractional models. We show that these models are flexible enough to describe the gathered data with the additional merit of providing physiological information.

In digital signal processing field, for recovering the signal without distortion, Shannon sampling theory must be fulfilled in the traditional signal sampling. However, in some practical applications, it is becoming an obstacle because of the dramatic increase of the costs due to increased volume of the storage and transmission as a function of frequency for sampling. Therefore, how to reduce the number of the sampling in analog to digital conversion (ADC) for wideband and how to compress the large data effectively has been becoming major subject for study. Recently, a novel technique, so-called “compressed sampling”, abbreviated as CS, has been proposed to solve the problem. This method will capture and represent compressible signals at a sampling rate significantly lower than the Nyquist rate.

This paper not only surveys the theory of compressed sampling, but also simulates the CS with the software Matlab. The error between the recovered signal and original signal for simulation is around -200dB. The attempts were made to apply CS. The error between the recovered signal and original one for experiment is around -40 dB which means the CS is realized in a certain extent. Furthermore, some related applications and the suggestions of the further work are discussed.

This chapter describes the conversion process and its limitations and discusses methods to reduce the impact of these limitations in various applications. The process of converting analog signals into digital signals can be divided into two parts. First, the signal will be measured at discrete time instants and then quantized into fixed amplitude levels. The key parameters to consider when selecting an analog-to-digital converter (ADC) or digital-to-analog converter (DAC) are the resolution of the quantizer in number of bits and the sampling rate of the sampler. The chapter discusses these two functions with a basic example. It introduces some commonly used sampling techniques for improving the performance of an existing ADC, such as oversampling, sigma-delta conversion, dithering, time-interleaved sampling, undersampling, harmonic sampling, time-equivalent sampling and post-correction methods. The theory behind signal reconstruction is discussed with respect to the sampling theorem.

This paper deals with the hardware and software architecture of a signal detector prototype. It is able to observe a wide band and to find, in such band, the empty or occupied channels. It is intended to be used either in telecommunication devices or in spectrum monitoring instruments working in the new scenarios of telecommunications, where a flexible and dynamic spectrum management will be introduced. The signal detector has been validated on simulated and emulated signals showing high detection percentages even with low signal to noise ratios.

In this paper we evaluate the generation of a multi-tone set for characterizing the behavior of nonlinear radio frequency (RF) modules in its out-of-band when harmonic sampling is used as digitizer. The purpose is to provide the reader with a tool to select proper frequencies and record length for a given application and test-bed. The method is based on simulations and the use of Sidon sequences. The proposed method is applicable to sparse discrete frequency multi-tones.

A compact multi band slotted two elements MIMO antenna is designed on FR4 substrate with the dimensions 60×60×1.5 mm3 and an εr of 4.4. To achieve high isolation between the radiation elements, a Defected Ground Surface (DGS) technique is applied. The isolation between the elements and gain both increased considerably. In the simulated and measured return loss vs frequency results, multi bands are obtained. The maximum isolation is obtained as -36.32 dB at 8.8 GHz in the measured results.

This thesis addresses the design study, implementation and analysis of signal processing algorithms for a 79 GHz millimeter-wave Phase Modulated Continuous Wave (PMCW) Multi Input Multi Output (MIMO) short range radar; performed in IMEC research institute (Leuven, Belgium). The radar system targets high resolution performance with low power consumption in order to integrate a full MIMO radar transceiver with digital processor and antennas in a compact package featuring a size of 1 cm². Achieving such radar system characteristics requires the exploitation of a highly demanding digital architecture with signal processing gain and high range, speed and angle resolutions. The improved resolution and detection capabilities will be achieved by performing signal processing algorithms on the reflected waveform. The digital front-end implements parallel range gate processing with a bank of correlators that perform: pulse compression, coherent accumulation to further increase Signal to Noise Ratio (SNR) and N-point FFT to extract the Doppler information. The use of MIMO is proposed implementing a code domain technique in the PMCW waveform, the Outer Hadamard Code MIMO. This concept makes use of a unique sequence for all the transmitting antennas that is rendered by an outer sequence to ensure the orthogonality of the transmitted waveforms. The outer code makes use of the good cross-correlation properties of the Hadamard sequences and the waveform uses sequences that exhibit perfect auto-correlation profile, the Almost Perfect Autocorrelation Sequences (APAS). The MIMO implementation results in higher angular resolution and extra processing gain. The use of beamforming techniques in the radar allows the angle estimation of the detected targets; using rough and fine beamforming that provides with coarse and precise Angle of Arrival (AoA) estimation in an early and late stage respectively. A Constant False Alarm Rate (CFAR) processing stage is implemented in the stage of the system where higher signal processing gain is achieved. This algorithm allows the variation of the CFAR parameters and analyzes the detections in order to improve the probability of detection (P_d) while decreasing the probability of false alarm (P_fa). A series of simulations with different scenarios and variable parameters are set in order to analyze the performance of the system. The simulations analyze the gain achieved in each stage and their outcomes show an impressive processing gain that can reach SNR improvements as high as 77 dB for a small virtual array while keeping the P_fa low with the CFAR adjustment. The use of bigger arrays demonstrates the possibility to enable clear detections for low Radar Cross Section (RCS) targets in far distances of the unambiguous range. The use of beamforming shows interference reduction improvement as the beam widths narrow with the increasing number of virtual array antennas. These results have been achieved while keeping the system design parameters to a range resolution of 7.5 cm for a maximum range of 37.5 meters with speed resolution of 0.2 m/s and a maximum detectable speed of 12.66 m/s. The outcomes support the good performance of the signal processing techniques implemented and the benefits in applying them in a SoC mmWave MIMO radar.

Coastal global navigation satellite system (GNSS) stations equipped with a standard geodetic receiver and antenna enable water level measurement using the GNSS interferometry reflectometry (GNSS-IR) technique. By using GNSS-IR, the vertical distance between the antenna and the reflector surface (e.g., water surface) can be obtained in the vertical (height) reference frame. In this study, the signal-to-noise ratio (SNR) data from four selected stations over three months are used for this purpose. We determined the predominant multipath frequency in SNR data that is obtained using Lomb–Scargle periodogram (LSP) method. The obtained sea surface heights (SSH) are assessed using tide gauge observations regarding accuracy and correlation coefficients. In this study, we investigated daily and hourly GNSS observations and used single frequencies of GPS (L1, L2 and L5), GLONASS (L1 and L2), Galileo (L1, L5, L6, L7 and L8), and BeiDou (L2 and L7) signals to estimate the SSH. The results show that the optimal signals for extracting the SSH are the L1 signal for the GPS, Galileo, and GLONASS systems and the L2 signal for the BeiDou system. The accuracy and correlation parameters for the optimal GPS signal in the daily mode are 2 cm and 0.87, respectively. The same parameters for the optimal GLONASS signal are 4 cm and 0.91. However, the obtained accuracy and correlation coefficients using the best Galileo and BeiDou signals are reduced, i.e., 4 cm and 0.88 using Galileo and 12 cm and 0.52 by employing the Galileo signals, respectively. Our results also show that the GPS L1 signal is more consistent with the tide gauge data. In the following, using the time series derived from the L1 signal and tide gauge readings, the tidal frequencies are extracted and compared using the Least Square Harmonic Estimation (LS-HE) approach. The findings demonstrate that 145 significant tidal frequencies can be extracted using the GNSS-IR time series. The existence of an acceptable correlation between the tidal frequencies of the GNSS-IR and the tide gauge time series indicates the usefulness of the GNSS-IR time series for tide studies. From our results, we can conclude that the GNSS-IR technique can be applied in coastal locations alongside tide gauge measurements for a variety of purposes.

Spectrum sensing is an essential pre-processing step of cognitive radio technology for dynamic radio spectrum management. One of the main functions of Cognitive radios is to detect the unused spectrum and share it without harmful interference with other users. The detection of signal components present within a determined frequency band is an important requirement of any sensing technique. Most methods are restricted to the detection of the spectral lines. However, these methods may not comply with the needs imposed by practical applications. 

This master thesis work presents a novel method to detect significant spectral components in measured non-flat spectra by classifying them in two groups: signal and noise frequency lines. The algorithm based on Fisher’s discriminant analysis, aside from the detection of spectral lines, estimates the magnitude of the spectral lines and provides a measure of the quality of classification to determine if a spectral line was incorrectly classified. Furthermore, the frequency lines with higher probability of misclassification are regrouped and the validation process recomputed, which results in lower probabilities of misclassification.

The proposed automatic detection algorithm requires no user interaction since any prior knowledge about the measured signal and the noise power is needed. The presence or absence of a signal regardless of the shape of the spectrum can be detected. Hence, this method becomes a strong basis for high-quality operation mode of cognitive radios.

Simulation and measurement results prove the advantages of the presented technique. The performance of the technique is evaluated for different signal-to-noise ratios (SNR) ranging from 0 to -21dB as required by the IEEE standard for smart radios. The method is compared with previous signal detection methods.

The impact of the signal bandwidth and observation bandwidth on the detection performance of the maximumminimum eigenvalue detector is studied in this letter. The considered signals are the Gaussian signals. The optimum ratio between the signal and the observation bandwidth is analytically proven to be 0.5 when reasonable values of the system dimensionality are used. The analytical proof is verified by simulations.

Combating the effects of mixer and power amplifier (PA) imperfections on transmitter performance is crucial for the design of wireless systems. PA compression and in-phase/quadrature imbalance are analyzed for a single-input-single-output transmitter model. The influence of the imperfection on the normalized mean squared error (NMSE) of the transmitter is studied using an analytical framework that relies on the classic Bussgang theory. The study concludes with a closed-form expression for the NMSE that provides insights into the behavior of the transmitter. © 2019 IEEE.

A combined L-S-band antenna module consists of four rectangular patch antennas, two of which operate at the frequencies of the first harmonics (L-band), and two others at the frequencies of the second harmonics (S-band). The module is intended for use in super heterodyne signal reception circuits, for example in studying the various non-linear phenomena. The results of this study are presented in terms of improving the performance of each antenna and minimising their mutual influence. The resonance frequencies of the L-band antenna differ by 50MHz. All the antennas radiate close to the zenith and their beamwidths in two principal planes are virtually the same. At the resonance frequencies, the reflection coefficient is S-11<-30dB for the transmitting antenna, and S-11<-20dB for the receiving antenna. The minimum allowable distance between the antennas is determined, at which their mutual influence can be neglected (S-12<-50dB).

This work is about digital signal processing methods to be used to determine information of low frequency low amplitude signals of known frequency. Different adaptive filter concepts such as Wiener filter, NLMS filter and lock-in are implemented and compared to each other. The comparison carried out for different input signal amplitude and noise variance with the objective to find the best algorithm for noise cancelling. The comparison is done using a signal of interest combined with white noise as input to the filter element. The aim of the comparison is to find the most appropriate filter for further signal analyzis. The key topics for the evaluation are the efficiency of noise cancelling and ease of implementation in a data processing unit.

Increasing demands for data rate, energy efficiency and reliability in wireless communications have resulted in the introduction of radio frequency (RF) multiple input multiple output (MIMO) transmitters. However, MIMO transmitters suffer from additional crosstalk impairments along with the power amplifier (PA) and I/Q imbalance distortions observed in single input single output (SISO) transmitters. Therefore, this thesis focuses on the characterization and compensation of these hardware impairments in RF SISO and MIMO transmitters.

PA distortions are often compensated using the Volterra series, but it suffers from high computational complexity. Therefore, a non-parametric method based on density estimation has been proposed in this thesis to estimate the PA transfer function, from which pruned Volterra models can be developed. The method is validated for a Doherty PA and achieves competitive error performance at a lower complexity than its competitors.

For MIMO transmitters, a characterization technique that uses multitone excitation signals has been proposed. Multitone signals yield non-overlapping tones at the outputs of the MIMO Volterra kernels. These kernel outputs are used to identify the dominant crosstalk impairments, from which block structure and base-band behavioral models are developed. The method is validated for 2x2 and 3x3 MIMO transmitters and it is shown that the derived models achieve a better complexity accuracy trade-off than the other pruned MIMO Volterra models considered in this thesis.

Finally, the thesis presents compensation models for joint static I/Q imbalance and MIMO PA distortions based on conjugate pair and real-valued basis functions. The models are augmented with sub-sample resolution to compensate for dynamic I/Q imbalance distortions. The proposed models are validated for a 2x2 RF MIMO transmitter and achieve a better complexity accuracy trade-off than the other state-of-the-art models considered in this thesis.

This paper presents a comparative analysis of the complexity accuracy tradeoff in state-of-the-art RF MIMO transmitter mitigation models. The complexity and accuracy of the candidate models depends on the basis functions considered in these models. Therefore, a brief description of the mitigation models is presented accompanied by derivations of the model complexities in terms of the number of FLOPs. Consequently, the complexity accuracy tradeoff in the candidate models is evaluated for a 2 × 2 RF MIMO transmitter. Furthermore, the model complexities are analyzed for increasing nonlinear orders and number of antennas.

This paper analyzes the joint effects of in-phase and quadrature (I/Q) imbalance and power amplifier (PA) distortion for RF multiple input multiple output (MIMO) transmitters in the presence of crosstalk. This paper proposes candidate models for the digital predistortion of static I/Q imbalanced sources exciting a dynamic MIMO Volterra system. The proposed models are enhanced using a novel technique based on subsample resolution to account for dynamic I/Q imbalance distortions. Finally, the computational complexity of the proposed models is analyzed for implementation suitability in digital platforms. It is shown that the error spectrum for the proposed models in subsample resolution reaches the noise floor of the measurements. The proposed models achieve a normalized mean squared error of -50 dB and an adjacent channel power ratio of -57 dB for signal bandwidths upto 65 MHz and crosstalk levels ranging to -10 dB. These results demonstrate the effectiveness of the proposed techniques in the joint mitigation of I/Q imbalance and PA distortion with crosstalk for a typical 2x2 MIMO telecommunication setup.

This paper uses multitone signals to simplify the analysis of 3×3 multiple-input multiple-output (MIMO) Volterra systems by isolating the third-order kernel outputs from each other. Multitone signals fed to an MIMO Volterra system yield a spectrum that is a permutation of the sums of the input signal tones. This a priori knowledge is used to design multitone signals such that the third-order kernel outputs are isolated in the frequency domain. The signals are designed by deriving the conditions for the offset and spacing of the input frequency grids. The proposed technique is then validated for the six possible configurations of a 3x3 RF MIMO transmitter impaired by crosstalk effects. The proposed multitone signal design is used to extract the third-order kernel outputs, and their relative contributions are analyzed to determine the dominant crosstalk effects for each configuration.

This paper proposes a technique for designing multitone signals that can separate the third order multiple input multiple output (MIMO) Volterra kernels. Multitone signals fed to a MIMO Volterra system yield a spectrum that is a permutation of the sums of the input signal tones. This a priori knowledge is used to design multitone signals such that the output from the MIMO Volterra kernels does not overlap in the frequency domain, hence making it possible to separate these kernels from the output of the MIMO Volterra system. The proposed technique is applied to a 2×2 RF MIMO transmitter to determine its dominant hardware impairments. For input crosstalk, the proposed method reveals the dominant self and cross kernels whereas for output crosstalk, the proposed method reveals that only the self kernels are dominant.

In this paper a mathematical technique is developed to find the parameters of a medium in terms of its scattered electromagnetic fields. Optical nonlinearity plays an important role in finding the scattering parameters of a medium. Using perturbation theory and nonlinear inverse scattering techniques with first order, second order and third order optical nonlinearity we find scattered electromagnetic fields. Using error minimization techniques parameters are estimated in term of permittivity and permeability up to second order. © 2019 IEEE.

This paper presents design and analysis of meta-material based bowtie-shaped fractal antenna for the frequency range of l.l-l.8 THz for futuristic Terahertz applications. A meta-material-inspired structure isinserted on ground plane to enhance overall performance of the proposed design. A comparative analysis of various iterated designs has been carried out in detail. Performance parameters e.g. return loss, antenna gain, radiation efficiency, radiation pattern are presented and discussed. Simulated resultsshow the multiband nature of designed antennas. Maximum gain of 6.59 dB and return loss of −21.00 dB are obtained at 1.5455 THz and 1.1787 THz respectively. HFSS simulation software is used for simulation of the designs.

A multi-band slotted bowtie antenna for L, S and C band applications is presented. The proposed antenna has the configuration of 40 mm58 mm1.5 mm. The slots are etched on the bowtie antenna to obtain multi-band features. The number of slots and spacing between them is varied for parametric study. The designed is fabricated on FR4 material and the measured and simulated results are in good agreement with each other thus confirming the proposed antenna design. High Frequency Structural Simulator (HFSS) is used for simulating the antenna design.

A modified symmetric split ring resonator (MSSRR) loaded bowtie antenna with multi-band features over a range of 1.6–7.1 GHz is proposed in this paper. The antenna is designed on FR4 substrate with relative permittivity εr = 4.1 and thickness 1.5 mm. The fabricated antenna size is 40 mm × 58 mm. The Modified Symmetric SRR used beneath the substrate is novel and consists of two circles that are broken into four quarter circles by four rods. The effect of MSSRR location and its geometry is investigated and analysed thoroughly. The measurement results of the fabricated antenna are in good agreement with the simulation results, which confirms the proposed antenna design’s properties. ANSYS High Frequency Structural Simulator (HFSS) software is used for simulating the antenna.

In this paper, we show both theoretically and experimentally that the probability density function of the intensity of an amplified signal by parametric amplifiers subject to a pump with excess noise is highly asymmetric. This is due to the nonlinear relationship between the optical pump power and the parametric gain. Because of this, the relationship between the noise figure (NF) and the bit error rate (BER) is modified, compared with that predicted by the chi(2) theory, which is an effect that is notable at large NFs and low BERs. The difference in predicted BER can be of several orders of magnitudes between the correct theory and the chi(2) approximation in single-stage parametric amplifiers. We also show that in the limit of many cascaded parametric amplifiers, the statistics of the noise of an amplified optical signal approaches chi(2). Furthermore, the BER of a parametric amplifier is generally lower compared with erbium-doped fiber amplifiers for the same NF values if we assume quantum-limited amplification.

A new method of signal processing by smart antennas is proposed and justified. It allows to improve the accuracy of angle measurement and to restore the image of the object with superresolution. The method is based on the extrapolation of the signals received by each element of the antenna array, outside the aperture. This allows introducing new virtual elements and thus synthesizing significantly larger antenna array. The method is tested in numerical experiments using a mathematical model and the maximum effective angular resolution is found for different cases and objects. Algorithms based on the method of digital aperture synthesis provide angular superresolution 3-7 times greater than that according to the Rayleigh criterion for a signal/noise ratio of 12-13 dB.

This paper deals with the implementation and performance evaluation of adaptive filtering algorithms for noise cancellation without reference signal. Noise cancellation is a technique of estimating a desired signal from a noise-corrupted observation. If the signal and noise characteristics are unknown or change continuously over time, the need of adaptive filter arises. In contrast to the conventional digital filter design techniques, adaptive filters do not have constant filter parameters, they have the capability to continuously adjust their coefficients to their operating environment. To design an adaptive filter, that produces an optimum estimate of the desired signal from the noisy environment, different adaptive filtering algorithms are implemented and compared to each other. The Least Mean Square LMS, the Normalized Least Mean Square NLMS and the Recursive Least Square RLS algorithm are investigated. Three performance criteria are used in the study of these algorithms: the rate of convergence, the error performance and the signal-to-noise ratio SNR. The implementation results show that the adaptive noise cancellation application benefits more from the use of the NLMS algorithm instead of the LMS or RLS algorithm.