Few more scripts to evaluate new ML-MLSE Equalizer - which is not better!!! :-(
This commit is contained in:
silas (home)
@@ -53,6 +53,8 @@ classdef ML_MLSE < handle
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state_dict % containers.Map: key(sequence)->state index
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key_fmt = '%.8g_'; % key format for sequence strings
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nSym % |constellation|
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ber = []
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end
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methods
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@@ -182,7 +184,7 @@ classdef ML_MLSE < handle
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% ==============================================================
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% FFE + Whitening + ML-Based Branch Metric Estimation + Viterbi
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% ==============================================================
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debug = 0;
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debug = 1;
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% --- Input padding and preallocation
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y = zeros(N,1);
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@@ -199,19 +201,34 @@ classdef ML_MLSE < handle
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pred = zeros(nSymbols, obj.nStates, 'uint32');
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pm_sto = nan(obj.nStates, nSymbols,'like',pm);
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% --- Initialize "true" trellis state for training (shift-register style)
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% expect sequences in chronological order [x_{k-L+1}:x_k], but combs rows are [x_k, x_{k-1}, ...]
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if numel(d) >= obj.L
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init_seq = d(1:obj.L); % [x_1 ... x_L]
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true_to_state_idx = obj.state_dict(obj.seq_key(flip(init_seq))); % flip to [x_L, x_{L-1}, ...]
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%%% START IDX
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if training
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max_start = length(x) - ( (ceil(N/obj.sps)-1)*obj.sps + 1 );
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max_start = max(1, max_start); % safety
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start_sample = randi([1, max_start], 1); %rnd training; not really good
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start_sample = 1;
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end_sample = start_sample + (ceil(N/obj.sps)-1)*obj.sps;
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else
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true_to_state_idx = 1;
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start_sample = 1;
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end_sample = N;
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end
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start_symbol = 1 + floor((start_sample - 1)/obj.sps); % ABSOLUTE symbol index
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if numel(d) >= obj.L && start_symbol >= obj.L
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init_seq = d(start_symbol-obj.L+1 : start_symbol); % [d_k-L+1 ... d_k]
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true_to_state_idx = obj.state_dict(obj.seq_key(flip(init_seq))); % [d_k ... d_k-L+1]
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else
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% Not enough history – fall back to state 1
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true_to_state_idx = uint32(1);
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end
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symbol = 0;
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for sample = 1:obj.sps:N
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for sample = start_sample:obj.sps:end_sample
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symbol = symbol + 1;
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k = symbol;
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sym_idx = start_symbol + (symbol - 1);
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% --- Build Δ-delayed observation window y_k
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i1 = sample - obj.Nf + 1 + obj.delta;
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@@ -232,78 +249,72 @@ classdef ML_MLSE < handle
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v_tilde = pm(obj.valid_from_idx) + c_hat; % [nFeasible×1]
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% ===== Gradient update (Algorithm 1) =====
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% if training
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if k > obj.L
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% shift-register: previous "to" becomes current "from"
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if 1 %training
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% previous "to" becomes current "from" (shift-register)
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true_from_state_idx = true_to_state_idx;
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% current "to" from data window
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curr_seq = d(k-obj.L+1:k);
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key_to = obj.seq_key(flip(curr_seq));
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% --- Build current "to" state from ABSOLUTE symbol index
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if sym_idx >= obj.L
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curr_seq = d(sym_idx-obj.L+1 : sym_idx); % [d_k-L+1 ... d_k]
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key_to = obj.seq_key(flip(curr_seq)); % -> [d_k ... d_k-L+1]
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if isKey(obj.state_dict, key_to)
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true_to_state_idx = obj.state_dict(key_to);
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else
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% fall back safely (should not happen with proper constellation)
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% Fall back safely (should not happen with proper constellation)
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true_to_state_idx = true_from_state_idx;
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end
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else
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% not enough history yet
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true_from_state_idx = 1;
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true_to_state_idx = true_to_state_idx; % keep init
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end
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if 0
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disp(['FROM: state',char(num2str(true_from_state_idx)),' : symbol transition', char(num2str(obj.combs(true_from_state_idx,:)))]);
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disp(['TO: state',char(num2str(true_to_state_idx)),' : symbol transition', char(num2str(obj.combs(true_to_state_idx,:)))]);
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% Not enough history yet for a full L-symbol state
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% keep previous 'to' and 'from'
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true_to_state_idx = true_to_state_idx;
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true_from_state_idx = true_from_state_idx;
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end
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% Dirac delta over correct extended transition (from,to)
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dirac = zeros(obj.nFeasible,1);
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dirac(obj.valid_from_idx==true_from_state_idx & obj.valid_to_idx==true_to_state_idx) = 1; % This Dirac delta function δ(s = s∗ k, s′ = s∗ k−1) = 1 if the extended state (s, s′) corresponds to the true realized states (s∗ k, s∗ k−1), and is zero otherwise.
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dirac(obj.valid_from_idx==true_from_state_idx & ...
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obj.valid_to_idx ==true_to_state_idx) = 1;
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% softmax over -v_tilde (numerically safe shift)
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p = exp(-(v_tilde - max(v_tilde)));
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p = p./sum(p); % found in formula (9) and (19)
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p = p./(sum(p)+eps);
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% gradient term (t - p)
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dmp = (dirac - p)'; % 1×nFeasible
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if mod(symbol,128) == 1 && debug
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% --- Normalize and compute probabilities
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v_norm = v_tilde - max(v_tilde);
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probs_lin = exp(-v_norm); probs_lin = probs_lin ./ sum(probs_lin);
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probs_log = -v_norm;
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% Per-feature gradient; implicit expansion gives (Nf+1)×nFeasible
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dL_Dw = (yk) .* dmp;
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% --- Map back into nStates×nStates grid
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probs_mat = nan(obj.nStates, obj.nStates);
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probs_logmat = nan(obj.nStates, obj.nStates);
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probs_mat(obj.valid) = probs_lin;
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probs_logmat(obj.valid) = probs_log;
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% Start updates only when the ABSOLUTE symbol index has ≥ L history
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if sym_idx >= obj.L
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% --- Identify the current true transition
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[to_idx, from_idx] = find(obj.valid);
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cur_idx = find(dirac==1);
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cur_to = to_idx(cur_idx);
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cur_from = from_idx(cur_idx);
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figure(11); clf;
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imagesc(probs_logmat); axis xy; colorbar;
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xlabel('From state'); ylabel('To state'); set(gca,'FontSize',10);
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hold on;
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plot(cur_from, cur_to, 'rs', 'MarkerSize', 10, 'LineWidth', 2, 'MarkerFaceColor', 'none');
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hold off;
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obj.w = obj.w - ones(size(dL_Dw,1),1).*mu .* dL_Dw; % (Nf+1)×nFeasible
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% obj.w = obj.w - mu * dL_Dw; % (Nf+1)×nFeasible
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end
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if 1 %training
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% dmp is large for the correct transition -> update emphasizes that branch
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dL_Dw = dmp .* (yk); % ∂CE/∂(w) - formula (10)
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% only start with updates when we are inside the signal
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if k > obj.L
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obj.w = obj.w - mu * dL_Dw; % (Nf+1)×nFeasible
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end
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% if debug && epoch > 2
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% figure(100);
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% subplot(4,1,1);
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% heatmap(p');
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% title('Probs')
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% subplot(4,1,2);
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% heatmap(dmp);
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% title('Update')
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% subplot(4,1,3);
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% heatmap(dL_Dw);
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% title('Update')
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% subplot(4,1,4);
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% heatmap(bj.w);
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% title('Update')
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%
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% end
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end
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% --- Compare-Select (matrix form, min of costs)
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v_tilde_mat = inf(obj.nStates, obj.nStates);
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v_tilde_mat(obj.valid) = v_tilde;
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@@ -327,10 +338,21 @@ classdef ML_MLSE < handle
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y_vit = obj.first_sym(viterbi_path);
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y = obj.first_sym(viterbi_path);
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if 1 %debug || training
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err = sum(y ~= d(1:length(y)));
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ser = err./length(y);
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fprintf('Epoch: %d - SER: %.1e \n',epoch, ser);
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if debug %&& training
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sym_start = start_symbol;
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sym_end = start_symbol + symbol - 1;
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ref_slice = d(sym_start : sym_end);
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err = sum(y ~= ref_slice(1:numel(y)));
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ref_bits = PAMmapper(obj.S,0).demap(ref_slice);
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eq_bits = PAMmapper(obj.S,0).demap(y);
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[~, ~, ber, ~] = calc_ber(ref_bits, eq_bits, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
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fprintf('Epoch: %d - BER: %.1e \n',epoch, ber);
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obj.ber(epoch) = ber;
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% ser = err./length(y);
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% fprintf('Epoch: %d - SER: %.1e \n',epoch, ser);
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figure(10);
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subplot(2,2,1:2);
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@@ -347,6 +369,8 @@ classdef ML_MLSE < handle
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scatter(1:symbol,pm_sto,1,'.')
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% plot(1:symbol,pm_sto,'LineStyle','none')
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title('Path Metric Winners')
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drawnow
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end
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end
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end
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@@ -15,7 +15,7 @@ if 1
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wh.addStorage("ber");
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% wh = submit_simulations(wh,"parallel",0,"simulation_mode",0);
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wh = submit_handle(@imdd_model,wh,"parallel",1);
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wh = submit_handle(@imdd_model,wh,"parallel",0);
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end
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@@ -1,213 +0,0 @@
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function minimal_model_gpt
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% Minimal binary IM/DD + ISI (M=3), AWGN. RX: ML-based MLSE vs classic MLSE
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% Fixes:
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% 1) Correct (s',s) labeling: s'=[x_{k-1},x_{k-2}], s=[x_k,x_{k-1}]
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% 2) Window standardization (z-score) from pilot
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% 3) Non-negative learned BM: c_hat = (w^T y_std + b).^2
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% 4) Stable training loop bounds; BER length alignment
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clear; clc; rng(1);
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%% Parameters
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Ktr = 4000; % pilot
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Kte = 20000; % test
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M = 3; % true channel memory
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L = 3; % MLSE memory (states keep L-1 symbols)
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h = [0.55 1.00 0.45]; % ISI FIR
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EsN0dB= 12;
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D = 5*L; % traceback (unused here; full TB)
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Nwin = 7; % BM feature window
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Delta = 0;
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%% TX, channel, noise
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map = @(b) 2*b-1;
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b_tr = randi([0 1],Ktr,1); x_tr = map(b_tr);
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b_te = randi([0 1],Kte,1); x_te = map(b_te);
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convpad = @(x) filter(h,1,[x; zeros(M-1,1)]);
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ytr_clean = convpad(x_tr);
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yte_clean = convpad(x_te);
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EsN0 = 10^(EsN0dB/10);
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sigma2 = 1/(2*EsN0);
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awgn = @(len) sqrt(sigma2)*randn(len,1);
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y_tr = ytr_clean + awgn(length(ytr_clean));
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y_te = yte_clean + awgn(length(yte_clean));
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% remove tail to keep equal lengths
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trim = M-1;
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y_tr = y_tr(1:end-trim); x_tr = x_tr(1:end-trim); b_tr = b_tr(1:end-trim);
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y_te = y_te(1:end-trim); x_te = x_te(1:end-trim); b_te = b_te(1:end-trim);
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%% Trellis
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nStates = 2^(L-1);
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statesBin = de2bi(0:nStates-1,L-1,'left-msb');
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symOfBit = @(bit) (2*bit-1);
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% transitions (s'->s) with input symbol u in {±1}
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trans = struct('sp',[],'s',[],'u',[],'ubit',[]);
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idx=1;
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for sp=1:nStates
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prev_bits = statesBin(sp,:); % [x_{k-1}, x_{k-2}] as bits
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for ubit=[0 1]
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u = symOfBit(ubit);
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nb = [prev_bits(1),ubit]; % new state s = [x_k, x_{k-1}]
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s = bi2de(nb,'left-msb')+1;
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trans(idx).sp=sp; trans(idx).s=s; trans(idx).u=u; trans(idx).ubit=ubit;
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idx=idx+1;
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end
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end
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nTrans = numel(trans);
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%% Classic BM (Gaussian)
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BM_classic = @(yk, sp, s, u) (( yk - h * [u, symOfBit(statesBin(sp,:))]')^2)/(2*sigma2);
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%% ML-based BM: c_hat = (w^T y_std + b)^2 (nonnegative)
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padN = Nwin-1;
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pad = @(y) [zeros(Delta+padN,1); y; zeros(max(0,Nwin-1-Delta),1)];
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y_tr_p = pad(y_tr); y_te_p = pad(y_te);
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% standardize window features using pilot
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Ytr_mat = im2col_sliding(y_tr_p, Nwin, Delta); % Nwin × T
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mu_win = mean(Ytr_mat,2);
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sd_win = std(Ytr_mat,0,2)+1e-8;
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W = zeros(Nwin,nTrans);
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b0= zeros(1,nTrans);
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softmax = @(z) exp(z - max(z))./sum(exp(z - max(z)));
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mu = 0.005; % LR
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nEpoch = 6; % light training
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Ttr = length(y_tr);
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for ep=1:nEpoch
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v = zeros(nStates,1); v(2:end)=Inf;
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for k = L : min(Ttr - (L-1), Ttr) % need x(k-1), x(k-2)
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% true (s',s):
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sp_bits = [(x_tr(k-1)<0), (x_tr(k-2)<0)];
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s_bits = [(x_tr(k) <0), (x_tr(k-1)<0)];
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sp_star = bi2de(sp_bits,'left-msb')+1;
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s_star = bi2de(s_bits ,'left-msb')+1;
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% feature window (z-scored)
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yw = y_tr_p(k-Delta : k-Delta+Nwin-1);
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yw = (yw - mu_win) ./ sd_win;
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% compute extended PM logits over all (s',s)
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vtil = -inf(nTrans,1);
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for t=1:nTrans
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z = W(:,t).'*yw + b0(t);
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c_hat = z*z; % (.)^2 nonnegative BM
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vtil(t) = -( v(trans(t).sp) + c_hat);
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end
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pext = softmax(vtil).';
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t_star = find([trans.sp]==sp_star & [trans.s]==s_star,1);
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% CE gradient wrt c_hat, chain rule through square
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delta = pext; delta(t_star)=delta(t_star)-1; delta = -delta; % sign for vtil=-(...)
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for t=1:nTrans
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z = W(:,t).'*yw + b0(t);
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dc_dz = 2*z; % d(z^2)/dz
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g = delta(t) * dc_dz; % ∂L/∂z
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W(:,t) = W(:,t) - mu * g * yw;
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b0(t) = b0(t) - mu * g;
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end
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% PM update for next step
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v_next = inf(nStates,1);
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for t=1:nTrans
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sp=trans(t).sp; s=trans(t).s;
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z = W(:,t).'*yw + b0(t);
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c_hat = z*z;
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cand = v(sp)+c_hat;
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if cand < v_next(s)
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v_next(s)=cand;
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end
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end
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v = v_next;
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end
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end
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%% Decode (full-length traceback)
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[xhat_ml, bhat_ml] = viterbi_decode(y_te, L, trans, @(k) feat_std(y_te_p,k,Nwin,Delta,mu_win,sd_win), ...
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@(yk,sp,s,u) learnedBM(W,b0,yk,sp,s,u), 0);
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[xhat_ex, bhat_ex] = viterbi_decode(y_te, L, trans, @(k) y_te(k), ...
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@(yk,sp,s,u) BM_classic(yk,sp,s,u), 0);
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%% BER (align by min length)
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Lmin = min([length(bhat_ml), length(bhat_ex), length(b_te)]);
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BER_ml = mean(bhat_ml(1:Lmin) ~= (b_te(1:Lmin)>0));
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BER_ex = mean(bhat_ex(1:Lmin) ~= (b_te(1:Lmin)>0));
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fprintf('BER (ML-based MLSE): %.3e\n', BER_ml);
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fprintf('BER (classic MLSE ): %.3e\n', BER_ex);
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end
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% ----------------- helpers -----------------
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function Y = im2col_sliding(y_pad, Nwin, Delta)
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T = length(y_pad) - (Nwin-1) - Delta;
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Y = zeros(Nwin,T);
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for k=1:T
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Y(:,k) = y_pad(k-Delta : k-Delta+Nwin-1);
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end
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end
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function yw = feat_std(y_pad,k,Nwin,Delta,mu_win,sd_win)
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yw = y_pad(k-Delta : k-Delta+Nwin-1);
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yw = (yw - mu_win) ./ sd_win;
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end
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function c = learnedBM(W,b0,yw,sp,s,~)
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% pick parameters of the (sp->s) transition
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persistent map;
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if isempty(map)
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% build once: index of W/b0 for each (sp,s)
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nStates = size(W,1)*0+1; %#ok<NASGU>
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end
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% linear scan is fine at this size:
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% (use first match of (sp,s))
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c = inf;
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for t=1:size(W,2)
|
||||
% suppose we stored (sp,s) order as in training; we cannot access here.
|
||||
% Instead, pass the exact column via a small mapper (build each call):
|
||||
end
|
||||
% Faster: precompute a table outside; here we reconstruct like in training
|
||||
% (rebuild tiny mapper)
|
||||
persistent key_sp key_s
|
||||
if isempty(key_sp)
|
||||
key_sp = evalin('caller','[trans.sp]');
|
||||
key_s = evalin('caller','[trans.s]');
|
||||
end
|
||||
t = find(key_sp==sp & key_s==s,1);
|
||||
z = W(:,t).'*yw + b0(t);
|
||||
c = z*z;
|
||||
end
|
||||
|
||||
function [xhat, bhat] = viterbi_decode(y, L, trans, getFeat, BMfun, D_unused)
|
||||
nStates = 2^(L-1);
|
||||
T = length(y);
|
||||
v = inf(nStates,T); v(:,1)=Inf; v(1,1)=0;
|
||||
prev = zeros(nStates,T); in_u = zeros(nStates,T);
|
||||
|
||||
for k=1:T
|
||||
if k==1, vprev=inf(nStates,1); vprev(1)=0; else, vprev=v(:,k-1); end
|
||||
vcur = inf(nStates,1); pre=zeros(nStates,1); inb=zeros(nStates,1);
|
||||
|
||||
feat = getFeat(k); % either scalar y(k) or standardized window
|
||||
for t=1:numel(trans)
|
||||
sp=trans(t).sp; s=trans(t).s; u=trans(t).u;
|
||||
ck = BMfun(feat, sp, s, u);
|
||||
cand = vprev(sp)+ck;
|
||||
if cand < vcur(s)
|
||||
vcur(s)=cand; pre(s)=sp; inb(s)=u;
|
||||
end
|
||||
end
|
||||
v(:,k)=vcur; prev(:,k)=pre; in_u(:,k)=inb;
|
||||
end
|
||||
|
||||
[~,st]=min(v(:,T));
|
||||
xhat=zeros(T,1);
|
||||
for k=T:-1:1
|
||||
xhat(k)=in_u(st,k);
|
||||
st=prev(st,k); if st==0, st=1; end
|
||||
end
|
||||
bhat = xhat>0;
|
||||
end
|
||||
@@ -18,13 +18,12 @@ laser_linewidth = 1e6;
|
||||
% Channel
|
||||
link_length = 0;
|
||||
|
||||
alpha = 0;
|
||||
|
||||
doub_mode = db_mode.no_db;
|
||||
cols = linspecer(6);
|
||||
rop = [-5];
|
||||
rop = [-8];
|
||||
bwl = [0.5:0.1:1.5];
|
||||
fsym = [212:16:256].*1e9;
|
||||
fsym = [160:16:256].*1e9;
|
||||
% nonlin_mod = [0.5:0.01:0.75];
|
||||
nonlin_mod = ones(size(fsym)).*0.5;
|
||||
|
||||
@@ -100,6 +99,7 @@ for r = 1:length(fsym)
|
||||
%%
|
||||
|
||||
mu_lms = 0.0005;
|
||||
pf_ncoeffs = 2;
|
||||
eq_ = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",2,"dd_mode",1,"adaption_technique","lms");
|
||||
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
|
||||
|
||||
@@ -116,49 +116,63 @@ for r = 1:length(fsym)
|
||||
% Sequence Est
|
||||
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels,'scale_mode',0,'trellis_exclusion',0,'trellis_state_mode',2,'debug',0,'DIR',pf_.coefficients);
|
||||
[y_mlse] = mlse_.process(y_white,Symbols);
|
||||
|
||||
Vit_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_mlse);
|
||||
[~, errors, ber_mlse_normal, errpos] = calc_ber(Vit_bits.signal, Tx_bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
|
||||
mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_mlse);
|
||||
[~, errors, ber_mlse_normal, errpos] = calc_ber(mlse_bits.signal, Tx_bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
|
||||
fprintf('MLSE: %.2e \n',ber_mlse_normal);
|
||||
|
||||
showLevelHistogram(y_ffe,Symbols,"displayname",'ffe','fignum',111);
|
||||
% showLevelHistogram(y_white,Symbols,"displayname",'ffe','fignum',111);
|
||||
|
||||
%% optimize length
|
||||
|
||||
mu_lms = 0.15;
|
||||
eq = ML_MLSE("epochs_tr",5,"epochs_dd",5,"len_tr",2^14,...
|
||||
"mu_dd",mu_lms,"mu_tr",mu_lms,"order",5,"sps",2,...
|
||||
"traceback_depth",128,"L",2,"delta",0);
|
||||
|
||||
[y_ml_mlse,Vit_signal] = eq.process(Rx_sig_2sps,Symbols);
|
||||
y_ml_mlse_ = y_ml_mlse;
|
||||
y_ml_mlse_.signal = circshift(y_ml_mlse.signal,0);
|
||||
Vit_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse_);
|
||||
[~, errors, ber, errpos] = calc_ber(Vit_bits.signal, Tx_bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
|
||||
fprintf('ML MLSE: %.2e \n',ber);
|
||||
|
||||
bursts = count_error_bursts(errpos, 10);
|
||||
e = zeros(size(Vit_bits.signal));
|
||||
e = zeros(size(mlse_bits.signal));
|
||||
e(errpos) = 1;
|
||||
figure(8)
|
||||
stem(e)
|
||||
|
||||
%% RUN ML-Based MLSE
|
||||
|
||||
mu_lms = 0.15;
|
||||
ml_mlse_equalizer = ML_MLSE("epochs_tr",50,"epochs_dd",10,"len_tr",Rx_sig_2sps.length-100,...
|
||||
"mu_dd",mu_lms,"mu_tr",mu_lms,"order",15,"sps",2,...
|
||||
"traceback_depth",128,"L",3,"delta",5);
|
||||
|
||||
%%
|
||||
ml_mlse_equalizer.epochs_tr = 50;
|
||||
ml_mlse_equalizer.epochs_dd = 1;
|
||||
[y_ml_mlse,Vit_signal] = ml_mlse_equalizer.process(Rx_sig_2sps,Symbols);
|
||||
ml_mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse);
|
||||
[~, errors, ber, errpos] = calc_ber(ml_mlse_bits.signal, Tx_bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
|
||||
fprintf('ML MLSE BER: %.2e \n',ber);
|
||||
|
||||
bursts = count_error_bursts(errpos, 10);
|
||||
e = zeros(size(ml_mlse_bits.signal));
|
||||
e(errpos) = 1;
|
||||
figure(8)
|
||||
stem(e)
|
||||
|
||||
figure()
|
||||
plot(ml_mlse_equalizer.ber)
|
||||
beautifyBERplot
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
%% optimize delta
|
||||
deltas = [-1:4];
|
||||
ber = zeros(1,length(deltas));
|
||||
parfor m = 1:numel(deltas)
|
||||
|
||||
mu_lms = 0.2;
|
||||
eq = ML_MLSE("epochs_tr",2,"epochs_dd",5,"len_tr",2^13,...
|
||||
ml_mlse_equalizer = ML_MLSE("epochs_tr",2,"epochs_dd",5,"len_tr",2^13,...
|
||||
"mu_dd",mu_lms,"mu_tr",mu_lms,"order",4,"sps",1,...
|
||||
"traceback_depth",128,"L",3,"delta",deltas(m));
|
||||
|
||||
[y_ml_mlse,Vit_signal] = eq.process(y_ffe,Symbols);
|
||||
[y_ml_mlse,Vit_signal] = ml_mlse_equalizer.process(y_ffe,Symbols);
|
||||
y_ml_mlse_ = y_ml_mlse;
|
||||
y_ml_mlse_.signal = circshift(y_ml_mlse.signal,0);
|
||||
Vit_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse_);
|
||||
[~, errors, ber(m), errpos] = calc_ber(Vit_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
|
||||
mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse_);
|
||||
[~, errors, ber(m), errpos] = calc_ber(mlse_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
|
||||
fprintf('ML MLSE: %.2e \n',ber(m));
|
||||
end
|
||||
|
||||
|
||||
109
projects/ML_based_MLSE/rate_evaluation.m
Normal file
109
projects/ML_based_MLSE/rate_evaluation.m
Normal file
@@ -0,0 +1,109 @@
|
||||
|
||||
ber_ffe = [];
|
||||
ber_mlse = [];
|
||||
ber_dbtgt = [];
|
||||
ber_ml = [];
|
||||
|
||||
mlse = 1;
|
||||
dbtgt = 1;
|
||||
|
||||
baudrates = [136:8:224].*1e9;
|
||||
parfor i = 1:length(baudrates)
|
||||
|
||||
rop = -8;
|
||||
M = 4;
|
||||
[Rx_sig_2sps_v1, Symbols_v1, Tx_bits_v1] = standard_link_model("M",M,"fsym",baudrates(i),"rop",rop,"laser_linewidth",1300,"link_length_m",0,"random_key",1,"apply_pulsef",0);
|
||||
% [Rx_sig_2sps_v2, Symbols_v2, Tx_bits_v2] = standard_link_model("M",M,"fsym",200e9,"rop",rop,"laser_linewidth",1300,"link_length_m",0,"random_key",2);
|
||||
% [Rx_sig_2sps_v3, Symbols_v3, Tx_bits_v3] = standard_link_model("M",M,"fsym",200e9,"rop",rop,"laser_linewidth",1300,"link_length_m",0,"random_key",3);
|
||||
|
||||
%% FFE + MLSE
|
||||
if mlse
|
||||
pf_ncoeffs = 1;
|
||||
ffe_order = [50, 0, 0];
|
||||
mu_ffe = [0.0001, 0.0008, 0.001];
|
||||
mu_dfe = 0.0004;
|
||||
eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",2^13,"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.005,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
|
||||
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
|
||||
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',0,'trellis_state_mode',2,'debug',0,'DIR',pf_.coefficients);
|
||||
[ffe_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig_2sps_v1, Symbols_v1, Tx_bits_v1, ...
|
||||
"precode_mode", duob_mode,...
|
||||
'showAnalysis', 0, ...
|
||||
"postFFE", [],...
|
||||
"eth_style_symbol_mapping", 0);
|
||||
|
||||
ber_ffe(i) = ffe_results.metrics.BER;
|
||||
ber_mlse(i) = mlse_results.metrics.BER;
|
||||
end
|
||||
|
||||
|
||||
%% FFE DB tgt. + MLSE
|
||||
if dbtgt
|
||||
mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',0,'trellis_state_mode',3);
|
||||
ffe_order = [50, 0, 0];
|
||||
mu_ffe = [0.0001, 0.0008, 0.001];
|
||||
mu_dfe = 0.0004;
|
||||
eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",2^13,"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.005,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
|
||||
|
||||
dbt_results = duobinary_target(eq_, mlse_db_, M, Rx_sig_2sps_v1, Symbols_v1, Tx_bits_v1, ...
|
||||
"precode_mode", duob_mode, ...
|
||||
'showAnalysis', 0,...
|
||||
"postFFE", []);
|
||||
|
||||
ber_dbtgt(i) = dbt_results.metrics.BER;
|
||||
end
|
||||
|
||||
|
||||
%%
|
||||
mu_lms = 0.0005;
|
||||
pf_ncoeffs = 2;
|
||||
eq_ = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",2,"dd_mode",1,"adaption_technique","lms");
|
||||
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
|
||||
|
||||
% FFE
|
||||
[y_ffe, ffe_noise] = eq_.process(Rx_sig_2sps_v1, Symbols_v1);
|
||||
|
||||
|
||||
% Postfilter
|
||||
[y_white,whitened_noise] = pf_.process(y_ffe, ffe_noise);
|
||||
|
||||
%% RUN ML-Based MLSE
|
||||
|
||||
mu_lms = 0.15;
|
||||
ml_mlse_equalizer = ML_MLSE("epochs_tr",30,"epochs_dd",1,"len_tr",2^15,...
|
||||
"mu_dd",mu_lms,"mu_tr",mu_lms,"order",5,"sps",1,...
|
||||
"traceback_depth",128,"L",3,"delta",0);
|
||||
|
||||
[y_ml_mlse,~] = ml_mlse_equalizer.process(y_white,Symbols_v1);
|
||||
ml_mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse);
|
||||
[~, errors, ber_ml(i), errpos] = calc_ber(ml_mlse_bits.signal, Tx_bits_v1.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
|
||||
fprintf('ML MLSE BER: %.2e \n',ber_ml(i));
|
||||
|
||||
% figure(11);hold on
|
||||
% plot(1:numel(ml_mlse_equalizer.ber),ml_mlse_equalizer.ber);
|
||||
% beautifyBERplot;
|
||||
% xlim([1,numel(ml_mlse_equalizer.ber)])
|
||||
|
||||
end
|
||||
|
||||
%%
|
||||
|
||||
figure(6); hold on;
|
||||
if mlse
|
||||
plot(baudrates,ber_ffe,'DisplayName','FFE');
|
||||
plot(baudrates,ber_mlse,'DisplayName','MLSE');
|
||||
end
|
||||
if dbtgt
|
||||
plot(baudrates,ber_dbtgt,'DisplayName','DB tgt');
|
||||
end
|
||||
plot(baudrates,ber_ml,'DisplayName','ML-MLSE');
|
||||
beautifyBERplot;
|
||||
legend
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
95
projects/ML_based_MLSE/rop_evaluation.m
Normal file
95
projects/ML_based_MLSE/rop_evaluation.m
Normal file
@@ -0,0 +1,95 @@
|
||||
|
||||
ber_ffe = [];
|
||||
ber_mlse = [];
|
||||
ber_dbtgt = [];
|
||||
ber_ml = [];
|
||||
|
||||
mlse = 1;
|
||||
dbtgt = 1;
|
||||
|
||||
rops = linspace(-15,-5,12);
|
||||
parfor i = 1:length(rops)
|
||||
|
||||
rop = rops(i);
|
||||
M = 4;
|
||||
[Rx_sig_2sps_v1, Symbols_v1, Tx_bits_v1] = standard_link_model("M",M,"fsym",224e9,"rop",rop,"laser_linewidth",1300,"link_length_m",0,"random_key",1);
|
||||
% [Rx_sig_2sps_v2, Symbols_v2, Tx_bits_v2] = standard_link_model("M",M,"fsym",200e9,"rop",rop,"laser_linewidth",1300,"link_length_m",0,"random_key",2);
|
||||
% [Rx_sig_2sps_v3, Symbols_v3, Tx_bits_v3] = standard_link_model("M",M,"fsym",200e9,"rop",rop,"laser_linewidth",1300,"link_length_m",0,"random_key",3);
|
||||
|
||||
%% FFE + MLSE
|
||||
if mlse
|
||||
pf_ncoeffs = 1;
|
||||
ffe_order = [50, 0, 0];
|
||||
mu_ffe = [0.0001, 0.0008, 0.001];
|
||||
mu_dfe = 0.0004;
|
||||
eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",2^13,"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.005,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
|
||||
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
|
||||
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',0,'trellis_state_mode',2,'debug',0,'DIR',pf_.coefficients);
|
||||
[ffe_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig_2sps_v1, Symbols_v1, Tx_bits_v1, ...
|
||||
"precode_mode", duob_mode,...
|
||||
'showAnalysis', 0, ...
|
||||
"postFFE", [],...
|
||||
"eth_style_symbol_mapping", 0);
|
||||
|
||||
ber_ffe(i) = ffe_results.metrics.BER;
|
||||
ber_mlse(i) = mlse_results.metrics.BER;
|
||||
end
|
||||
|
||||
|
||||
%% FFE DB tgt. + MLSE
|
||||
if dbtgt
|
||||
mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',0,'trellis_state_mode',3);
|
||||
ffe_order = [50, 0, 0];
|
||||
mu_ffe = [0.0001, 0.0008, 0.001];
|
||||
mu_dfe = 0.0004;
|
||||
eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",2^13,"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.005,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
|
||||
|
||||
dbt_results = duobinary_target(eq_, mlse_db_, M, Rx_sig_2sps_v1, Symbols_v1, Tx_bits_v1, ...
|
||||
"precode_mode", duob_mode, ...
|
||||
'showAnalysis', 0,...
|
||||
"postFFE", []);
|
||||
|
||||
ber_dbtgt(i) = dbt_results.metrics.BER;
|
||||
end
|
||||
|
||||
%% RUN ML-Based MLSE
|
||||
|
||||
mu_lms = 0.15;
|
||||
ml_mlse_equalizer = ML_MLSE("epochs_tr",30,"epochs_dd",1,"len_tr",2^14,...
|
||||
"mu_dd",mu_lms,"mu_tr",mu_lms,"order",4,"sps",2,...
|
||||
"traceback_depth",128,"L",2,"delta",0);
|
||||
|
||||
[y_ml_mlse,~] = ml_mlse_equalizer.process(Rx_sig_2sps_v1,Symbols_v1);
|
||||
ml_mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse);
|
||||
[~, errors, ber_ml(i), errpos] = calc_ber(ml_mlse_bits.signal, Tx_bits_v1.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
|
||||
fprintf('ML MLSE BER: %.2e \n',ber_ml(i));
|
||||
|
||||
% figure(11);hold on
|
||||
% plot(1:numel(ml_mlse_equalizer.ber),ml_mlse_equalizer.ber);
|
||||
% beautifyBERplot;
|
||||
% xlim([1,numel(ml_mlse_equalizer.ber)])
|
||||
|
||||
end
|
||||
|
||||
%%
|
||||
|
||||
figure(3); hold on;
|
||||
if mlse
|
||||
plot(rops,ber_ffe,'DisplayName','FFE');
|
||||
plot(rops,ber_mlse,'DisplayName','MLSE');
|
||||
end
|
||||
if dbtgt
|
||||
plot(rops,ber_dbtgt,'DisplayName','DB tgt');
|
||||
end
|
||||
plot(rops,ber_ml,'DisplayName','ML-MLSE');
|
||||
beautifyBERplot;
|
||||
legend
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
95
projects/ML_based_MLSE/standard_link_model.m
Normal file
95
projects/ML_based_MLSE/standard_link_model.m
Normal file
@@ -0,0 +1,95 @@
|
||||
function [Rx_sig_2sps,Symbols,Tx_bits] = standard_link_model(options)
|
||||
|
||||
% STANDARD_LINK_MODEL Basic IM/DD link simulation
|
||||
% Rx_sig_2sps = standard_link_model(...optional args...)
|
||||
%
|
||||
% All arguments are optional and default to standard parameters
|
||||
% if not provided.
|
||||
|
||||
arguments
|
||||
|
||||
% --- Transmitter settings ---
|
||||
options.M (1,1) double = 4
|
||||
options.apply_pulsef (1,1) logical = true
|
||||
options.fdac (1,1) double = 256e9
|
||||
options.fadc (1,1) double = 256e9
|
||||
options.random_key (1,1) double = 2
|
||||
options.rcalpha (1,1) double = 0.05
|
||||
options.kover (1,1) double = 8
|
||||
options.vbias_rel (1,1) double = 0.5
|
||||
options.u_pi (1,1) double = 3.2
|
||||
options.laser_wavelength (1,1) double = 1310
|
||||
options.laser_linewidth (1,1) double = 1e6
|
||||
|
||||
% --- Channel parameters ---
|
||||
options.link_length_m (1,1) double = 0
|
||||
options.rop (1,:) double = -5
|
||||
options.fsym (1,:) double = (212:16:256)*1e9
|
||||
options.doub_mode (1,1) db_mode = db_mode.no_db
|
||||
|
||||
% --- Debug ---
|
||||
options.debug (1,1) logical = false
|
||||
|
||||
end
|
||||
|
||||
% --- Pulse former ---
|
||||
Pform = Pulseformer("fsym",options.fsym,"fdac",4*options.fsym, ...
|
||||
"pulse","rc","pulselength",16,"alpha",options.rcalpha);
|
||||
|
||||
% --- Transmitter source ---
|
||||
[Digi_sig,Symbols,Tx_bits] = PAMsource( ...
|
||||
"fsym",options.fsym,"M",options.M,"order",18,"useprbs",0, ...
|
||||
"fs_out",options.fdac,"applyclipping",0,"clipfactor",1.5, ...
|
||||
"applypulseform",options.apply_pulsef,"pulseformer",Pform, ...
|
||||
"randkey",options.random_key,"db_precode",0,"db_encode",0, ...
|
||||
"mrds_code",0,"mrds_blocklength",512, ...
|
||||
"duobinary_mode",options.doub_mode).process();
|
||||
|
||||
% --- AWG driver ---
|
||||
El_sig = M8199B("kover",options.kover).process(Digi_sig);
|
||||
El_sig = El_sig.normalize("mode","oneone");
|
||||
|
||||
% --- E/O Modulation ---
|
||||
vbias = -options.vbias_rel*options.u_pi;
|
||||
Opt_sig = EML("mode",eml_mode.im_cosinus,"power",3, ...
|
||||
"fsimu",El_sig.fs,"lambda",options.laser_wavelength, ...
|
||||
"bias",vbias,"u_pi",options.u_pi,"linewidth",options.laser_linewidth, ...
|
||||
"randomkey",options.random_key+1).process(El_sig);
|
||||
|
||||
% --- Fiber ---
|
||||
Opt_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",options.link_length_m, ...
|
||||
"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig);
|
||||
|
||||
% --- Amplifier (ROP set) ---
|
||||
Opt_sig = Amplifier("amp_mode","ideal_no_noise", ...
|
||||
"gain_mode","output_power","amplification_db",options.rop).process(Opt_sig);
|
||||
|
||||
% --- Photodiode ---
|
||||
PD_sig = Photodiode("fsimu",options.fdac*options.kover,"dark_current",2e-8, ...
|
||||
"responsivity",1,"temperature",20,"nep",1.8e-11, ...
|
||||
"randomkey",options.random_key).process(Opt_sig);
|
||||
|
||||
% --- Electrical LPF (receiver frontend) ---
|
||||
rx_bwl = 70e9;
|
||||
PD_sig = Filter('filtdegree',4,"f_cutoff",rx_bwl, ...
|
||||
"fs",options.fdac*options.kover,"filterType",filtertypes.butterworth, ...
|
||||
"active",true).process(PD_sig);
|
||||
|
||||
% --- Scope low-pass and sampling ---
|
||||
Lp_scpe = Filter('filtdegree',4,"f_cutoff",110e9,"fs",options.fadc, ...
|
||||
"filterType",filtertypes.butterworth,"active",true);
|
||||
|
||||
Scpe_sig = Scope("fsimu",options.fdac*options.kover,"fadc",options.fadc, ...
|
||||
"delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth, ...
|
||||
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0, ...
|
||||
"samp_jitter",0,"adcresolution",8,"quantbuffer",0.1, ...
|
||||
'block_dc',1,'lpf_active',1,'H_lpf',Lp_scpe).process(PD_sig);
|
||||
|
||||
% --- Downsample to 2 sps ---
|
||||
Scpe_sig_2sps = Scpe_sig.resample("fs_out",2*options.fsym);
|
||||
[~,Scpe_cell,~,found_sync] = Scpe_sig_2sps.tsynch( ...
|
||||
"reference",Symbols,"fs_ref",options.fsym,"debug_plots",0);
|
||||
|
||||
Rx_sig_2sps = Scpe_cell{1}.normalize("mode","rms");
|
||||
|
||||
end
|
||||
Reference in New Issue
Block a user