For that reason, an equalizer needs to automatically adjust its coefficients in response to the channel variations. We saw in detail how time variations in the channel unfold on the scale of the coherence time and how this impacts the Rx signal. Time variationĮven when the wireless channel is known to a reasonably accurate level, it eventually changes after some time. As the wireless channel deteriorates, so does the reliability on its estimate. In any case, the quality of the channel estimation is only as good as the channel itself. While this information, commonly known as Channel State Information (CSI), can be gained from a training sequence embedded in the Rx signal, the channel characteristics are unknown in many other situations. In developing the coefficients for an equalizer, we usually assume that perfect channel information is available at the Rx. MotivationĪ few reasons for an adaptive equalizer are as follows. Here, we start with the motivation to develop an automatic equalizer with self-adjusting taps. A conceptual block diagram of the equalization process is shown in the figure below where the composite channel includes the effects of Tx/Rx filters and the multipath.Ī classification of equalization algorithms was described in an earlier article. In essence, the output of an equalizer should be a Nyquist pulse for a single symbol case. Equalization refers to any signal processing technique in general and filtering in particular that is designed to eliminate or reduce this ISI before symbol detection. The wireless channel is a source of severe distortion in the received (Rx) signal and our main task is to remove the resulting Inter-Symbol Interference (ISI) from the Rx samples. An LMS equalizer in communication system design is just one of those beautiful examples and its other applications include noise and echo cancellation, beamforming, neural networks and so on. Due to its simplicity and robustness, it has been the most widely used adaptive filtering algorithm in real applications. The LMS algorithm was first proposed by Bernard Widrow (a professor at Stanford University) and his PhD student Ted Hoff (the architect of the first microprocessor) in the 1960s.
0 Comments
Leave a Reply. |