摘要:
Using a known or later developed time domain equalizer coefficient training algorithm, a least square solution for the time domain equalizer coefficients is taken at a starting point and iteratively improved on. In particular, the improvement is directed towards maximizing number of bits per frame loaded over the time domain equalizer coefficient choice. This can be accomplished by maximizing capacity directly rather than setting a goal to shorten the channel and hoping that the capacity will be maximized as a result.
摘要:
Using a known or later developed time domain equalizer coefficient training algorithm, a least square solution for the time domain equalizer coefficients is taken at a starting point and iteratively improved on. In particular, the improvement is directed towards maximizing number of bits per frame loaded over the time domain equalizer coefficient choice. This can be accomplished by maximizing capacity directly rather than setting a goal to shorten the channel and hoping that the capacity will be maximized as a result.
摘要:
Using a known or later developed time domain equalizer coefficient training algorithm, a least square solution for the time domain equalizer coefficients is taken at a starting point and iteratively improved on. In particular, the improvement is directed towards maximizing number of bits per frame loaded over the time domain equalizer coefficient choice. This can be accomplished by maximizing capacity directly rather than setting a goal to shorten the channel and hoping that the capacity will be maximized as a result.
摘要:
Using a known or later developed time domain equalizer coefficient training algorithm, a least square solution for the time domain equalizer coefficients is taken at a starting point and iteratively improved on. In particular, the improvement is directed towards maximizing number of bits per frame loaded over the time domain equalizer coefficient choice. This can be accomplished by maximizing capacity directly rather than setting a goal to shorten the channel and hoping that the capacity will be maximized as a result.
摘要:
Using a known or later developed time domain equalizer coefficient training algorithm, a least square solution for the time domain equalizer coefficients is taken at a starting point and iteratively improved on. In particular, the improvement is directed towards maximizing number of bits per frame loaded over the time domain equalizer coefficient choice. This can be accomplished by maximizing capacity directly rather than setting a goal to shorten the channel and hoping that the capacity will be maximized as a result.
摘要:
Using a known or later developed time domain equalizer coefficient training algorithm, a least square solution for the time domain equalizer coefficients is taken at a starting point and iteratively improved on. In particular, the improvement is directed towards maximizing number of bits per frame loaded over the time domain equalizer coefficient choice. This can be accomplished by maximizing capacity directly rather than setting a goal to shorten the channel and hoping that the capacity will be maximized as a result.
摘要:
Using a known or later developed time domain equalizer coefficient training algorithm, a least square solution for the time domain equalizer coefficients is taken at a starting point and iteratively improved on. In particular, the improvement is directed towards maximizing number of bits per frame loaded over the time domain equalizer coefficient choice. This can be accomplished by maximizing capacity directly rather than setting a goal to shorten the channel and hoping that the capacity will be maximized as a result.
摘要:
Using a known or later developed time domain equalizer coefficient training algorithm, a least square solution for the time domain equalizer coefficients is taken at a starting point and iteratively improved on. In particular, the improvement is directed towards maximizing number of bits per frame loaded over the time domain equalizer coefficient choice. This can be accomplished by maximizing capacity directly rather than setting a goal to shorten the channel and hoping that the capacity will be maximized as a result.
摘要:
Typical forward error correction methods employ Trellis Code Modulation. By substituting low density parity check coding in place of the convolution code as part of a combined modulation and encoding procedure, low density parity check coding and modulation can be performed. The low density parity check codes have no error floor, no cycles, an equal bit error rate for the information bits and the parity bits, and timely construction of both a parity check matrix with variable codeword size and a generator matrix is possible.
摘要:
Through the use of feedback in determining frequency domain equalization, interference can be reduced. Specifically, the determined constellation point closest to the determined received point can be fed back to aid in determining one or more other closest constellation points.