Research
From Forward Prediction Error and Backward Prediction Error to Orthogonal Data in Space (Lattice Predictor) and the Origin of a System to Pick up Another
In this paper, we will develop another class of linear filter which involve order update and time update. These filters have the important fact of order update. We will show a computationally efficient modular lattice-like architecture. This lead to a filter with computational complexity linear with the order which is the length. The design of order recursive adaptive filter can take two approaches. 1. Stochastic [16] gradient approach. This is Wiener theory. 2. Least squares approach. This is Kalman filter theory. The second approach is code demanding. We will start with the first approach.
An Adaptive Filter to Pick up a Wiener Filter from the Error using MSE with and Without Noise
n this paper we explain the suboptimum channel equalization approach. This approach employ linear transversal filter that we will explain. This filter structure has computational complexity that is linear with the order. This filter is shown in figure 1. Its input is the sequence v k, its output is the estimate of the output sequence Ik. The estimate might be expressed as The estimate I k is quantized to the nearest information symbol. Considerabl e research have been done to optimize the filter coefficients ck. A measure of performance for digital communication system is the average probability of error. In this system this is highly non linear function of ck. As we can see this method is computationally complex.
Adaptive Filters
We know the optimum and suboptimum receiver for ISI in the transmission through band-limited non ideal channels. The optimum employ maximum likelihood detection. The sub optimum employ linear equalizer. In our design of the equalizer we assume that we know at the receiver the impulse response of the channel or the frequency response. In most […]
Generation of any PDF from a Set of Equally likely Random Variables
Computer quantization is important to consider in digital signal processing because it limits the accuracy of signals to be processed. In this paper we will talk about the quantization effect on system performance and use the result to make an improvement in the signal and systems field. Computers communicate with ones and zeros back and forth. The ones and zeros make a word that a computer send to another. Each character of the word is a bit and the word has eight bits. The word can be called a byte. One byte can have 256 different words. In general if we have eight bits register in a computer the dynamic rang of numbers are quantized to 256 levels. This may result in error because the number we want to process may not fall exactly in its level. The accuracy depend on the computer and the number of bits on a register. In this paper we want to use A/D quantization error .
