- added Wiener stuff

git-svn-id: http://moon:8086/svn/matlab/trunk@40 801c6759-fa7c-4059-a304-17956f83a07c

ofdm/calcWiener.m

@@ -0,0 +1,53 @@
+function W_syms = calcWiener(drm_mode, drm_bw)
+% calcWiener('B', '10k');
+fs = 12000;
+fd_max = 0.1;
+tau_max = 0.0001;
+
+[params, spec_occ] = drm_params(drm_mode, drm_bw);
+N = params.nu
+Ts = params.nu/fs
+dF = 1/Ts
+
+frames_per_window = 2*params.y
+window_delay = params.y
+num_symbols_per_frame = params.nspf
+num_carrier_per_symbol = spec_occ.kmax - spec_occ.kmin - 1
+num_symbols_per_frame = num_symbols_per_frame * num_carrier_per_symbol
+
+% PHI = auto-covariance-matrix
+f_cut_t = 0.0675*1/window_delay; % two-sided maximum doppler frequency (normalized w.r.t symbol duration Ts) 
+f_cut_k = 1.75*params.ng/params.nu; % two-sided maximum echo delay (normalized w.r.t useful symbol duration Tu)
+
+W_syms = cell( params.y, 1 );
+
+for d=0:window_delay-1
+    k2_pos = [];
+    t2_pos = [];
+    for s=0:frames_per_window-1
+        [c, p, a] = getRefGain(params, spec_occ, s+d);
+        k2_pos = [k2_pos c];                            % length = window_delay*frames_per_window
+        t2_pos = [t2_pos ones(1, length(c))*(s)];       % length = window_delay*frames_per_window
+    end
+    
+    PHI = zeros(length(k2_pos));
+    % PHI
+    for k1=1:length(k2_pos)
+        k2 = [1:length(k2_pos)];
+        k1_pos = k2_pos(k1);
+        t1_pos = t2_pos(k1);
+        PHI(k1,k2) = sinc(f_cut_k*(k1_pos-k2_pos)) .* sinc(f_cut_t*(t1_pos-t2_pos));
+    end
+    PHI_inv = inv(PHI + eye(length(PHI))*0.0001);
+
+    THETA = zeros(1, length(k2_pos));
+    % THETA
+    carriers = spec_occ.kmin:spec_occ.kmax;
+    for k1=1:length(carriers)
+        k2 = [1:length(k2_pos)];
+        k1_pos = carriers(k1);
+        t1_pos = window_delay;
+        THETA(k2) = sinc( (k1_pos-k2_pos)*f_cut_k ).*sinc( (t1_pos-t2_pos)*f_cut_t );
+        W_syms{d+1}(:, k1) = transpose( THETA*PHI_inv );
+    end
+end

ofdm/jcorr.m

@@ -0,0 +1,7 @@
+function Rxy = jcorr(X, Y)
+    N = min(length(X), length(Y));
+%    X = X - mean(X);
+%    Y = Y - mean(Y);
+    for m=0:N-1
+        Rxy(m+1,:) = sum(X(1+m:N) .* conj(Y(1:N-m)));
+    end

ofdm/wiener_eval.m

@@ -0,0 +1,58 @@
+function wiener_eval(N, SNR, sigm)
+
+close all;
+
+USE_TD = 0;
+
+s = exp(j*4*pi.*(0:N-1)'/N);
+h = -exp(j*4*pi.*(0:N-1)'/N) .* (1+(0.5 - 0.5*cos(2*pi.*(0:N-1)'/N)));
+r = awgn(h.*s, SNR);
+
+hr = r./s;
+
+if USE_TD
+[ss, gs, rxy, ryy] = wiener_td(s, r, sigm);
+ss = ss./abs(ss);
+
+[hs, gh, rxyh, ryyh] = wiener_td(h, hr, sigm);
+
+figure
+plot(rxy); grid; legend('rxy');
+figure
+plot(ryy); grid; legend('ryy');
+
+else
+[ss, gs] = wiener_fd(s, r, sigm);
+ss = ss./abs(ss);
+
+[hs, gh] = wiener_fd(h, hr, sigm);
+end
+
+figure;
+subplot(2, 1, 1)
+plot(0:N-1, abs(h), 'b-', 0:N-1, abs(hr), 'g-', 0:N-1, abs(hs), 'r-'); grid; legend('|h|', '|h_{r}|', '|h_{s}|');
+subplot(2, 1, 2)
+plot(0:N-1, angle(h), 'b-', 0:N-1, angle(hr), 'g-', 0:N-1, angle(hs), 'r-'); grid; legend('phi(h)', 'phi(h_{r})', 'phi(h_{s})');
+
+figure;
+plot(real(s), imag(s), 'bO', real(r), imag(r), 'rx', real(ss), imag(ss), 'm+'); grid; legend('s', 'r', 'W*r');
+
+figure;
+plot(real(gs), imag(gs), 'b+'); grid; legend('gh');
+
+figure;
+plot(abs(gh)); grid; legend('|gs|');
+
+function h = channel(N, L, phi_max, omega_d_max, delay_max)
+k = 1;      % time
+for l=1:L   % frequency
+    hsum = 0;
+    for n=1:N
+        delay_n = delay_max*(1+fix(rand));
+        omega_n = omega_d_max*(1 - 2*rand());
+        phi_n = phi_max*pi*(1 - 2*rand());
+        hsum = hsum + exp(j*0*(phi_n+2*pi*omega_n*k + 2*pi*delay_n/L*l))
+    end
+    h(l,:) = 1/sqrt(N) * hsum;
+end
+

ofdm/wiener_fd.m

@@ -0,0 +1,14 @@
+function [xs, g, PSDx, PSDy] = wiener_fd(x, y, sigma_noise)
+    N = length(x);
+    Y = fft(y);
+    X = fft(x);
+    PSDx = 1/N*abs(X).^2; % spectral power
+    PSDy = 1/N*abs(Y).^2; % spectral power
+
+    k = sqrt(sum(PSDx)./sum(PSDy))
+    % fourier transform of the wiener filter
+    G = PSDx./(PSDx + sigma_noise.^2); 
+    % perform the filtering over the fourier
+    G = 1/k*G.*PSDx./PSDy;
+    xs = ifft(G.*Y);
+    g = ifft(G);

ofdm/wiener_td.m

@@ -0,0 +1,20 @@
+function [xs, g, rxy, ryy] = wiener_td(x, y, sigma_noise)
+%rxy = ifft(fft(x).*conj(fft(y)));
+x = x - mean(x);
+y = y - mean(y);
+rxy = circulant(y, 'step', -1).'*conj(x);
+%ryy = ifft(fft(y).*conj(fft(y)));
+ryy = circulant(y, 'step', -1).'*conj(y);
+
+sum_rxy = sum(abs(rxy))
+sum_ryy = sum(abs(ryy))
+
+R = toeplitz(ryy + sigma_noise.^2*(1-j));
+g = inv(R)*(rxy)./sum_ryy;
+
+sum_g = mean(abs(g))
+g = g./sum_g;
+
+xs = (circulant((g)))*(y);
+
+