Robert J. Inkol

On the Cyclostationarity of OFDM and Single Carrier Linearly Digitally Modulated Signals in Time Dispersive Channels: Theoretical Developments and Application

Previous studies on the cyclostationarity aspect of orthogonal frequency division multiplexing (OFDM) and single carrier linearly digitally modulated (SCLD) signals assumed simplified signal and channel models or considered only second-order cyclostationarity. This paper presents new results concerning the cyclostationarity of these signals under more general conditions, including time dispersive channels, additive Gaussian noise, and carrier phase, frequency, and timing offsets. Analytical closed-form expressions are derived for time- and frequency-domain parameters of the cyclostationarity of OFDM and SCLD signals. In addition, a condition to eliminate aliasing in the cycle and spectral frequency domains is derived. Based on these results, an algorithm is developed for recognizing OFDM versus SCLD signals. This algorithm obviates the need for commonly required signal preprocessing tasks, such as signal and noise power estimation and the recovery of symbol timing and carrier information.

Classification of Space-Time Block Codes Based on Second-Order Cyclostationarity with Transmission Impairments

Signal classification is important in various commercial and military applications. Multiple antenna systems complicate the signal classification problem since there is now the issue of estimating the number and configuration of transmit antennas. The novel blind classification algorithm proposed in this paper exploits the cyclostationarity property of space-time block codes (STBCs) for the classification of multiple antenna systems in the presence of possible transmission impairments. Analytical expressions for the second-order cyclic statistics used as the basis of the algorithm are derived, and the computational cost of the proposed algorithm is considered. This algorithm avoids the need for a priori knowledge of the channel coefficients, modulation, carrier phase, and timing offsets. Moreover, it does not need accurate information about the transmission data rate and carrier frequency offset. Monte Carlo simulation results demonstrate a good classification performance with low sensitivity to phase noise and channel effects, including frequency-selective fading and Doppler shift.

Cyclostationarity-Based Robust Algorithms for QAM Signal Identification

This letter proposes two novel algorithms for the identification of quadrature amplitude modulation (QAM) signals. The cyclostationarity-based features used by these algorithms are robust with respect to timing, phase, and frequency offsets, and phase noise. Based on theoretical analysis and simulations, the identification performance of the proposed algorithms compares favorably with that of alternative approaches.

Efficient approximations for the arctangent function

1053-5888/06/

Cyclostationarity-based Algorithm for Blind Recognition of OFDM and Single Carrier Linear Digital Modulations

20.00©2006IEEE T his article provides several efficient approximations for the arctangent function using Lagrange interpolation and minimax optimization techniques. These approximations are particularly useful when processing power, memory, and power consumption are important issues. In addition to comparing the errors and the computational workload of these approximations, we also extend them to all four quadrants.

Fourth-Order Statistics for Blind Classification of Spatial Multiplexing and Alamouti Space-Time Block Code Signals

The paper studies the cyclostationarity of an orthogonal frequency division multiplexing (OFDM) with a view to recognizing OFDM against single carrier linear digital (SCLD) modulations. The analytical expressions for the nth-order cyclic cumulants (CCs) and cycle frequencies of an OFDM signal embedded in additive white Gaussian noise and subject to phase, frequency and timing offsets are derived An algorithm based on a second-order CC is proposed to recognize OFDM against SCLD modulations. The recognition algorithm of the authors obviates the need for preprocessing tasks, such as symbol timing estimation, carrier and waveform recovery, and signal and noise power estimation. The results of simulation experiments confirm the theoretical analysis.

Joint signal detection and classification based on first-order cyclostationarity for cognitive radios

Blind signal classification, a major task of intelligent receivers, has important civilian and military applications. This problem becomes more challenging in multi-antenna scenarios due to the diverse transmission schemes that can be employed, e.g., spatial multiplexing (SM) and space-time block codes (STBCs). This paper presents a class of novel algorithms for blind classification of SM and Alamouti STBC (AL-STBC) transmissions. Unlike the prior art, we show that signal classification can be performed using a single receive antenna by taking advantage of the space-time redundancy. The first proposed algorithm relies on the fourth-order moment as a discriminating feature and employs the likelihood ratio test for achieving maximum average probability of correct classification. This requires knowledge of the channel coefficients, modulation type, and noise power. To avoid this drawback, three algorithms have been further developed. Their common idea is that the discrete Fourier transform of the fourth-order lag product exhibits peaks at certain frequencies for the AL-STBC signals, but not for the SM signals, and thus, provides the basis of a useful discriminating feature for signal classification. The effectiveness of these algorithms has been demonstrated in extensive simulation experiments, where a Nakagami-m fading channel and the presence of timing and frequency offsets are assumed.

Hybrid RSS/AOA emitter location estimation based on least squares and maximum likelihood criteria

The sensing of the radio frequency environment has important commercial and military applications and is fundamental to the concept of cognitive radio. The detection and classification of low signal-to-noise ratio signals with relaxed a priori information on their parameters are essential prerequisites to the demodulation of an intercepted signal. This paper proposes an algorithm based on first-order cyclostationarity for the joint detection and classification of frequency shift keying (FSK) and amplitude-modulated (AM) signals. A theoretical analysis of the algorithm performance is also presented and the results compared against a performance benchmark based on the use of limited assumed a priori information on signal parameters at the receive-side. The proposed algorithm has the advantage that it avoids the need for carrier and timing recovery and the estimation of signal and noise powers.

Second-Order Cyclostationarity of BT-SCLD Signals: Theoretical Developments and Applications to Signal Classification and Blind Parameter Estimation

Linear least squares (LS) and maximum likelihood (ML) estimators are derived for emitter geolocation using both received signal strength (RSS) and angle of arrival (AOA) information obtained from an heterogeneous sensor array. The results of simulation experiments provide useful insights into the behavior of these hybrid approaches and demonstrate that the use of simple RSS sensors to augment traditional AOA sensors can significantly improve the attainable geolocation accuracy.

Second-Order Cyclostationarity of Cyclically Prefixed Single Carrier Linear Digital Modulations with Applications to Signal Recognition

This paper investigates the second-order cyclostationarity of block transmitted-single carrier linearly digitally modulated (BT-SCLD) signals, and its applications to signal classification and blind (non-data aided) parameter estimation. Analytical closed-form expressions are derived for the cyclic autocorrelation function (CAF), cyclic spectrum (CS), complementary CAF (CCAF), complementary CS (CCS), and corresponding cycle frequencies (CFs). Furthermore, the conditions for avoiding aliasing in the cycle and spectral frequency domains are obtained. Based on these findings, we propose algorithms for classifying BT-SCLD, orthogonal frequency division multiplexing (OFDM), and SCLD signals, and for the blind estimation of the BT-SCLD block transmission parameters. Simulation and laboratory experiments demonstrate the effectiveness of the proposed algorithms under low signal-to-noise ratios (SNRs), short sensing times, and various channel conditions. Furthermore, these algorithms have the advantage of not requiring the recovery of carrier, waveform, and symbol timing information, or the estimation of signal and noise powers.