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|Title:||Equalization techniques for high order qam signals||Authors:||Kashyap, Rajat.||Keywords:||DRNTU::Engineering||Issue Date:||2013||Abstract:||Equalization techniques counterbalance the distortion and additive white Gaussian noise (AWGN) introduced by communication channels which are not known beforehand to reduce the inter-symbol interference (lSI),. Since the channel is unknown a priori, adaptive algorithms are used for this purpose. Broadly categorized, there are two methods by which adaptive algorithms approximate the inverse of the channel impulse response, (i) by using a training sequence (ii) without using a training sequence. Conventional adaptive algorithms require a properly synchronized (at both transmitter and receiver) training sequence which makes it possible to adjust the equalizer coefficients according to the employed algorithm, thereby minimizing the mean square error. This type of equalization is called Non-Blind equalization. However, there are many situations which require equalization without the use of a training sequence. One example of such a situation is multipoint data networks. For this purpose, a Blind equalizer has to be built into the receiver design. Blind equalizers estimate the transmitted signal and the channel parameters without using a known training sequence. Minimization of a class of non-convex cost functions is used as a criterion for adaptation. By these cost functions, lSI is characterized independently of the data symbol constellation and of the carrier phase used in the transmission system . In this project, three blind equalization algorithms (Constant Modulus Algorithm (CMA), Modified Constant Modulus Algorithm (MCMA) and Variable Step-size Modified Constant Modulus Algorithm (VSS-MCMA)) were studied mathematically and their performance is shown for high order QAM (Quadrature Amplitude Modulation) signals by simulations in this dissertation. These algorithms were then implemented with a decision feedback equalizer to further improve the symbol error rate (SER) performance. The following essential findings were made: 1) Blind algorithms are robust with respect to distortions and, by using appropriate step-size and reference gain parameters we can ensure convergence to optimal gains.2) Since CMA is phase-blind in nature, it cannot correct the phase errors. Whereas, simulations show that MCMA corrects the phase error and results in a lower MSE as well. 3) In VSS-MCMA, by using different step-size parameters we can ensure faster convergence and lower MSE. 4) CMA with DFE provides better SER than CMA with linear equalizers. SER is further improved by implementing a decision feedback equalizer (DFE) using MCMA and VSS-MCMA.||URI:||http://hdl.handle.net/10356/54905||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
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