Comparison of SPS and FA methods for Blind Carrier Frequency Offset Estimation in OFDM systems

In orthogonal frequency-division multiplexing (OFDM) systems, synchronization issues are of great importance since synchronization errors might destroy the orthogonality among all subcarriers and, therefore, introduce intercarrier interference (ICI) and intersymbol interference (ISI). Several schemes of frequency offset estimation in OFDM systems have been investigated. This paper compares performance and computational complexity of Smoothing Power Spectrum (SPS) and Frequency Analysis (FA) methods for blind carrier frequency offset (CFO) estimation in OFDM systems.

Estimasi Carrier Ferquency Offset menggunakan Timing Metric pada Sinyal OFDM

OFDM is one of technology that can be utilized in a variety of telecommunication systems that being widely developed today, for application in LAN, WLAN, 3G, 4G, or 5G. One of the problem faced by the OFDM technology that its sensitivity to Carrier Frequency Offset (CFO) and the lack of synchronization in the OFDM signal. This research aims to design the synchronization that estimates Carrier Frequency Offset (CFO) to obtain synchronization of OFDM signal, where the error of the estimated Carrier Frequency Offset can be obtained, minimized and better than previous studies. The CFO estimation method in this research is using the training symbol on the OFDM symbol and utilize the statistical characteristics of the timing metric. This researchs result shows the Mean Square Error (MSE) of estimated Carrier Frequency Offset to Carrier Frequency Offset input, with range MSE 9.43 x 10-3 at 0 dB SNR input and MSE 1.687 x 10-5 at 30 dB SNR input. If Signal to Noise Ratio is greater, then the value of the mean square error (MSE) will be smaller. The position of the timing metric for timing estimation also affects to CFO estimation. CFO estimation accuracy will be maximized when using maximum timing metric.

GNSS time and frequency transfers through national positioning, navigation and timing infrastructure

GNSS signals have been a practical time transfer tool to realise a Coordinated Universal Time (UTC) and set civilian clocks around the world with respect to this atomic time standard. UTC time scale is maintained by the International Bureau of Weights and Measurements (BIPM) adjusted to be close to a time scale based on the Earth’s rotation. In Thailand, the official atomic time clocks are maintained by the National Institute of Metrology Thailand (NIMT) to produce UTC(NIMT) and Thailand standard time which is always 7 hours ahead of UTC(NIMT) because of the time zone differences between Greenwich and Bangkok. National Positioning, Navigation and Timing (PNT) infrastructure comprises of GNSS geodetic receivers uniformly distributed to continually observe GNSS signals, mainly for geodetic survey applications both real-time and post-processing services. NIMT is involved in order to provide time link to UTC and to determine the characteristics of GNSS receiver internal clocks; namely, fractional frequency offset and frequency stabilities by applying the GNSS time transfer techniques of common-view algorithms. Monitored time differences with respect to UTC(NIMT) are achieved from selected 4 ground stations in different parts of the country with observations of 21 days in order to determine the frequency stability at 1-day and 7-day modes. GNSS standard log files; in RINEX format, at these receivers are transformed into a time transfer standard format; CGGTTS, used to compute the time differences between two stations, the fractional frequency offset and the frequency stability. Averaged fractional frequency offsets are 2.8 × 10 − 13 Hertz/Hertz 2.8\times {10^{-13}}\hspace{2.38387pt}\text{Hertz/Hertz} and computed Allan deviation is around 1.5 × 10 − 13 Hertz/Hertz 1.5\times {10^{-13}}\hspace{2.38387pt}\text{Hertz/Hertz} for an averaging time of 1 day. The comparison of the national time scale and receiver clock offsets of every receivers in this national GNSS PNT infrastructure could be accomplished through common-view time transfer using GNSS satellites to maintain the time link of geodetic active control points to UTC as well as to determine receiver internal clock characteristics.

A Carrier Parameter Decoupling Algorithm for High Order APSK Modulation

In satellite communication, carrier parameter estimation usually uses a serial structure, and the accuracy of frequency offset estimation (FOE) will greatly affect the accuracy of phase offset estimation (POE). A new carrier synchronization mode (NCSM) can realize the decoupling of carrier FOE and POE to a certain extent, but this mode is based on multibase phase shift keying (MPSK) modulation analysis, the decoupling performance is poor when uses in amplitude phase shift keying (APSK) modulation, and the decoupling performance of NCSM has a low tolerance of frequency offset. An improved carrier parameter estimation decoupling technique is proposed to solve these problems. The simulation results show that, compared with the original method, under the premise of ensuring the accuracy of carrier parameter estimation, the proposed method is more robust to the modulation mode, the POE has stronger antioffset ability, and the normalized FOE range has been significantly enhanced.

Accuracy Analysis of Feature-Based Automatic Modulation Classification via Deep Neural Network

A feature-based automatic modulation classification (FB-AMC) algorithm has been widely investigated because of its better performance and lower complexity. In this study, a deep learning model was designed to analyze the classification performance of FB-AMC among the most commonly used features, including higher-order cumulants (HOC), features-based fuzzy c-means clustering (FCM), grid-like constellation diagram (GCD), cumulative distribution function (CDF), and raw IQ data. A novel end-to-end modulation classifier based on deep learning, named CCT classifier, which can automatically identify unknown modulation schemes from extracted features using a general architecture, was proposed. Features except GCD are first converted into two-dimensional representations. Then, each feature is fed into the CCT classifier for modulation classification. In addition, Gaussian channel, phase offset, frequency offset, non-Gaussian channel, and flat-fading channel are also introduced to compare the performance of different features. Additionally, transfer learning is introduced to reduce training time. Experimental results showed that the features HOC, raw IQ data, and GCD obtained better classification performance than CDF and FCM under Gaussian channel, while CDF and FCM were less sensitive to the given phase offset and frequency offset. Moreover, CDF was an effective feature for AMC under non-Gaussian and flat-fading channels, and the raw IQ data can be applied to different channels’ conditions. Finally, it showed that compared with the existing CNN and K-S classifiers, the proposed CCT classifier significantly improved the classification performance for MQAM at N = 512, reaching about 3.2% and 2.1% under Gaussian channel, respectively.