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

A new bound and algorithm for star 16-QAM carrier phase estimation

Copyright © 2003 IEEEThe true Cramer-Rao lower bound (CRLB) is derived and evaluated for the estimation of carrier phase of Star 16-quadrature amplitude modulation (QAM) and can be simply applied to carrier frequency estimation. Different geometries are investigated by varying the ring ratio (RR). For signal-to-noise ratios (SNRs) between 6-15 dB, the CRLB with RR=3 is lower than that of Square 16-QAM. A modified phase estimator is presented, which closely follows the new CRLB. Investigation of symbol error performance in short packet length reveals Star 16-QAM to be superior to Square 16-QAM for SNR<13 dB, which is a reasonable operating range for a coded system. Although Square 16-QAM and Star RR=1.8 are optimum for a perfect receiver, when the effect of phase estimation is considered, we find Star RR=3 to be better for SNR below 10 dB.Feng Rice, Mark Rice, and Bill Cowle

Channel estimation, data detection and carrier frequency offset estimation in OFDM systems

Orthogonal Frequency Division Multiplexing (OFDM) plays an important role in the implementation of high data rate communication. In this thesis, the problems of data detection and channel and carrier frequency offset estimation in OFDM systems are studied. Multi-symbol non-coherent data detection is studied which performs data detection by processing multiple symbols without the knowledge of the channel impulse response (CIR). For coherent data detection, the CIR needs to be estimated. Our objective in this thesis is to work on blind channel estimators which can extract the CIR using just one block of received OFDM data. A blind channel estimator for (Single Input Multi Output) SIMO OFDM systems is derived. The conditions under which the estimator is identifiable is studied and solutions to resolve the phase ambiguity of the proposed estimator are given.A channel estimator for superimposed OFDM systems is proposed and its CRB is derived. The idea of simultaneous transmission of pilot and data symbols on each subcarrier, the so called superimposed technique, introduces the efficient use of bandwidth in OFDM context. Pilot symbols can be added to data symbols to enable CIR estimation without sacrificing the data rate. Despite the many advantages of OFDM, it suffers from sensitivity to carrier frequency offset (CFO). CFO destroys the orthogonality between the subcarriers. Thus, it is necessary for the receiver to estimate and compensate for the frequency offset. Several high accuracy estimators are derived. These include CFO estimators, as well as a joint iterative channel/CFO estimator/data detector for superimposed OFDM. The objective is to achieve CFO estimation with using just one OFDM block of received data and without the knowledge of CIR

Carrier frequency offset estimation for orthogonal frequency division multiplexing systems

Orthogonal frequency division multiplexing (OFDM) is an attractive modulation scheme used in wideband communications because it essentially transforms the frequency selective channel into a flat fading channel. Furthermore, the combination of multiple-input multiple-output (MIMO) signal processing and OFDM seems to be an ideal solution for supporting reliable high data rate transmission for future wireless communication systems. However, despite the great advantages OFDM systems offer, such systems present challenges of their own. One of the most important challenges is carrier frequency offset (CFO) estimation, which is crucial in building reliable wireless communication systems. In this thesis, we consider CFO estimation for the downlink and uplink OFDM systems. For the downlink channel, we focus on blind schemes where the cost functions are designed such that they exploit implicit properties associated with the transmitted signal where no training signal is required. By taking the unconditional maximum likelihood approach, we propose a virtual subcarrier based blind scheme for MIMO-OFDM systems in the presence of spatial correlation. We conclude that the presence of spatial correlation does not impact the CFO estimation significantly. We also propose a CFO estimator for OFDM systems with constant modulus signaling and extend it to MIMO-OFDM systems employing orthogonal space-time block coding. The curve fitting method is used which gives a closed-form expression for CFO estimation. Therefore, the proposed scheme provides an excellent trade-off between complexity and performance as compared to prominent existing estimation schemes. Furthermore, we design a blind CFO estimation scheme for differentially modulated OFDM systems based on the finite alphabet constraint. It can achieve better performance at high signal-to-noise ratios (SNRs) at the expense of some additional computational complexity as compared to the schemes based on the constant modulus constraint. The constrained Cramer-Rao lower bound (CRLB) is also derived for the blind estimation scheme. As for the uplink channel, which is a more challenging problem, we propose two training aided schemes. One is based on a scalar extended Kalman filter (EKF) and the other one is on the variable projection (VP) algorithm. For both schemes, we assume that the system uses an arbitrary subcarrier assignment scheme, which is more involved than the other two schemes, namely block and interleaved subcarrier assignment scheme. In the first scheme, to apply the scalar EKF algorithm, we represent the measurement equation as a function of a scalar state, i.e., each user's CFO, in lieu of a state vector which consists of both CFO and channel coefficients by replacing the unknown channel coefficients with a nonlinear function of CFO. This proposed scheme can achieve the CRLB at high SNR for two users with a complexity lower than that of the alternating-projection method. In the second scheme, the VP algorithm is used for CFO estimation which is followed with a robust minimum mean square error (MMSE) estimator for channel estimation. In the VP algorithm, the nonlinear least square cost function is optimized numerically by updating the CFOs and channel coefficients separately at each iteration. We demonstrate that this proposed scheme is superior to the existing methods in terms of convergence speed, computational complexity and estimation performance

Channel Estimation in Coded Modulation Systems

With the outstanding performance of coded modulation techniques in fading channels, much research efforts have been carried out on the design of communication systems able to operate at low signal-to-noise ratios (SNRs). From this perspective, the so-called iterative decoding principle has been applied to many signal processing tasks at the receiver: demodulation, detection, decoding, synchronization and channel estimation. Nevertheless, at low SNRs, conventional channel estimators do not perform satisfactorily. This thesis is mainly concerned with channel estimation issues in coded modulation systems where different diversity techniques are exploited to combat fading in single or multiple antenna systems. First, for single antenna systems in fast time-varying fading channels, the thesis focuses on designing a training sequence by exploiting signal space diversity (SSD). Motivated by the power/bandwidth efficiency of the SSD technique, the proposed training sequence inserts pilot bits into the coded bits prior to constellation mapping and signal rotation. This scheme spreads the training sequence during a transmitted codeword and helps the estimator to track fast variation of the channel gains. A comprehensive comparison between the proposed training scheme and the conventional training scheme is then carried out, which reveals several interesting conclusions with respect to both error performance of the system and mean square error of the channel estimator. For multiple antenna systems, different schemes are examined in this thesis for the estimation of block-fading channels. For typical coded modulation systems with multiple antennas, the first scheme makes a distinction between the iteration in the channel estimation and the iteration in the decoding. Then, the estimator begins iteration when the soft output of the decoder at the decoding iteration meets some specified reliability conditions. This scheme guarantees the convergence of the iterative receiver with iterative channel estimator. To accelerate the convergence process and decrease the complexity of successive iterations, in the second scheme, the channel estimator estimates channel state information (CSI) at each iteration with a combination of the training sequence and soft information. For coded modulation systems with precoding technique, in which a precoder is used after the modulator, the training sequence and data symbols are combined using a linear precoder to decrease the required training overhead. The power allocation and the placement of the training sequence to be precoded are obtained based on a lower bound on the mean square error of the channel estimation. It is demonstrated that considerable performance improvement is possible when the training symbols are embedded within data symbols with an equi-spaced pattern. In the last scheme, a joint precoder and training sequence is developed to maximize the achievable coding gain and diversity order under imperfect CSI. In particular, both the asymptotic performance behavior of the system with the precoded training scheme under imperfect CSI and the mean square error of the channel estimation are derived to obtain achievable diversity order and coding gain. Simulation results demonstrate that the joint optimized scheme outperforms the existing training schemes for systems with given precoders in terms of error rate and the amount of training overhead

Synchronisation in sampled receivers for narrowband digital modulation schemes.

SIGLEAvailable from British Library Document Supply Centre- DSC:DXN0033576 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

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

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Doppler Spread Estimation in MIMO Frequency-Selective Fading Channels

One of the main challenges in high-speed mobile communications is the presence of large Doppler spreads. Thus, accurate estimation of maximum Doppler spread (MDS) plays an important role in improving the performance of the communication link. In this paper, we derive the data-aided (DA) and non-data-aided (NDA) Cramér-Rao lower bounds (CRLBs) and maximum likelihood estimators (MLEs) for the MDS in multiple-input multiple-output (MIMO) frequency-selective fading channel. Moreover, a low-complexity NDA-moment-based estimator (MBE) is proposed. The proposed NDA-MBE relies on the second- and fourth-order moments of the received signal, which are employed to estimate the normalized squared autocorrelation function of the fading channel. Then, the problem of MDS estimation is formulated as a non-linear regression problem, and the least-squares curve-fitting optimization technique is applied to determine the estimate of the MDS. This is the first time in the literature, when DA- and NDA-MDS estimation is investigated for MIMO frequency-selective fading channel. Simulation results show that there is no significant performance gap between the derived NDA-MLE and NDA-CRLB, even when the observation window is relatively small. Furthermore, the significant reduced-complexity in the NDA-MBE leads to low root-mean-square error over a wide range of MDSs, when the observation window is selected large enough