Cyclostationarity Of Signals Research Articles

Simple and practical cyclostationary beamforming algorithms

Inspired by the asymmetric principal component analysis (APCA) neural model and based on the signal cyclostationarity, the authors propose a simple gradient-descent beamforming (GDB) algorithm. The GDB algorithm is an adaptive blind beamforming algorithm and can be used to extract signals with cyclostationarity under a complex signal environment. Although the GDB algorithm suffers from slow convergence, it has a low computational complexity. Two more algorithms, called the boosted GDB and the beta GDB algorithms have been defined based on the GDB algorithm. All the algorithms have been simulated and compared. The boosted GDB and beta GDB algorithms are shown capable of providing fast convergence and satisfactory signal-to-interference-and-noise ratio (SINR) performance, and can be used for implementation in real-time systems.

Robust technique for estimating the bearings of cyclostationary signals

This paper deals with the problem of direction finding using cyclostationary signals in the presence of cycle frequency error (CFE). By exploiting the signal cyclostationarity, cyclic MUSIC and conjugate cyclic MUSIC presented in Gardner (Cyclostationarity in Communications and Signal Processing, New York, 1994; Proc. IEEE 76 (1988) 845) have been shown to be effective for signal-selective direction finding. However, they suffer from severe performance degradation even if there is a small error in the cycle frequency of the signals of interest. In this paper, we first evaluate the performance of conjugate cyclic MUSIC when CFE exists. An analytical formula is derived to show the behavior of the performance degradation due to CFE. By exploiting the eigenstructure of the conjugate cyclic correlation matrix of the received array data, we develop a technique including a method for estimating the cycle frequency of the SOI and an efficient method in conjunction with a subspace-fitting algorithm to achieve robust direction finding against CFE. Several simulation examples confirming the theoretical analysis and showing the effectiveness of the proposed technique are also presented.

Direct blind channel equalization via the programmable canonical correlation analysis

This paper concerns direct blind channel equalization using the programmable canonical correlation analysis (PCCA). The PCCA-based equalizer calculates the weight vector for spatial filter based on signal cyclostationarity. The design problem of the PCCA is to solve an eigenproblem and find the first dominant eigenvector as the weight vector. In this paper, we investigate the maximum eigenvalue and express it as a function of two output signal-to-interference plus noise ratios. We also study the effect of small channel coefficients on the maximum eigenvalue and present a detection method for the PCCA to guarantee the equalization performance better than a specified performance requirement. Finally, several simulation examples are provided to show the effectiveness of the proposed approach.

Blind estimation of symbol timing and carrier frequency offset in wireless OFDM systems

Orthogonal frequency-division multiplexing (OFDM) systems are highly sensitive to synchronization errors. We introduce an algorithm for the blind estimation of symbol timing and carrier frequency offset in wireless OFDM systems. The proposed estimator is an extension of the Gini-Giannakis (see IEEE Trans. Commun., vol.46, p.400-411, 1998) estimator for single-carrier systems. It exploits the cyclostationarity of OFDM signals and relies on second-order statistics only. Our method can be applied to pulse shaping OFDM systems with arbitrary time-frequency guard regions, OFDM based on offset quadrature amplitude modulation, and biorthogonal frequency-division multiplexing systems. We furthermore propose the use of different subcarrier transmit powers (subcarrier weighting) and periodic transmitter precoding to achieve a carrier frequency acquisition range of the entire bandwidth of the OFDM signal, and a symbol timing acquisition range of arbitrary length. Finally, we provide simulation results demonstrating the performance of the new estimator.

Direction-finding methods for cyclostationary signals in the presence of coherent sources

By exploiting the signal cyclostationarity, conjugate cyclic MUSIC presented by Gardner (1994), has been shown to be effective in performing signal-selective direction finding. However, a direct application of conjugate cyclic MUSIC in the presence of coherent signals of interest (SOIs) will result in ambiguity. To tackle the drawback of conjugate cyclic MUSIC, we present a scheme called the Hankel approximation method (HAM) in conjunction with conjugate cyclic MUSIC to cope with the performance deterioration due to coherent SOIs for a uniform linear array. Theoretical analysis shows that at least 2K sensor elements are required to resolve K coherent SOIs. Making use of the forward/backward (F/B) technique presented by Pillai and Kwon (1989), F/B HAM is further developed, which requires [3K/2] sensor elements to decorrelate K coherent SOIs, where [x] represents the integer part of x. Several simulation examples are also presented for illustrating the effectiveness of the proposed methods.

Efficient robust adaptive beamforming for cyclostationary signals

This paper deals with the problem of robust adaptive array beamforming for cyclostationary signals. By exploiting the signal cyclostationarity, the LS-SCORE algorithm presented in a paper by Agee et al. (1990) has been shown to be effective in performing adaptive beamforming without requiring the direction vector of the desired signal. However, this algorithm suffers from severe performance degradation even if there is a small mismatch in the cycle frequency of the desired signal. In this paper, we first evaluate the performance of the LS-SCORE algorithm in the presence of cycle frequency error (CFE). An analytical formula is derived to show the behavior of the performance degradation due to CFE. An efficient method is then developed based on the fact that the array output power approaches its maximum as the CFE is reduced. We formulate the problem as an optimization problem for reducing the CFE iteratively to achieve robust adaptive beamforming against the CFE. Simulation examples for confirming the theoretical analysis and showing the effectiveness of the proposed method are provided.

Robust adaptive array beamforming for cyclostationary signals under cycle frequency error

This paper deals with the problem of robust adaptive array beamforming for cyclostationary signals. By exploiting the signal cyclostationarity, the SCORE algorithms presented by Agee, Schell and Gardner (1990) have been shown to be effective in performing adaptive beamforming without requiring the direction vector of the desired signal. However, these algorithms suffer from severe performance degradation even if there is a small mismatch in the cycle frequency of the desired signal. In this paper, we first evaluate the performance of the SCORE algorithms in the presence of cycle frequency error (CFE). An analytical formula is derived to show the behavior of the performance degradation due to CFE. Based on the theoretical result, we then develop an efficient method in conjunction with the SCORE algorithms to achieve robust adaptive beamforming against the CFE. Several simulation examples for confirming the theoretical analysis and showing the effectiveness of the proposed method are also presented.

Adaptive array beamforming for cyclostationary signals

This paper concerns the adaptive array beamforming using signal cyclostationarity. We present recursive algorithms for implementing the self-cohesive restoral (SCORE) approaches which were originally developed by Agee, Schell and Gardner (1990) to perform blind adaptive signal extraction using antenna arrays. Utilizing the theory of matrix factorization in conjunction with a power normalization scheme, we first reformulate the original problems considered by the SCORE approaches as simpler constrained optimization problems. By employing the modular Gram-Schmidt orthogonalization (GSO) structure, adaptive algorithms are then developed for finding the optimal weight vector. It is shown that although the GSO structure used requires a computation load O(N/sup 2/) for an adaptive-beamformer with N elements, the computing time required for producing an output data vector is reduced to O(N) due to the pipeline implementation suitable for the GSO process. The convergence property for each of the proposed algorithms is considered. A simple and efficient method is proposed for choosing appropriate initial guesses to initiate the adaptive algorithms. Simulation examples are given to show the effectiveness of the proposed algorithms.