Cyclic Spectral Analysis Research Articles

Cyclic spectral analysis of OFDM/QAM modulation using stochastic matrix-based method

The cyclostationarity of the OFDM/QAM signals induced by the use of cyclic prefix or pulse shaping is the key to blind synchronization, channel identification and equalization in these systems. Therefore, continuous-time and discrete-time spectral correlation characterization of the pulse shaping OFDM/QAM signals is performed by a matrix-based stochastic method based on Markov chain signal representation. Exact analytical expression for the cyclic spectrum of the continuous-time OFDM/QAM signal is derived applying the proposed method, the characteristic cyclic features according to the cyclic prefix and/or pulse shaping employing are analyzed and some graphed results are presented.

Identification of closed-loop linear systems via cyclic spectral analysis given noisy input-output time-domain data

The problem of closed-loop system identification given noisy time-domain input-output measurements is considered. It is assumed that the various disturbances affecting the system are zero-mean stationary whereas the closed-loop system operates under an external cyclostationary input which is not measured. Noisy measurements of the (direct) input and output of the plant are assumed to be available. The closed-loop system must be stable, but it is allowed to be unstable in open loop. No knowledge about the linear-feedback mechanism is assumed. Two identification algorithms are investigated using cyclic spectral analysis of noisy input-output data. For both approaches, the open-loop transfer function is first estimated using the cyclic spectrum and cyclic cross-spectrum of the input-output data. These transfer function estimates are then used as data for the proposed algorithms. Both classes of parameter estimators are shown to be weakly consistent in any stationary noise (both at input as well as output). Asymptotic performance analysis of the proposed parameter estimators is also provided. Computer simulation examples are presented in support of the proposed approaches.

Cyclic spectral analysis of continuous-phase modulated signals

Continuous-phase modulated (CPM) signals play a prominent role in modern communication systems due to their desirable constant-modulus property and the ability to control their power and bandwidth efficiencies. Popular CPM signals include the classical minimum-shift keyed (MSK) signal, the LREC family of signals also known as continuous-phase frequency-shift-keyed (CPFSK) signals, and Gaussian MSK, which is used in state-of-the-art GSM and PCS mobile communication systems. CPM signals, like virtually all man-made communication signals, are known to exhibit cyclostationarity, which implies that their probabilistic parameters, such as mean, second moment, and higher order cumulants, are almost-periodic functions of time. A novel representation of CPM signals as a sum of PAM signals is presented for both integer and noninteger modulation index cases. Then, the Nth-order cyclostationarity properties of binary CPM signals are derived in terms of Nth-order temporal and spectral moment and cumulant functions. Moreover, the case of M-ary CPM signals is briefly addressed. The results are illustrated with simulations involving MSK, LREC, and GMSK signals.

Applications of the cyclic spectral analysis to the surface temperature fluctuations in a stochastic climate model and a GCM simulation

Abstract In this paper, we introduce the cyclostationary processes into climate analysis and undertake a systematic study of the cyclic spectra of surface temperature fluctuations. The technique is adapted from cyclostationarity theory in signal processing. To demonstrate the usefulness of this technique, a very simple cyclostationary stochastic climate model is constructed. Our results show that the seasonal cycle strongly modulates the amplitudes of the covariance and the spectrum. The technique was also applied to the surface temperature fluctuations in a fifteen‐year seasonal run of the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM2, R15) using a zonally symmetric all‐land surface as the lower boundary. The results indicate that intraseasonal oscillations localized according to time of year are still present even after the surface temperature fields have been normalized using the commonly used procedure. Both examples suggest that the “annual cycle” cannot be “removed” b...

Search-efficient methods of detection of cyclostationary signals

Conventional signal processing methods that exploit cyclostationarity for the detection of weak signals in noise require fine resolution in cycle frequency for long integration time. Hence, in cases of weak-signal detection and broadband search, problems in implementation, such as excessive computational complexity and storage and search arise. This paper introduces two new search-efficient methods of cycle detection, namely the autocorrelated cyclic autocorrelation (ACA) and the autocorrelated cyclic periodogram (ACP) methods. For a given level of performance reliability, the ACA and ACP methods allow much larger resolution width in cycle frequency to be used in their implementations, compared to the conventional methods of cyclic spectral analysis. Thus, the amount of storage and search can be substantially reduced. Analyses of the two methods, performance comparison, and computer simulation results are presented.

Cyclic Spectral Analysis of Fluctuations in a GCM Simulation

Due to the variety of periodic or quasi-periodic deterministic forcings (e.g., diurnal cycle, seasonal cycle, Milankovitch cycles, etc.), most climate fluctuations may be modeled as cyclostationary processes since their properties are modulated by these cycles. Difficulties in using conventional spectral analysis to explore the seasonal variation of climate fluctuations have indicated the need for some new statistical techniques. It is suggested here that the cyclic spectral analysis he used for interpreting such fluctuations. The technique is adapted from cyclostationarity theory in signal processing. To demonstrate the usefulness of this technique, a very simple cyclostationarity stochastic climate model is constructed. The results show that the seasonal cycle strongly modulates the amplitude of the covariance and spectrum. The seasonal variation of intraseasonal oscillations in the Tropics has also been studied on a zonally symmetric all-land planet in the absence of external forcing. The idealized planet has no ocean no topography. A 15-year length seasonal run of the atmosphere is analyzed with the NCAR Community Climate Model (CCM2, R15). Analysis of the simulation data indicates the presence of intraseaonal oscillations in the Tropics, which are also localized in the time of year. Both examples suggest that these techniques might be useful for analysis of fluctuations that exhibit locality in both frequency and season.

Parallel computation structures for a class of cyclic spectral analysis algorithms

A digital frequency smoothing algorithm for cyclic spectral analysis is described. Partitioning schemes are presented that have properties amenable to parallel implementation. Parallel computation structures, based on the partitioning schemes, are described and system architectures proposed.

Estimating spectral correlations with simple nonlinear transformations

Two computationally efficient cyclic spectral analysis algorithms are compared using a computer simulation. Each algorithm replaces multiplications with sign changes and multiplexing operations. Compared with conventional frequency smoothing estimators, the quadruplex and one bit spectral correlation algorithms suffered losses in output signal to noise ratio of approximately 0.6 dB and 1.9 dB, respectively.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Computationally efficient algorithms for cyclic spectral analysis

Two computationally efficient algorithms for digital cyclic spectral analysis, the FFT accumulation method (FAM) and the strip spectral correlation algorithm (SSCA), are developed from a series of modifications on a simple time smoothing algorithm. The signal processing, computational, and structural attributes of time smoothing algorithms are presented with emphasis on the FAM and SSCA. As a vehicle for examining the algorithms the problem of estimating the cyclic cross spectrum of two complex-valued sequences is considered. Simplifications of the resulting expressions to special cases of the cross cyclic spectrum of two complex-valued sequences, such as the cyclic spectrum of a single real-valued sequence, are easily found. Computational and structural simplifications arising from the specialization are described. >