Study On The Methods Of Flat Fast Fading Channels Prediction

The wireless mobile communication channel is very complex. It limits theperformance of the wireless communication system a lot. The multipath problembetween the transmitter and the receiver and the fast moving terminal which causethe Doppler affect result in the amplitude and phase of the receive signal varyfastly. We call it "feeding". It is the influence of the deep feeding that limit theperformance of the communication system very much. We need the bettermodulation, coding and power control methods to be used in the feeding channelefficiently. A.J. Glodsmith and S.G..Chua have investigated some new adaptivetransmitter technique, for example adaptive modulation, adaptive coding, adaptivepower control and adaptive transmitter antenna diversity and so on. Theseadaptive transmitter schemes all vary the constellation size, symbol rate, codingrate, transmitted power level weights of transmission antennas or any combinationof these parameters by instantaneously monitoring channel conditions. They aretrying to use both power and spectrum more efficiently without sacrificing the biterror rate performance to realize the higher information transmitter rate. To implement adaptive transmission methods in practice, the channel state(CSI) must be available at the transmitter. With the sampling data of the receiversamples in the past interval, we use the arithmetic of channel predictions topredict the fading channel coefficient. Afterwards, transmit the fading channelcoefficient to transmitter in the feedback channel. The transmitter will decide thetransmission power, modulation methods, coding methods and the transmissionantennas, to fit the transmission conditions of the time. Thus, transmitter isoptimized, and the communication performance is improved at the same time. The thesis first introduces the significance of this subject, development statusand some basic theory knowledge. The important part and mostly task of thisthesis are the modification of the current channel prediction arithmetic. It has twoparts: the first part is the improvement of basic ESPRIT arithmetic. It is a channelprediction method in common use. The second part is the investigation of theadaptive subspace tracing. These modified arithmetic is1. The C-ESPRIT arithmetic based on sampling data conjugation recomposition The basic idea is: the sampling data itself is used in the basic ESPRITarithmetic but the data are complex numbers is ignored. The conjugation data of 73ABSTRACTthe sampling is reused to compute the self-correlation matrix in C-ESPRITarithmetic. The veracity and precision of the arithmetic are improved and theoperation quantity does not changed much at the same time.2. The SS-ESPRIT and MSSESPRIT arithmetic based on data pretreatment The basic ESPRIT arithmetic is the best in the current prediction arithmetic(such as the Maximum Entropy Method (MEM) and the Long-range Predictionmethod (LRP)). Its' serious shortcoming is that the operation quantity is greatwhen the singular value decomposition (SVD) or eigenvalue decomposition iscomputed. It limits the real time application. To overcome the shortcoming of thearithmetic, the dimensions of singular value decomposition or eigenvaluedecomposition must be reduced. SS-ESPRIT and MSS-ESPRIT arithmetic aregiven. The SS-ESPRIT is formed by the basic ESPRIT method combined with thespace smooth techniques. The basic idea of the space smooth techniques is thatthe self-correlation matrix is disparted and assembled again, which is estimated bythe Rcc = CCH method. Then the eigenvalue decomposition is computed. It is ?apparent that the operation quantity is reduced by reducing the dimensions ofeigenvalue decomposition but the prediction precision is decreased. Well thevirtues of the two arithmetics are utilized by the MSS-ESPRIT arithmeticsubstantially. The sampling data conjugation recomposition and space smoothingtechniques are com...