LTE ,VITERBI, LDPC IN MATLAB

Exercise
-implement an lte conjunctilar encoder at a rate of 1/3 and
constraint length K=7. For the given encoder, the tables
generator is: g1 = 171oct = [1 1 1 1 0 0 1], g2 = 133oct = [1 0 1 1 0 1
1], g3 = 165oct = [1 1 1 0 1 0 1]. Assume QPSK configuration and 243
QAM symbols per frame.
-For a given range of the ratio of average energy per bit to
the power spectral density of noise (Εb/No) [1,..,10] dB
assume the n=10000000 bits transmission from an awgn channel
communication with noiseVar = 10.^(-snrdB/10).
-On the receiver use Viterbi decoding with the following
Parameters:
• Decision type: Unquantized decision uses the Euclidean distance to
calculate the branch metrics.
• Traceback depth: 37
• Operation mode: Continuous
Questions
Compare bit error rate (ber) (with a related chart) for
the absence of codification by encoding;
described above for the entire range of values (Εb/No)
[1,..,10] dB .
Hint: You can use the functions: erfc, convenc,
qammod, awgn, qamdemod, vitdec of the communication toolbox of
Matlab

2
Exercise
Modify Exercise 1 so that the BER of the decoding
case and concalicative encoder to be compared also with
an ldpc codec with a 3/4 rate that you use in ieee
802.11 standard. Assume, most QPSK configuration and 243 QAM
symbols per frame.
Use the functions:
-For QPSK configuration/deformation:
• qpskmod = comm. PSKModulator(M,'BitInput',true);
• qpskdemod = comm. PSKDemodulator(M,'BitOutput',true, ...
'DecisionMethod','Approximate log-likelihood ratio', ...
'Variance',noiseVar);
-For LDPC encoding/decoding:
• ldpcEncode(), ldpcDecode();
Questions
-Write the comments on the results you observe:
1. for the comparison of conjunctural codes with codes
LDPC.
2. for the comparison of conjunctimal codes and
LDPC codes in relation to the results for their
bar codes block of the 1st task


INSTRUCTIONS
- Use the Matlab Trial and not the Octave, so as to
you can use all of the above functions.
- Create a file (.m) in Matlab for each of the two
Exercises.
- For each of the two exercises also create a document
documentation that you will describe the code, the output of the
your programs, as well as you will answer the questions that
are put in the above exercises.
- Your exercise will be delivered like a zip file (set to
includes type files (.m) and documentation) in
electronic form in eclass on the delivery date.
- The zip file will be named in the following way:
erg2_onoma_epwnymo_number registry.zip.
-For Exercise 2 create a sparse H(162×648) table
via base matrix P =
[16 17 22 24 9 3 14 -1 4 2 7 -1 26 -1 2 -1 21 -1 1 0 -1 -1 -1 -1
25 12 12 3 3 26 6 21 -1 15 22 -1 15 -1 4 -1 -1 16 -1 0 0 -1 -1 -1
25 18 26 16 22 23 9 -1 0 -1 4 -1 4 -1 8 23 11 -1 -1 -1 0 0 -1 -1
9 7 0 1 17 -1 -1 7 3 -1 3 23 -1 16 -1 -1 21 -1 0 -1 -1 0 0 -1
24 5 26 7 1 -1 -1 15 24 15 -1 8 -1 13 -1 13 -1 11 -1 -1 -1 -1 0 0
2 2 19 14 24 1 15 19 -1 21 -1 2 -1 24 -1 3 -1 2 1 -1 -1 -1 0 ];