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 […]
