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Silas Oettinghaus
2026-07-13 19:59:57 +02:00
parent ef0a74cb7f
commit f421348e5b
15 changed files with 2682 additions and 487 deletions
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Classes/04_DSP/Equalizer/MPI_reduction/FFE_A1.m Normal file
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classdef FFE_A1 < handle
%FFE_A1 Feed-forward equalizer with A1 moving-average input suppression.
%
% This class is intentionally narrow: it contains the normal adaptive FFE
% flow plus the A1 block-rate moving-average input correction. A2,
% DC-tracking, and optimizer logic are kept out of this file.
properties
sps
order
e
e_tr
error
len_tr
mu_tr
epochs_tr
adaption_technique
dd_mode
mu_dd
epochs_dd
dd_len_fraction
% A1 moving-average input suppression
dc_avg_bufferlength_a1
dc_avg_update_blocklength_a1
dc_smoothing_a1
P
constellation
decide
save_debug = 0
debug_struct
end
methods
function obj = FFE_A1(options)
arguments(Input)
options.sps = 2
options.order = 15
options.len_tr = 4096
options.mu_tr = 0
options.epochs_tr = 5
options.adaption_technique adaption_method = adaption_method.lms
options.dd_mode = 1
options.mu_dd = 1e-5
options.epochs_dd = 5
options.dd_len_fraction = 1
options.dc_avg_bufferlength_a1 = 0
options.dc_avg_update_blocklength_a1 = 0
options.dc_smoothing_a1 = 0
options.decide = false
options.save_debug = 0
end
fn = fieldnames(options);
for n = 1:numel(fn)
obj.(fn{n}) = options.(fn{n});
end
assert(obj.dc_avg_bufferlength_a1 >= 0);
assert(obj.dc_avg_update_blocklength_a1 >= 0);
obj.e = zeros(obj.order,1);
obj.error = 0;
obj.dc_avg_bufferlength_a1 = floor(obj.dc_avg_bufferlength_a1);
obj.dc_avg_update_blocklength_a1 = floor(obj.dc_avg_update_blocklength_a1);
obj.dc_smoothing_a1 = min(max(obj.dc_smoothing_a1,0),1);
end
function [X,Noi] = process(obj,X,D)
X = X.normalize("mode","rms");
obj.constellation = unique(D.signal);
delta = 0.05;
obj.P = (1/delta) * eye(obj.order);
training = true;
showviz = false;
obj.equalize(X.signal,D.signal,obj.mu_tr,obj.epochs_tr,obj.len_tr,training,showviz);
obj.e_tr = obj.e;
n = X.length;
training = false;
if obj.dd_mode
[signal,decision] = obj.equalize(X.signal,D.signal,obj.mu_dd,obj.epochs_dd,n,training,showviz);
else
[signal,decision] = obj.equalize(X.signal,D.signal,0,1,n,training,showviz);
end
if obj.decide
X.signal = decision;
else
X.signal = signal;
end
X.fs = D.fs;
X = X.logbookentry([num2str(obj.order),' tap FFE A1']);
Noi = X - D;
end
function [y,d_hat] = equalize(obj,x,d,mu,epochs,N,training,showviz)
arguments
obj
x
d
mu
epochs
N
training
showviz %#ok<INUSA>
end
x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
lambda = mu;
n_symbols = ceil(N / obj.sps);
y = zeros(n_symbols,1);
d_hat = zeros(n_symbols,1);
err = zeros(n_symbols,1);
constellation = obj.constellation;
adaption_code = obj.adaptionCode();
adaption_is_rls = adaption_code == 3;
mask = ones(obj.order,1);
maincursor_pos = ceil(length(obj.e)/2);
grad = 0;
weight = 0;
update = 0;
if mu == 0 || (~obj.dd_mode && ~training)
epochs = 1;
end
dc_avg_enabled = obj.dc_avg_bufferlength_a1 > 1;
if dc_avg_enabled
dc_avg_buffer = NaN(obj.dc_avg_bufferlength_a1,1);
dc_avg_buffer_pos = 0;
dc_avg_sum = 0;
dc_avg_valid_count = 0;
dc_avg_est = 0;
dc_avg_update_blocklength = obj.dc_avg_update_blocklength_a1;
if dc_avg_update_blocklength <= 0
dc_avg_update_blocklength = obj.dc_avg_bufferlength_a1;
end
dc_avg_update_blocklength = max(1,floor(dc_avg_update_blocklength));
dc_avg_input_offset = floor(obj.order/2);
% Hardware-like A1 is causal; dc_smoothing_a1 is reserved for offline variants.
end
debug_enabled = obj.save_debug;
if debug_enabled
obj.debug_struct.error = NaN(1,n_symbols);
obj.debug_struct.error_first_epoch = NaN(1,n_symbols);
obj.debug_struct.main_cursor = NaN(1,n_symbols);
obj.debug_struct.mu_nlms = NaN(1,n_symbols);
obj.debug_struct.update_gradient = NaN(1,n_symbols);
obj.debug_struct.dc_avg_offset = NaN(1,n_symbols);
if training
obj.debug_struct.error_tr = NaN(1,n_symbols);
obj.debug_struct.update_tr = NaN(1,n_symbols);
else
obj.debug_struct.error_dd = NaN(1,n_symbols);
obj.debug_struct.update = NaN(1,n_symbols);
end
end
for epoch = 1:epochs
symbol = 0;
for sample = 1:obj.sps:N
symbol = symbol + 1;
dc_avg_offset = 0;
U = x(obj.order+sample-1:-1:sample);
if dc_avg_enabled
dc_avg_offset = dc_avg_est;
U = U - dc_avg_offset;
end
y(symbol,1) = (obj.e.*mask).' * U;
if dc_avg_enabled
raw_sample_idx = dc_avg_input_offset + sample;
[dc_avg_buffer,dc_avg_buffer_pos,dc_avg_sum,dc_avg_valid_count,dc_avg_est] = ...
obj.updateA1Buffer(dc_avg_buffer,dc_avg_buffer_pos,dc_avg_sum, ...
dc_avg_valid_count,dc_avg_est,x(raw_sample_idx),symbol, ...
dc_avg_update_blocklength);
end
if training
d_hat(symbol,1) = d(symbol);
else
[~,symbol_idx] = min(abs(y(symbol) - constellation));
d_hat(symbol,1) = constellation(symbol_idx);
end
err(symbol) = d_hat(symbol) - y(symbol);
if training || obj.dd_mode
switch adaption_code
case 1
normU = (U.'*U) + eps;
weight = mu / normU;
grad = err(symbol) * U;
update = grad * weight;
obj.e = obj.e + update;
case 2
weight = mu;
grad = err(symbol) * U;
update = grad * weight;
obj.e = obj.e + update;
case 3
denom = lambda + U.' * obj.P * U;
k = (obj.P * U) / denom;
update = k * err(symbol);
obj.e = obj.e + update;
obj.P = (1/lambda) * (obj.P - k * (U.' * obj.P));
end
end
if debug_enabled && epoch == 1
obj.debug_struct.error_first_epoch(1,symbol) = err(symbol) * err(symbol)';
end
if debug_enabled && epoch == epochs
error_power = err(symbol) * err(symbol)';
update_power = update.'*update ./ (sqrt((obj.e.'*obj.e) / obj.order) + eps);
obj.debug_struct.error(1,symbol) = error_power;
obj.debug_struct.main_cursor(1,symbol) = abs(obj.e(maincursor_pos));
obj.debug_struct.mu_nlms(1,symbol) = weight;
obj.debug_struct.update_gradient(1,symbol) = grad.'*grad;
obj.debug_struct.dc_avg_offset(1,symbol) = dc_avg_offset;
if training
obj.debug_struct.error_tr(1,symbol) = error_power;
obj.debug_struct.update_tr(1,symbol) = update_power;
else
obj.debug_struct.error_dd(1,symbol) = error_power;
obj.debug_struct.update(1,symbol) = update_power;
end
end
end
end
if ~adaption_is_rls
obj.P = [];
end
end
end
methods (Access = private)
function adaption_code = adaptionCode(obj)
if obj.adaption_technique == adaption_method.nlms
adaption_code = 1;
elseif obj.adaption_technique == adaption_method.lms
adaption_code = 2;
elseif obj.adaption_technique == adaption_method.rls
adaption_code = 3;
else
builtin("error","FFE_A1:InvalidAdaptionTechnique", ...
"Unsupported FFE adaption technique.");
end
end
end
methods (Static, Access = private)
function [buffer,buffer_pos,buffer_sum,valid_count,estimate] = ...
updateA1Buffer(buffer,buffer_pos,buffer_sum,valid_count,estimate, ...
new_value,symbol,update_blocklength)
buffer_pos = buffer_pos + 1;
if buffer_pos > numel(buffer)
buffer_pos = 1;
end
old_value = buffer(buffer_pos);
if isfinite(old_value)
buffer_sum = buffer_sum - old_value;
valid_count = valid_count - 1;
end
if isfinite(new_value)
buffer(buffer_pos) = new_value;
buffer_sum = buffer_sum + new_value;
valid_count = valid_count + 1;
else
buffer(buffer_pos) = NaN;
end
if mod(symbol,update_blocklength) == 0
if valid_count == 0
estimate = 0;
else
estimate = buffer_sum / valid_count;
end
end
end
end
end