Inverse Model of Channel (Equalizer)
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Resource Overview
In channel equalization applications, the original signal distorted by the channel is used as the input to an adaptive filter, with the desired signal being a time-delayed version of the original signal, as shown in Figure 22(a). Typically, the time-delayed version of the input signal is available at the receiver end in the form of a standard training signal. When the Mean Square Error (MSE) is minimized, it indicates that the adaptive filter has successfully represented the inverse model of the channel (equalizer). Implementation typically involves configuring the adaptive filter with appropriate tap weights and using algorithms like LMS or RLS for iterative optimization.
Detailed Documentation
In channel equalization applications, the original signal affected by channel distortion is used as the input to an adaptive filter. The objective is to obtain a time-delayed version of the original signal, as illustrated in Figure 22(a). Typically, the time-delayed version of the input signal can be obtained at the receiver using a standard training signal. When the Mean Square Error (MSE) reaches its minimum value, it indicates that the adaptive filter successfully represents the inverse model of the channel, i.e., the equalizer. This process commonly involves implementing adaptive algorithms such as Least Mean Squares (LMS) or Recursive Least Squares (RLS), where filter coefficients are updated iteratively to minimize the error between the filter output and the desired time-delayed signal.
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