[SOLVED] Hard Vs LLR demodulation of QAM symbols

[hunter555persia]

Hi, we know that optimum demodulation of QAM symbols is based on decision regions obtained from MAP rule: argmax(p(Sm|r)) where Sm is any possible sent symbol and r is the received symbol.

Now what is the difference between hard and LLR demodulation ?? Which one is based on above ??
Thanks in advance.

 

[David83]

As far as I know, the above rule is called hard decision detection (decoding), because the output of the detector is a valid symbol, which is the final decision. In soft decision, usually a two parallel (or serial) convolution codes are concatenated in a certain way to make these codewords look like independent. At the receiver there are two serial soft-input soft output Veterbi algorithm detectors the exchange soft information between each other, until a saturation point reached. This is called soft decoding and its performance is much better than hard decoding. Turbo codes class is an example of this.

 

[hunter555persia]

This is independent of coding. what is the difference between hard and LLR demodulation of QAM symbols ?? Which one is based on MAP rule ? Assuming equiprobable symbols MAP rule reduces to finding the minimum Euclidean distance.

 

[hunter555persia]

I found the answer. Hard decoding is based on minimum Euclidean distance. This gives the optimum estimate of SYMBOLS. But in LLR demodulation BITS are estimated NOT symbols. LLR gives a reliability measure for each estimated bit. The bigger this number, the more precise the estimate. One major use of this is in decoding of M-ary modulated convolutional codes where by assignment of reference reliability factors to 1 and 0 of trellis branches, the metrics can be calculated.