determine what represents the most likely received symbol often varying on what is known about the symbols for example if it is more likely that a particular symbol is sent then incorporating this knowledge into the decision algorithm provides an advantage over an algorithm which supposes that all symbols are sent equally often to further illustrate in the fdd context the decoder can operate like 32 matched filters with one filter for each symbol wherein each symbol has a particular waveform chip bit sequence each matched filter correlates the received waveform with the waveform corresponding to a particular symbol the correlation outputs from each of the 32 matched filters are basically a peak corresponding to energy a large peak means that u201c very likely this was the symbol sent u201d wherein a code word is equivalent to a chip sequence and a small correlation peak means that u201c unlikely this was the right symbol u201d then the largest peak out of the 32 obtained peaks is selected and is determined to be the symbol that was sent because this is a statistical hypothesis test on average the determined symbol is the best decision that can be made an example of this process is shown in fig3 the decoding process in the base station converts a sequence of received channel bits into soft decision metrics for every possible one out of m cqi symbols the cqi quality detector can be implemented in a single integrated circuit or as discrete components in general the information bit sequence the cqi word is n bits long the cqi word is encoded to a n n reed.