Update April
This commit is contained in:
Silas Oettinghaus
@@ -51,9 +51,9 @@ classdef EQ_silas < handle
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trainloops
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ddloops
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eq_blocklength % block lengt of EQ (until now, only the dc subtraction is affected by this)
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eq_parallelization_blocklength % block lengt of EQ (until now, only the dc subtraction is affected by this)
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eq_updatelatency % time in symbols until the calculated updates reach the signal again (until now, only the dc subtraction is affected by this)
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eq_avg_blocklength
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end
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@@ -79,8 +79,9 @@ classdef EQ_silas < handle
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options.mu_ffe_dd = [0.0004 0.0005 0.0006];
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options.mu_dfe_dd = 0.0005;
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options.eq_blocklength = 1;
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options.eq_parallelization_blocklength = 1;
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options.eq_updatelatency = 1;
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options.eq_avg_blocklength = 0;
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end
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fn = fieldnames(options);
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@@ -151,8 +152,8 @@ classdef EQ_silas < handle
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%% Adaptive Equalization Modes
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function trainingMode(obj)
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dc_block = ones(obj.eq_blocklength,1);
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dc_block = ones(obj.eq_parallelization_blocklength,1);
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for tloop = 1:obj.trainloops
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m = 1+obj.delay;
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@@ -176,7 +177,7 @@ classdef EQ_silas < handle
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% Calculate the Error
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obj.error = obj.e_dc + obj.e_ffe - obj.e_dfe - obj.d(obj.Nb(1)-1+m-obj.delay);
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if obj.mu_ffe_train ~= 0
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%update FFE coefficients with LMS
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@@ -191,8 +192,8 @@ classdef EQ_silas < handle
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%update DC error
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dc_block(dc_cnt) = obj.error .* obj.mu_dc_train;
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if dc_cnt == obj.eq_blocklength
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if dc_cnt == obj.eq_parallelization_blocklength
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obj.e_dc = obj.e_dc - mean(dc_block(dc_cnt));
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dc_cnt = 0;
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end
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@@ -208,8 +209,10 @@ classdef EQ_silas < handle
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%start the dd mode with coefficients from training
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coeff = [obj.e;obj.b];
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obj.e_dc = ones(obj.eq_updatelatency,1).*obj.e_dc;
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dc_block = ones(obj.eq_parallelization_blocklength,1);
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dc_block = ones(obj.eq_blocklength,1);
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for ddloop = 1:obj.ddloops
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@@ -227,50 +230,96 @@ classdef EQ_silas < handle
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mu_dfe = ones(1,sum(obj.Cb))*obj.mu_dfe_dd;
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y = zeros(1,floor(obj.x_length/obj.sps));
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d_feedback = zeros(obj.Cb(1),1);
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d_vnle = obj.calcVNLENonlinVecs(d_feedback,obj.Ib2,obj.Ib3,obj.Nb,obj.d_norm);
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d_hat = zeros(obj.x_length,1);
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m_reg = 0;
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if obj.eq_avg_blocklength > 0
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averaging_window = zeros(obj.eq_avg_blocklength,1);
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end
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for k = 1:obj.sps:obj.x_length
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dc_cnt = dc_cnt+1;
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m=m+1;
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%get Sigal input vectors with correct length for VNLE
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x = obj.x_in(obj.Ne(1)+k-1:-1:k).';
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%
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if obj.eq_avg_blocklength > 0 %% Das läuft gut mit 400er Fenster!!
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averaging_window = circshift(averaging_window,obj.sps);
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averaging_window(1:obj.sps,1) = x(1:obj.sps);
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avg_(k) = mean(averaging_window);
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x = x-avg_(k);
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end
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%bring this signal to "special" VNLE format
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x_vnle = obj.calcVNLENonlinVecs(x,obj.Ie2,obj.Ie3,obj.Ne,obj.x_norm);
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%combine FFE with DFE to one vector (cursor between the two sequences)
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x_d = [x_vnle;-d_vnle];
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%Apply filter
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y(m) = obj.e_dc + x_d.'* coeff;
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%y(m) = (m_reg(end)*dc_cnt + obj.e_dc(end)) + x_d.'* coeff;
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if obj.mu_dc_dd > 0
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y(m) = obj.e_dc(end) + x_d.'* coeff;
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else
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y(m) = x_d.'* coeff;
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end
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% if obj.eq_avg_blocklength > 0 %% Das läuft nicht gut!!
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% averaging_window = circshift(averaging_window,obj.sps);
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% averaging_window(1:obj.sps,1) = y(m);
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% avg_(m) = mean(averaging_window);
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% y(m) = y(m)-avg_(m);
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% end
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%Decision
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[~,symbol_idx] = min(abs(y(m) - obj.d_constellation)); % decision for closest constellation point
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d_hat(k) = obj.d_constellation(symbol_idx);
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%Error between FFE & DFE filtered signal and Decision
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obj.error = y(m) - d_hat(k);
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obj.error(k) = y(m) - d_hat(k);
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%Update coefficients (both FFE and DFE)
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% obj.e = obj.e - obj.error * mu_ffe * conj(x_vnle);
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% obj.b = obj.b + obj.error * mu_dfe * conj(d_vnle);
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% coeff = [obj.e;obj.b];
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% if obj.eq_avg_blocklength > 0 %% Das läuft nicht gut!!
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% averaging_window = circshift(averaging_window,obj.sps);
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% averaging_window(1:obj.sps,1) = y(m);
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% avg_(m) = mean(averaging_window);
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% y(m) = y(m)-avg_(m);
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% end
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coeff = coeff - mu_mat*obj.error*conj(x_d);
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%update DC error
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dc_block(dc_cnt) = obj.error .* obj.mu_dc_dd;
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%Update FFE and DFE coefficients
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coeff = coeff - mu_mat*obj.error(k) * conj(x_d);
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%Update DC error
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dc_block(dc_cnt) = obj.error(k) ;
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if dc_cnt == obj.eq_parallelization_blocklength
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if obj.eq_updatelatency > 1
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obj.e_dc = circshift(obj.e_dc,1);
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% m_reg(end+1) = ((1:obj.eq_parallelization_blocklength)' \ (cumsum(dc_block)));
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%
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% obj.e_dc(1) = obj.e_dc(2) - sign(m_reg(end)) .* (sum(dc_block).* m_reg(end) .* obj.mu_dc_dd);
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obj.e_dc(1) = obj.e_dc(2) - sum(dc_block) .* obj.mu_dc_dd;
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else
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%m_reg(end+1) = ((1:obj.eq_parallelization_blocklength)' \ (cumsum(dc_block)));
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% obj.e_dc = obj.e_dc - sign(m_reg(end)) .* (sum(dc_block).* m_reg(end) .* obj.mu_dc_dd);
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obj.e_dc = obj.e_dc - sum(dc_block) .* obj.mu_dc_dd;
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end
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if dc_cnt == obj.eq_blocklength
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obj.e_dc = obj.e_dc - mean(dc_block(dc_cnt));
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dc_cnt = 0;
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end
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% Append new decision to decision feedback
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if obj.Nb(1) > 0
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