All-Digital Receiving Technology Research Based On Cyclostationary Theory

All digital receiving based on open-loop structure is a new signal processing technology in many problems of interest in signal processing and communications, which providing the perfective performance, including stability and compatibility, in the mean time simplifying the receiving structure. In almost all classical papers, stochastic processes, passed through the system which modeled as LTI or LTV, are usually described as an ensemble of impulse-response functions, or products of time-delayed or frequency-shifted versions of them. Thus, neither output processing statistical functions nor the system functions would be estimated by a single sample path. An alternative signal analysis framework that does not model signals as stochastic processes is the fraction-of-time (FOT) probability approach, where signals are modeled as single functions of time (time-series) rather than sample paths of stochastic processes. A further development in the FOT probability theory for nonstationary signals was refer to a class of nonstationary time-series wider than the class of ACS time-series, namely, the class of the generalized almost-cyclostationary (GACS) time-series. Signals belonging to this class exhibit multivariate statistical functions that are almost-periodic functions of time whose Fourier series expansions present coefficients and frequencies that can depend on the lag shifts of the signals. Thus, in the FOT probability approach, statistical parameters are defined through infinite-time averages of a single time-series or the products of time and frequency-shifted versions of this time-series, rather than ensemble averages of a stochastic process. In the present paper, the concept and basic theory of GACS is introduced in the FOT probability framework along with the development of non-stationary processes. Those include FOT probability, the statistical characterization of k-order statistics of the nonstationary processes, etc. Moreover, in the FOT probability framework, the kind of convergence of the estimators to be considered as the data-record length approaches infinity is the convergence of the function sequence of the finite-time averages. And also the problem of finite Cyclic frequency set is concluded when it is finite power sequence. The most results of FOT probability of GACS signal are discussed that GACS can be explained as the Fourier transmission and the spectral relation between different lag products, although stochastic systems transform ergodic input signals into nonergodic output signals. In the present paper, Doppler frequency is encountered very often in mobile communication problems. It is shown that multipath Doppler channels can be modeled as extended multipath Doppler channel model, which is FOT deterministic LTV systems, depending on the length of the data-record adopted to observe the input and output signals and the bandwidth of the input signal. Under the assumption of multiple slow fluctuating, it is shown that the FOT probability approach and extended multipath Doppler channel model allow one to characterize the radio channel, and they generate at the output several replicas of the input signal, each characterized by a different complex amplitude, delay, time scaling factor, and Doppler shift. It is well known that deep fades in signal power due to multipath radio propagation severely degrade the performance of mobile radio systems and impose high power requirements. In the present paper, a novel algorithm for long ranger prediction of fading channel is introduced, according to the extended Doppler channel model in the FOT probability presented in this paper. In this paper, it is shown that the superior performance of this algorithm is due to its modified iterative error estimation when MMSE estimate of the future fading coefficients given a fixed number of previous observations. We also enhanced the estimating performance by considering the similar cyclostation between samples instead of stationary in the classic paper. Moreover, the more extensive problems encountered in communication and signal processing on signal frequency, phase estimating and frame synchronization, are discussedby the FOT channel model presented in this paper. Sinusoidal signal identification and frequency estimating are the important problems in communication systems, especially there existing strongly interference sources nearby. For the cyclostationary of receiving signal and different cyclic frequency between signal and interference, a linear estimating algorithm based on polynomial expression is presented in this paper in order to identify the signal and decrease estimating time. It is shown by the simulation that this presented algorithm is effective and the frequency can be estimating by the envelope slope of second-order relationship. And also the estimating process is blind by the extended channel model, that is, operate without data-aided sequence. On the basis of GACS of multiple receiving array signal, second-order array can be decomposed to two parts: FOT cyclostationary array and FOT random array. By the FOT cyclostationary array, a union frequency and phase estimating algorithm is presented in this paper, Cyclostationary signal parameter algorithm ensures the depression of noise, and the open-loop structure of the presented algorithm fast the speed of parameter estimating effectively. Frame structure is a standard one in most data transmission protocols. In the FOT probability, a cyclic frequency can be extracted according to the starting flag or end flag of the frame, and the BER of the channel can also be estimated by the errors of flag bytes. Under the correct estimating of BER, unequal protecting algorithm is introduced to the frame synchronizing procession in this paper. It is shown that synchronizing algorithm with variable protecting coefficients can improve the performance, by extended the synchronizing time and also shorten the time of data synchronization. The presented algorithm in this paper has been applied effectively in video transmission system.