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 ];