429 research outputs found
Power delay profile and noise variance estimation for OFDM
In this letter, we present cyclic-prefix (CP) based noise-variance and power-delay-profile estimators for Orthogonal Frequency Division Multiplexing (OFDM) systems. Signal correlation due to the use of the CP is exploited without requiring additional pilot symbols. A heuristic estimator and a class of approximate maximum likelihood (ML) estimators are proposed. The proposed algorithms can be applied to both unitary and non-unitary constellations. These algorithms can be readily used for applications such as minimum mean-square error (MMSE) channel estimation
An Efficient Data-aided Synchronization in L-DACS1 for Aeronautical Communications
L-band Digital Aeronautical Communication System type-1 (L-DACS1) is an
emerging standard that aims at enhancing air traffic management (ATM) by
transitioning the traditional analog aeronautical communication systems to the
superior and highly efficient digital domain. L-DACS1 employs modern and
efficient orthogonal frequency division multiplexing (OFDM) modulation
technique to achieve more efficient and higher data rate in comparison to the
existing aeronautical communication systems. However, the performance of OFDM
systems is very sensitive to synchronization errors. L-DACS1 transmission is in
the L-band aeronautical channels that suffer from large interference and large
Doppler shifts, which makes the synchronization for L-DACS more challenging.
This paper proposes a novel computationally efficient synchronization method
for L-DACS1 systems that offers robust performance. Through simulation, the
proposed method is shown to provide accurate symbol timing offset (STO)
estimation as well as fractional carrier frequency offset (CFO) estimation in a
range of aeronautical channels. In particular, it can yield excellent
synchronization performance in the face of a large carrier frequency offset.Comment: In the proceeding of International Conference on Data Mining,
Communications and Information Technology (DMCIT
Advanced methods in automatic modulation classification for emerging technologies
Modulation classification (MC) is of large importance in both military and commercial communication applications. It is a challenging problem, especially in non-cooperative wireless environments, where channel fading and no prior knowledge on the incoming signal are major factors that deteriorate the reception performance. Although the average likelihood ratio test method can provide an optimal solution to the MC problem with unknown parameters, it suffers from high computational complexity and in some cases mathematical intractability. Instead, in this research, an array-based quasi-hybrid likelihood ratio test (qHLRT) algorithm is proposed, which depicts two major advantages. First, it is simple yet accurate enough parameter estimation with reduced complexity. Second the incorporation of antenna arrays offers an effective ability to combat fading. Furthermore, a practical array-based qHLRT classifier scheme is implemented, which applies maximal ratio combining (MRC) to increase the accuracy of both carrier frequency offset (CFO) estimation and likelihood function calculation in channel fading. In fact, double CFO estimations are executed in this classifier. With the first the unknown CFO, phase offsets and amplitudes are estimated as prerequisite for MRC operation. Then, MRC is performed using these estimates, followed by a second CFO estimator. Since the input of the second CFO estimator is the output of the MRC, fading effects on the incoming signals are removed significantly and signal-to-noise ratio (SNR) is augmented. As a result, a more accurate CFO estimate is obtained. Consequently, the overall classification performance is improved, especially in low SNR environment.
Recently, many state-of-the-arts communication technologies, such as orthogonal frequency division multiplexing (OFDM) modulations, have been emerging. The need for distinguishing OFDM signal from single carrier has become obvious. Besides, some vital parameters of OFDM signals should be extracted for further processing. In comparison to the research on MC for single carrier single antenna transmission, much less attention has been paid to the MC for emerging modulation methods. A comprehensive classification system is proposed for recognizing the OFDM signal and extracting its parameters. An automatic OFDM modulation classifier is proposed, which is based on the goodness-of-fittest. Since OFDM signal is Gaussian, Cramer-von Mises technique, working on the empirical distribution function, has been applied to test the presence of the normality. Numerical results show that such approach can successfully identify OFDM signals from single carrier modulations over a wide SNR range. Moreover, the proposed scheme can provide the acceptable performance when frequency-selective fading is present. Correlation test is then applied to estimate OFDM cyclic prefix duration. A two-phase searching scheme, which is based on Fast Fourier Transform (FFT) as well as Gaussianity test, is devised to detect the number of subcarriers. In the first phase, a coarse search is carried out iteratively. The exact number of subcarriers is determined by the fine tune in the second phase. Both analytical work and numerical results are presented to verify the efficiency of the proposed scheme
Preprint: Using RF-DNA Fingerprints To Classify OFDM Transmitters Under Rayleigh Fading Conditions
The Internet of Things (IoT) is a collection of Internet connected devices
capable of interacting with the physical world and computer systems. It is
estimated that the IoT will consist of approximately fifty billion devices by
the year 2020. In addition to the sheer numbers, the need for IoT security is
exacerbated by the fact that many of the edge devices employ weak to no
encryption of the communication link. It has been estimated that almost 70% of
IoT devices use no form of encryption. Previous research has suggested the use
of Specific Emitter Identification (SEI), a physical layer technique, as a
means of augmenting bit-level security mechanism such as encryption. The work
presented here integrates a Nelder-Mead based approach for estimating the
Rayleigh fading channel coefficients prior to the SEI approach known as RF-DNA
fingerprinting. The performance of this estimator is assessed for degrading
signal-to-noise ratio and compared with least square and minimum mean squared
error channel estimators. Additionally, this work presents classification
results using RF-DNA fingerprints that were extracted from received signals
that have undergone Rayleigh fading channel correction using Minimum Mean
Squared Error (MMSE) equalization. This work also performs radio discrimination
using RF-DNA fingerprints generated from the normalized magnitude-squared and
phase response of Gabor coefficients as well as two classifiers. Discrimination
of four 802.11a Wi-Fi radios achieves an average percent correct classification
of 90% or better for signal-to-noise ratios of 18 and 21 dB or greater using a
Rayleigh fading channel comprised of two and five paths, respectively.Comment: 13 pages, 14 total figures/images, Currently under review by the IEEE
Transactions on Information Forensics and Securit
Advanced classification of OFDM and MIMO signals with enhanced second order cyclostationarity detection
With the emergence of cognitive radio and the introduction of new modulation techniques such as OFDM and MIMO, the problem of Modulation Classification (MC) becomes more challenging and complicated. In the first part of the thesis, we explore the automatic modulation classification to blindly distinguish OFDM from single carrier signals. We use the fourth order cumulants; an approach which in the past has been also applied to classify single carrier signals. A blind OFDM parameter estimation scheme was then followed, which includes the estimation of number of subcarriers, CP length, timing and frequency offset and the oversampling factor for the OFDM signal. For the second part of the thesis, we improve the statistical signal processing techniques that were used in the first part. Particularly, the second order cyclostationarity based methods have been examined and improved. Based on the fact that most of the cyclostationary communication signals has a real cyclostationary part and a complex non-cyclostaionary part, we suggest an approach that enhance the second order cyclostationarity and hence increase its probability of detection. Using such improved second-order cyclostationarity, we present an improved synchronization method based on second order cyclostationarity. With the proposed approach, it is shown that the timing estimator, is independent of the frequency offset estimator, and therefore performs better than the previously proposed class of blind synchronization methods. To negate the dependence of the blind synchronization scheme on the prior knowledge of the raised cosine pulse shaping filters, we proposed a blind roll-off factor estimator based on the second order cyclostationarity. Last, we address the MIMO classification problem, wherein we estimate the number of transmitting antennas. Here the second order cyclostationarity test has been applied in distinguishing STC from BLAST modulation
Blind Estimation of Multiple Carrier Frequency Offsets
Multiple carrier-frequency offsets (CFO) arise in a distributed antenna
system, where data are transmitted simultaneously from multiple antennas. In
such systems the received signal contains multiple CFOs due to mismatch between
the local oscillators of transmitters and receiver. This results in a
time-varying rotation of the data constellation, which needs to be compensated
for at the receiver before symbol recovery. This paper proposes a new approach
for blind CFO estimation and symbol recovery. The received base-band signal is
over-sampled, and its polyphase components are used to formulate a virtual
Multiple-Input Multiple-Output (MIMO) problem. By applying blind MIMO system
estimation techniques, the system response is estimated and used to
subsequently transform the multiple CFOs estimation problem into many
independent single CFO estimation problems. Furthermore, an initial estimate of
the CFO is obtained from the phase of the MIMO system response. The Cramer-Rao
Lower bound is also derived, and the large sample performance of the proposed
estimator is compared to the bound.Comment: To appear in the Proceedings of the 18th Annual IEEE International
Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC),
Athens, Greece, September 3-7, 200
Preamble-Based Channel Estimation for CP-OFDM and OFDM/OQAM Systems: A Comparative Study
In this paper, preamble-based least squares (LS) channel estimation in OFDM
systems of the QAM and offset QAM (OQAM) types is considered, in both the
frequency and the time domains. The construction of optimal (in the mean
squared error (MSE) sense) preambles is investigated, for both the cases of
full (all tones carrying pilot symbols) and sparse (a subset of pilot tones,
surrounded by nulls or data) preambles. The two OFDM systems are compared for
the same transmit power, which, for cyclic prefix (CP) based OFDM/QAM, also
includes the power spent for CP transmission. OFDM/OQAM, with a sparse preamble
consisting of equipowered and equispaced pilots embedded in zeros, turns out to
perform at least as well as CP-OFDM. Simulations results are presented that
verify the analysis
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