Blind Carrier Phase Recovery for General 2{\pi}/M-rotationally Symmetric Constellations

This paper introduces a novel blind carrier phase recovery estimator for general 2{\Pi}/M-rotationally symmetric constellations. This estimation method is a generalization of the non-data-aided (NDA) nonlinear Phase Metric Method (PMM) estimator already designed for general quadrature amplitude constellations. This unbiased estimator is seen here as a fourth order PMM then generalized to Mth order (Mth PMM) in such manner that it covers general 2{\Pi}/M-rotationally symmetric constellations such as PAM, QAM, PSK. Simulation results demonstrate the good performance of this Mth PMM estimation algorithm against competitive blind phase estimators already published for various modulation systems of practical interest.Comment: 14 pages, 12 figures, International Journal of Wireless & Mobile Networks (IJWMN

On a Hybrid Preamble/Soft-Output Demapper Approach for Time Synchronization for IEEE 802.15.6 Narrowband WBAN

In this paper, we present a maximum likelihood (ML) based time synchronization algorithm for Wireless Body Area Networks (WBAN). The proposed technique takes advantage of soft information retrieved from the soft demapper for the time delay estimation. This algorithm has a low complexity and is adapted to the frame structure specified by the IEEE 802.15.6 standard for the narrowband systems. Simulation results have shown good performance which approach the theoretical mean square error limit bound represented by the Cramer Rao Bound (CRB)

Conditional maximum likelihood timing recovery: estimators and bounds

This paper is concerned with the derivation of new estimators and performance bounds for the problem of timing estimation of (linearly) digitally modulated signals. The conditional maximum likelihood (CML) method is adopted, in contrast to the classical low-SNR unconditional ML (UML) formulation that is systematically applied in the literature for the derivation of non-data-aided (NDA) timing-error-detectors (TEDs). A new CML TED is derived and proved to be self-noise free, in contrast to the conventional low-SNR-UML TED. In addition, the paper provides a derivation of the conditional Cramér–Rao Bound (CRB ), which is higher (less optimistic) than the modified CRB (MCRB) [which is only reached by decision-directed (DD) methods]. It is shown that the CRB is a lower bound on the asymptotic statistical accuracy of the set of consistent estimators that are quadratic with respect to the received signal. Although the obtained bound is not general, it applies to most NDA synchronizers proposed in the literature. A closed-form expression of the conditional CRB is obtained, and numerical results confirm that the CML TED attains the new bound for moderate to high Eg/No.Peer Reviewe

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

Analytic and Asymptotic Analysis of Bayesian Cramér-Rao Bound for Dynamical Phase Offset Estimation

International audienceIn this paper, we present a closed-form expression of a Bayesian Cramér-Rao lower bound for the estimation of a dynamical phase offset in a non-data-aided BPSK transmitting context. This kind of bound is derived considering two different scenarios: a first expression is obtained in an off-line context and then, a second expression in an on-line context logically follows. The SNR-asymptotic expressions of this bound drive us to introduce a new asymptotic bound, namely the Asymptotic Bayesian Cramér-Rao Bound. This bound is close to the classical Bayesian bound but is easier to evaluate

ML-Type EM-Based Estimation of Fast Time-Varying Frequency-Selective Channels Over SIMO OFDM Transmissions

This paper investigates the problem of fast time-varying frequency-selective (i.e., multipath) channel estimation over single-input multiple-output orthogonal frequency-division multiplexing (SIMO OFDM)-type transmissions. We do so by tracking the variations of each complex gain coefficient using a polynomial-in-time expansion. To that end, we derive the log-likelihood function (LLF) both in the data-aided (DA) and non-data-aided (NDA) cases. The DA maximum likelihood (ML) estimates over fast SIMO OFDM channels are derived here for the first time in closed-form expressions and hereby shown to be limited to applying over each receive antenna the DA least squares (LS) estimator tailored in [1] to fast SISO OFDM channels. This DA ML is used to initialize periodically, over a relatively large number of data blocks (i.e., with further reduced and relatively close-to-negligible pilot overhead compared to DA ML), a new expectation maximization (EM) ML-type solution we developed here in the NDA case to iteratively maximize the LLF. We also introduce an alternative regularized DA ML (RDM) initialization solution no longer requesting - in contrast to DA ML - more per-carrier pilot frames than the number of paths to further reduce overhead without incurring significant performance losses. Simulation results show that the proposed hybrid ML-EM estimator (i.e., combines all new NDA ML-EM and DA ML or RDM versions) converges within few iterations, thereby providing very accurate estimates of all multipath channel gains. Most importantly, this increased estimation accuracy translates into very significant BER and link-level per-carrier throughput gains over the best representative benchmark solution available so far for the problem at hand, the SISO DA LS technique in [1] with its new generalization here to SIMO systems

Frequency synchronization in HSPA+/LTE communications : a general model and towards uniform implementation

[no abstract