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function [enc_comp] = cm_dpske(E,M,mapping,sequence); % [enc_comp] = cm_dpske(E,M,mapping,sequence) % CM_DPSKE differentially encodes a sequence. % E is the average energy, M is the number of c

function [ar,e]=lpcrr2ar(rr); %LPCRR2AR convert autocorrelation coefs to ar coefs [AR,E]=(RR) %E is the residual energy % could test e each time and remove rows when it gets small % Cop

function [enc_comp] = cm_dpske(E,M,mapping,sequence); % [enc_comp] = cm_dpske(E,M,mapping,sequence) % CM_DPSKE differentially encodes a sequence. % E is the average energy, M is the number of cons

function [ar,e]=lpcrr2ar(rr); %LPCRR2AR convert autocorrelation coefs to ar coefs [AR,E]=(RR) %E is the residual energy % could test e each time and remove rows when it gets small % Cop

function[p]=cm_bpske(snr_in_db) % [p]=cm_bpske(snr_in_db) % cm_bpske finds the probability of error for the given value of snr_in_db N=5000; E=1; %energy per symbol s

function[p]=cm_bpske(snr_in_db) % [p]=cm_bpske(snr_in_db) % cm_bpske finds the probability of error for the given value of snr_in_db N=5000; E=1; %energy per symbol s

function [ar,e]=lpcrr2ar(rr); %LPCRR2AR convert autocorrelation coefs to ar coefs [AR,E]=(RR) %E is the residual energy % could test e each time and remove rows when it gets small % Cop

function [enc_comp] = cm_dpske(E,M,mapping,sequence); % [enc_comp] = cm_dpske(E,M,mapping,sequence) % CM_DPSKE differentially encodes a sequence. % E is the average energy, M is the number of c

%UEC Minimum mean-squared error time domain equalizer design % using the unit-energy constraint. % [B, W, D, MSE, Dv] = UEC(X, Y, N, Nb, Nw, Dmin, Dmax) returns % the optimal target impulse respon

function[p]=cm_bpske(snr_in_db) % [p]=cm_bpske(snr_in_db) % cm_bpske finds the probability of error for the given value of snr_in_db N=5000; E=1; %energy per symbol s