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EDIT for post #31: Channel causes different signal components to have different delays. So the LONG delay components of the current symbol are influenced by the SHORT delay components of the next symbol. :D
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...
Yes there is. LLR demodulation of QAM symbols. LLR demodulation is a bitwise demodulation method that gives you a reliability measure of bit estimates. By assigning a reference reliability to 0 and 1 branch metrics are calculated. Coding is identical to BPSK mode.
Signal space is discussed in chapter 2. Modulation in chapter 3. All the constellations that are sketched in a plane show the coefficients of 2 dimensions. Inphase component is sent with carrier cos(wt) and quadrature component is sent with carrier sin(wt). cos(wt) and sin(wt) are 2 basis...
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.
The baseband OFDM signal is complex, so Inphase and Quadrature components are recovered and sampled separately in receiver. In signal space concept, baseband complex signals are 2-dimensional, I and Q components are coefficients of each dimension.
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...
I got it. Fs = 1/Ts samples the received signal in both dimensions (Real and Imaginary) so the physical Fs is 2/Ts = 2W which equals the Nyquist rate. :D
Hi, when I run simulations in MATLAB my CPU meter never exceeds 30% so the simulation doesn't run at maximum speed. What do I do to make MATLAB use the CPU optimally ?
I'm running MATLAB in a laptop.
Thanks.
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