Measuring the Power Law Phase Noise of an RF Oscillator with a Novel Indirect Quantitative Scheme

Chen, Xiaolong; Peng, Cuiling; Huan, Huiting; Nian, Fushun; Yang, Bin

doi:10.3390/electronics8070767

Cited by 6 publications

(3 citation statements)

References 23 publications

(40 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is possible to further implement the MIM as a spatially-resolved readout instrument for memories with great resistivity changes [72]. MIM cannot detect small topographic changes with phases changes, but can directly measure local electronic properties and is more sensitive to operations [73][74][75][76]. The author demonstrates that the MIM can provide spatially resolved information when In 2 Se 3 nano-devices are phase-switched by voltage pulses [72].…”

Section: Electronic Inhomogeneitymentioning

confidence: 98%

Developments and Recent Progresses in Microwave Impedance Microscope

et al. 2020

Self Cite

View full text Add to dashboard Cite

Microwave impedance microscope (MIM) is a near-field microwave technology which has low emission energy and can detect samples without any damages. It has numerous advantages, which can appreciably suppress the common-mode signal as the sensing probe separates from the excitation electrode, and it is an effective device to represent electrical properties with high spatial resolution. This article reviews the major theories of MIM in detail which involve basic principles and instrument configuration. Besides, this paper summarizes the improvement of MIM properties, and its cutting-edge applications in quantitative measurements of nanoscale permittivity and conductivity, capacitance variation, and electronic inhomogeneity. The relevant implementations in recent literature and prospects of MIM based on the current requirements are discussed. Limitations and advantages of MIM are also highlighted and surveyed to raise awareness for more research into the existing near-field microwave microscopy. This review on the ongoing progress and future perspectives of MIM technology aims to provide a reference for the electronic and microwave measurement community.

show abstract

Section: Electronic Inhomogeneitymentioning

confidence: 98%

Developments and Recent Progresses in Microwave Impedance Microscope

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The PSD of the PN is typically modelled via a power law noise [ 44 , 45 ]:

where f is the frequency,

is a constant parameter of the model and

are the summation parameters, defining the PN type, e.g.,

corresponds to a white-noise model (with 0 for additive white noise sources external to the oscillator and 2 for additive white noise sources internal to the oscillator),

corresponds to a flicker PN (i.e., 1 for flicker phase noise and 3 for flicker frequency noise), and

corresponds to a random-walk PN.…”

Section: Transmitter Hardware Impairments or ‘Rf Features’ Overviewmentioning

confidence: 99%

A Survey of Spoofer Detection Techniques via Radio Frequency Fingerprinting with Focus on the GNSS Pre-Correlation Sampled Data

Wang

Sánchez

Caparra

et al. 2021

Sensors

View full text Add to dashboard Cite

Radio frequency fingerprinting (RFF) methods are becoming more and more popular in the context of identifying genuine transmitters and distinguishing them from malicious or non-authorized transmitters, such as spoofers and jammers. RFF approaches have been studied to a moderate-to-great extent in the context of non-GNSS transmitters, such as WiFi, IoT, or cellular transmitters, but they have not yet been addressed much in the context of GNSS transmitters. In addition, the few RFF-related works in GNSS context are based on post-correlation or navigation data and no author has yet addressed the RFF problem in GNSS with pre-correlation data. Moreover, RFF methods in any of the three domains (pre-correlation, post-correlation, or navigation) are still hard to be found in the context of GNSS. The goal of this paper was two-fold: first, to provide a comprehensive survey of the RFF methods applicable in the GNSS context; and secondly, to propose a novel RFF methodology for spoofing detection, with a focus on GNSS pre-correlation data, but also applicable in a wider context. In order to support our proposed methodology, we qualitatively investigated the capability of different methods to be used in the context of pre-correlation sampled GNSS data, and we present a simulation-based example, under ideal noise conditions, of how the feature down selection can be done. We are also pointing out which of the transmitter features are likely to play the biggest roles in the RFF in GNSS, and which features are likely to fail in helping RFF-based spoofing detection.

show abstract

“…In many types of time series of natural phenomena, noise can be described by a power-law noise process [29]. This kind of noise has been identified in, e.g., music [30], economics [31], geophysical data [32], electronic devices [33], astronomy [34], and GNSS positions [35]. There are several ways to determine this kind of noise, most of which are based on spectral analyses or maximum likelihood estimation [32,36].…”

Section: Introductionmentioning

confidence: 99%

Noises in Double-Differenced GNSS Observations

2022

View full text Add to dashboard Cite

Precise data processing from the Global Navigation Satellite Systems (GNSS) reference station network is mainly based on a combination of double-differenced carrier phase and code observations. This approach allows most of the measurement errors to be removed or reduced and is characterized as the most accurate method. However, creating observation differences between two receivers and two satellites increases the measurement noise of the observations by a factor of 2. As a result, it increases the impact of the incorrect definition of the noise characteristic on the results of the estimation of the unknowns in the positioning model. This is especially important in Multi-GNSS solutions, which integrate measurements from different systems, for which the stochastic parameters of observation may differ significantly. In this paper, the authors prepared a complex analysis of the noise type in double-differenced GNSS (GPS, GLONASS and Galileo) observations, both carrier phase and code ones, with a 1 s sampling interval. The Autocorrelation Function (ACF) method, the Lomb–Scargle (L-S) periodogram method, and the Allan variance (AVAR) method were used. The results that were obtained for the weekly set of measurement data showed that, depending on the system and type of observation, the noise level and its type are significantly different. Among the code measurements, the lowest noise levels were obtained for the GPS C5Q and Galileo C7Q/C8Q observations, with the standard deviations not exceeding ±10 cm, while the noisiest observations were for the GLONASS C1C and C2C signals, which had standard deviations of about ±90 cm and ±45 cm, respectively. For the carrier phase observations, each signal type was characterized by very similar noise levels of ±1.5–3.5 mm. The ACF analysis showed that 1 Hz double-differenced GNSS data can only be treated as being not correlated to time for carrier phase observations; for code observations, an irrelevant autocorrelation may be considered for measurement intervals greater than 20 s. Depending on the GNSS signals, the spectral index k varies in a range from −1.3 to −0.2 for code data and k = 0.0 in the case of phase data. Using the modified Allan deviation (MDEV) allows for specific noise types for each signal and GNSS system to be determined. All of the code observations were characterized by either flicker PM or white PM. In the case of the phase observations, they were all uniquely characterized by white PM (GPS and Galileo or by white PM and flicker PM (GLONASS).

show abstract

Measuring the Power Law Phase Noise of an RF Oscillator with a Novel Indirect Quantitative Scheme

Cited by 6 publications

References 23 publications

Developments and Recent Progresses in Microwave Impedance Microscope

Developments and Recent Progresses in Microwave Impedance Microscope

A Survey of Spoofer Detection Techniques via Radio Frequency Fingerprinting with Focus on the GNSS Pre-Correlation Sampled Data

Noises in Double-Differenced GNSS Observations

Contact Info

Product

Resources

About