Using MATLAB to plot a communication system using PAM binary

Using MATLAB to plot a communication system using PAM (binary) through AWGN discrete-time channel . PLZ show your code and plot.

Solution

AWGN discrete-time channel :

Syntax:

berub = bercoding(EbNo,\'conv\',decision,coderate,dspec)

berub = bercoding(EbNo,\'block\',\'hard\',n,k,dmin)

berub = bercoding(EbNo,\'block\',\'soft\',n,k,dmin)
berapprox = bercoding(EbNo,\'Hamming\',\'hard\',n)
berub = bercoding(EbNo,\'Golay\',\'hard\',24)
berapprox = bercoding(EbNo,\'RS\',\'hard\',n,k)
berapprox = bercoding(...,modulation)

Description:

berub = bercoding(EbNo,\'conv\',decision,coderate,dspec) returns an upper bound or approximation on the BER of a binary convolutional code with coherent phase shift keying (PSK) modulation over an additive white Gaussian noise (AWGN) channel. EbNo is the ratio of bit energy to noise power spectral density, in dB. If EbNo is a vector, berub is a vector of the same size, whose elements correspond to the different Eb/N0 levels. To specify hard-decision decoding, set decision to \'hard\'; to specify soft-decision decoding, set decision to \'soft\'. The convolutional code has code rate equal to coderate. The dspec input is a structure that contains information about the code\'s distance spectrum:

dspec.dfree is the minimum free distance of the code.

dspec.weight is the weight spectrum of the code.

berub = bercoding(EbNo,\'block\',\'hard\',n,k,dmin) returns an upper bound on the BER of an [n,k] binary block code with hard-decision decoding and coherent BPSK or QPSK modulation. dmin is the minimum distance of the code.

berub = bercoding(EbNo,\'block\',\'soft\',n,k,dmin) returns an upper bound on the BER of an [n,k] binary block code with soft-decision decoding and coherent BPSK or QPSK modulation. dmin is the minimum distance of the code.

berapprox = bercoding(EbNo,\'Hamming\',\'hard\',n) returns an approximation of the BER of a Hamming code using hard-decision decoding and coherent BPSK modulation. (For a Hamming code, if n is known, then k can be computed directly from n.)

berub = bercoding(EbNo,\'Golay\',\'hard\',24) returns an upper bound of the BER of a Golay code using hard-decision decoding and coherent BPSK modulation. Support for Golay currently is only for n=24. In accordance with [3], the Golay coding upper bound assumes only the correction of 3-error patterns. Even though it is theoretically possible to correct approximately 19% of 4-error patterns, most decoders in practice do not have this capability.

berapprox = bercoding(EbNo,\'RS\',\'hard\',n,k) returns an approximation of the BER of (n,k) Reed-Solomon code using hard-decision decoding and coherent BPSK modulation.

berapprox = bercoding(...,modulation) returns an approximation of the BER for coded AWGN channels when you specify a modulation type. See the berawgn function for a listing of the supported modulation types.

Binary PAM Modulator:

Using blocks from the SIMULINK Block Library, the Signal Processing Blockset, and the Communications Blockset, design a modulator (below) for the system model above. Use the modulator to send the sequence 1 -1 -1 1.
The modulator is composed of four blocks. The first block can be found at

DSP Blockset --> DSP Sources --> Signal From Workspace

Drag this block to your project window. Double-click on the Signal From Workspace block and set the block parameters.