ML_MLSE (before testing in depth)
minor changes here and there
This commit is contained in:
Silas Oettinghaus
@@ -48,6 +48,11 @@ classdef ML_MLSE < handle
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valid_to_idx
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valid_from_idx
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w
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% --- New: fast state lookup ---
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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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end
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methods
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@@ -79,7 +84,6 @@ classdef ML_MLSE < handle
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obj.e = zeros(obj.order,1);
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obj.error = 0;
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end
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function [X,X_viterbi] = process(obj, X, D)
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@@ -88,33 +92,33 @@ classdef ML_MLSE < handle
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% 1 normalize RMS
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X = X.normalize("mode","rms");
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obj.constellation = unique(D.signal);
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% Use sorted constellation for deterministic mapping
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obj.constellation = sort(unique(D.signal),'ascend');
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obj.nSym = numel(obj.constellation);
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if length(X)/length(D) ~= obj.sps
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warning('Signal length does not fit to reference!');
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end
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% ==============================================================
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% INITIALIZATION (only before final epoch and detection mode)
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% ==============================================================
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% --- Parameters
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obj.S = numel(unique(D.signal)); % alphabet size
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obj.Nf = obj.order*obj.sps; % filter length
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% obj.delta = 3;%ceil(obj.Nf/2); % delay parameter
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obj.S = numel(obj.constellation); % alphabet size
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obj.Nf = obj.order*obj.sps; % filter length
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% obj.delta = 3;%ceil(obj.Nf/2); % delay parameter
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obj.nStates = obj.S^obj.L;
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obj.nFeasible = obj.nStates*obj.S;
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% --- Trellis mapping
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obj.trellis_states = reshape(unique(D.signal),1,[]);
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obj.trellis_states = reshape(obj.constellation,1,[]);
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pre_comb_mat = repmat(obj.trellis_states, obj.L, 1);
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pre_comb_cell = mat2cell(pre_comb_mat, ones(1,obj.L), size(pre_comb_mat,2));
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obj.combs = fliplr(combvec(pre_comb_cell{:}).');
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obj.first_sym = obj.combs(:,1);
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obj.last_sym = obj.combs(:,end);
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obj.nStates = size(obj.combs,1);
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obj.combs = fliplr(combvec(pre_comb_cell{:}).'); % rows: states, columns: [x_k, x_{k-1}, ...]
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obj.first_sym = obj.combs(:,1);
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obj.last_sym = obj.combs(:,end);
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obj.nStates = size(obj.combs,1);
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% --- Valid transitions
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obj.valid = false(obj.nStates);
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@@ -130,10 +134,17 @@ classdef ML_MLSE < handle
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% --- Allocate vectors and weights
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% !! IF SHAPE FIT, then we already have smth there an we want
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% to start with the existing fitler-set
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if all(size(obj.w) ~= [obj.Nf+1,obj.nFeasible])
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if isempty(obj.w) || any(size(obj.w) ~= [obj.Nf+1,obj.nFeasible])
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obj.w = zeros(obj.Nf+1,obj.nFeasible); % filter weights per transition + bias tap
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obj.w = randn(obj.Nf+1,obj.nFeasible);
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end
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% obj.w = randn(obj.Nf+1,obj.nFeasible);
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% --- Precompute dictionary for fast state lookup (sequence -> state)
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keys = cell(obj.nStates,1);
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for i = 1:obj.nStates
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keys{i} = obj.seq_key(obj.combs(i,:)); % combs row is already [x_k, x_{k-1}, ...]
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end
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obj.state_dict = containers.Map(keys, 1:obj.nStates);
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% ==============================================================
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% TRAINING
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@@ -165,8 +176,6 @@ classdef ML_MLSE < handle
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X_viterbi.fs = D.fs; %change sampling frequency of outgoing signal from fdac e.g. 2 sps to symbol spaced = fsym
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lbdesc = [num2str(obj.order),'order FFE + PF + Viterbi'];
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X_viterbi = X_viterbi.logbookentry(lbdesc); % append to logbook
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end
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function [y,y_vit] = equalize(obj,x,d,mu,epochs,N,training)
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@@ -174,24 +183,34 @@ classdef ML_MLSE < handle
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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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% --- Input padding and preallocation
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y = zeros(N,1);
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% number of symbol steps in this block
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nSymbols = ceil(N/obj.sps);
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for epoch = 1:epochs
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pm = zeros(obj.nStates,1);
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% state metrics (log-domain costs): keep as column [nStates×1]
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pm = zeros(obj.nStates,1); % v_{k-1}(s′)
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c_hat = zeros(1,obj.nFeasible);
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v_tilde = zeros(1,obj.nFeasible);
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pred = zeros(N, obj.nStates, 'uint32');
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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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else
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true_to_state_idx = 1;
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end
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% ==============================================================
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% RUNTIME LOOP
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% ==============================================================
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symbol = 0;
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for sample = 1:obj.sps:N
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symbol = symbol + 1;
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k = symbol;
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% --- Build Δ-delayed observation window y_k
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@@ -201,80 +220,66 @@ classdef ML_MLSE < handle
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padL = max(0,1 - i1);
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padR = max(0,i2 - length(x));
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yk = [zeros(padL,1); buf(:); zeros(padR,1)]; % Nf×1
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yk = [yk;1];
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yk = [yk;1];
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% --- Predict branch metrics for all feasible transitions
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% --- Predict branch metrics for all feasible transitions: c_hat
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c_hat = (yk.' * obj.w); % [1×nFeasible]
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c_hat = c_hat.'; % [nFeasible×1]
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% --- Extended path metrics
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% --- Extended path metrics: v_tilde = pm(from) + c_hat
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% normalize pm to avoid growth (invariant to additive const)
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pm = pm - min(pm);
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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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% for current symbol index k -> previous (k-1) and current (k)
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if k > obj.L
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prev_seq = d(k-obj.L:k-1); % previous state symbols
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true_from_state_idx = find(ismember(obj.combs, flip(prev_seq).', 'rows'));% find state indices in trellis
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% shift-register: previous "to" becomes current "from"
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true_from_state_idx = true_to_state_idx;
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%if ~(true_from_state_idx == true_to_state_idx), warning('Impossible state transition?!'), end
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curr_seq = d(k-obj.L+1:k); % next state symbols
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true_to_state_idx = find(ismember(obj.combs, flip(curr_seq).', 'rows'));% find state indices in trellis
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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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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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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 = 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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end
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% true_from and true_to are (1) -> (-1)
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% thus dirac = [1,0,0,0]'
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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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if sum(dirac) == 0
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warning('whats happening?!');
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end
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% softmax over -v_tilde, one-hot target t
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% 4 feasible transitions: [0,0][0,1][1,0][1,1] #not in
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% order here!
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% first round:
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% v_tilde is zero
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% -> exp(-0) = 1
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% -> 1/(1+1+1+1)
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% -> 1/4
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% -> each transition is equally likely?
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% p = exp(-v_tilde);
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p = exp(-(v_tilde-max(v_tilde)));
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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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% 1-0.25 = 0.75
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% 0-0.25 = -0.25
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% what happens here at the zero? Is this some log
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% probability - larger zero is likely; lower zero is
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% unlikely? But this is based on known information (training)
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dmp = (dirac - p)';
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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); % numerical stability
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probs_lin = exp(-v_norm);
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probs_lin = probs_lin ./ sum(probs_lin);
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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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% --- Map back into nStates×nStates grid
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probs_mat = nan(obj.nStates, obj.nStates);
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probs_mat(obj.valid) = probs_lin; % linear-space probabilities
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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_logmat(obj.valid) = probs_log; % log-domain scores
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probs_mat(obj.valid) = probs_lin;
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probs_logmat(obj.valid) = probs_log;
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% --- Identify the current true transition
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[to_idx, from_idx] = find(obj.valid);
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@@ -282,51 +287,37 @@ classdef ML_MLSE < handle
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cur_to = to_idx(cur_idx);
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cur_from = from_idx(cur_idx);
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% --- Plot using imagesc (supports hold)
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figure(11); clf;
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imagesc(probs_logmat);
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axis xy; % origin top-left
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% colormap(parula);
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colorbar;
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xlabel('From state');
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ylabel('To state');
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set(gca,'FontSize',10);
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% --- Overlay current transition
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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', ...
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'MarkerSize', 10, 'LineWidth', 2, 'MarkerFaceColor', 'none');
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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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end
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if 1 %training
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% the correct state gets a high update, we weight this
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% with the input signal, from here the signals are not
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% "understandable"
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% dmp is large for the correct transition to update
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% only this one!
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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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% from paper: We have observed in simulations that ignoring the derivative of vk−1(s′) during training yields negligible loss after convergence.
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% my note: so the update direction is the same for b and w?
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%only start with updates when we are inside the signal
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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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% actual filter training updates:
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obj.w = obj.w - mu * dL_Dw;% Nf×nFeasible
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obj.w = obj.w - mu * dL_Dw; % (Nf+1)×nFeasible
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end
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end
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% compare select
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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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[pm, pred(k,:)] = min(v_tilde_mat, [], 2);
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[pm_next, pred(k,:)] = min(v_tilde_mat, [], 2);
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% re-center to keep metrics bounded (decision-invariant)
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pm_next = pm_next - min(pm_next);
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pm = pm_next;
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pm_sto(:,symbol) = pm;
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end
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[~, s_end] = max(pm);
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% --- Traceback (full; you can window with traceback_depth if desired)
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[~, s_end] = min(pm);
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viterbi_path = zeros(symbol,1,'uint32');
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viterbi_path(symbol) = s_end;
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for n = symbol:-1:2
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@@ -334,12 +325,8 @@ classdef ML_MLSE < handle
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end
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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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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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@@ -357,12 +344,20 @@ classdef ML_MLSE < handle
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title('Path Metrics (v_tilde)')
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subplot(2,2,4);
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% scatter(1:N,pm_sto,1,'.')
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plot(1:symbol,pm_sto)
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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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end
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end
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end
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end
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methods (Access=private)
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function k = seq_key(obj, seq)
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% Build a stable key string for a sequence row vector in the *same order as combs rows* ([x_k, x_{k-1}, ...])
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% Use rounding via sprintf to avoid floating-point issues.
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% seq must be a row vector.
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k = sprintf(obj.key_fmt, seq);
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end
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end
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end
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@@ -1,4 +1,7 @@
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function beautifyBERplot()
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function beautifyBERplot(options)
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arguments
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options.logscale = 1
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end
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% BEAUTIFYBERPLOT Enhances a BER plot for publication-quality figures.
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% Set line properties for all current plot lines
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@@ -26,8 +29,10 @@ function beautifyBERplot()
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% Set logarithmic scale for y-axis, but only if it makes sense.
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% If this is not always desired, you could condition this on the presence of lines or data.
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set(gca, 'YScale', 'log');
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if options.logscale
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set(gca, 'YScale', 'log');
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end
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% Customize grid and box appearance
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set(gca, 'Box', 'on', 'LineWidth', 0.8); % Thicker border
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grid on;
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@@ -5,7 +5,7 @@ db = DBHandler("dataBase", [dataBase], "type", database_type);
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M = 4;
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fp = QueryFilter();
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% fp.where('Runs', 'run_id','EQUALS', 987);
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fp.where('Runs', 'pam_level','EQUALS', M);
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% fp.where('Runs', 'pam_level','EQUALS', M);
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% fp.where('Runs', 'symbolrate','EQUALS', 150e9);
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% fp.where('Runs', 'fiber_length','EQUALS', 10);
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fp.where('Runs', 'is_mpi','EQUALS', 0);
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@@ -20,10 +20,11 @@ fields = db.getTableFieldNames('power_state_info');
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fields = [fields; db.getTableFieldNames('dashboard_ungrouped_new')];
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[dataTable,~] = db.queryDB(fp, fields);
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%%
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cfg = struct;
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cfg.x_axis = 'accumulated_dispersion'; % 'symbolrate' | 'baudrate' | 'bitrate' | 'wavelength'
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cfg.y_axis = 'BER'; % 'BER' | 'GMI' | 'AIR' | ...
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cfg.group_by = {'equalizer_structure','pre_emph'};
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cfg.x_axis = 'wavelength'; % 'symbolrate' | 'baudrate' | 'bitrate' | 'wavelength'
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cfg.y_axis = 'power_mzm'; % 'BER' | 'GMI' | 'AIR' | ...
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cfg.group_by = {};
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cfg.filters = struct('is_mpi',0,'pam_level',M,'equalizer_structure',[equalizer_structure.vnle,equalizer_structure.ffe,equalizer_structure.vnle_pf_mlse,equalizer_structure.vnle_db_mlse,equalizer_structure.dfe]);
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cfg.y_scale = 'auto'; % auto -> log for BER*, linear otherwise
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181
projects/IMDD_base_system/model_linewidth_evaluation.m
Normal file
181
projects/IMDD_base_system/model_linewidth_evaluation.m
Normal file
@@ -0,0 +1,181 @@
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%%% Run parameters
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% TX
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M = 4;
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apply_pulsef = 1;
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fdac = 256e9;
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fadc = 256e9;
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random_key = 2;
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rcalpha = 0.05;
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kover = 8;
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vbias_rel = 0.5;
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u_pi = 3.2;
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vbias = -vbias_rel*u_pi;
|
||||
laser_wavelength = 1300;
|
||||
laser_linewidth = logspace(0,6.2,24);
|
||||
|
||||
% Channel
|
||||
link_length = 10;
|
||||
|
||||
alpha = 0;
|
||||
|
||||
doub_mode = db_mode.no_db;
|
||||
cols = linspecer(6);
|
||||
rop = [-6];
|
||||
bwl = [0.5:0.1:1.5];
|
||||
fsym = [200:16:256].*1e9;
|
||||
% nonlin_mod = [0.5:0.01:0.75];
|
||||
fsym = ones(size(laser_linewidth)).*fsym(1);
|
||||
nonlin_mod = ones(size(laser_linewidth)).*0.5;
|
||||
|
||||
ffe_results = {};
|
||||
mlse_results_lin= {};
|
||||
|
||||
parfor r = 1:length(laser_linewidth)
|
||||
|
||||
Pform = Pulseformer("fsym",fsym(r),"fdac",4*fsym(r),"pulse","rc","pulselength",16,"alpha",rcalpha);
|
||||
|
||||
db_precode = 0;
|
||||
db_encode = 0;
|
||||
duob_mode = db_mode.no_db;
|
||||
apply_pulsef = 1;
|
||||
|
||||
[Digi_sig,Symbols,Tx_bits] = PAMsource(...
|
||||
"fsym",fsym(r),"M",M,"order",18,"useprbs",1,...
|
||||
"fs_out",fdac,...
|
||||
"applyclipping",0,"clipfactor",1.5,...
|
||||
"applypulseform",apply_pulsef,"pulseformer",Pform,...
|
||||
"randkey",random_key,...
|
||||
"db_precode",db_precode,"db_encode",db_encode,...
|
||||
"mrds_code",0,"mrds_blocklength",512,"duobinary_mode",duob_mode).process();
|
||||
|
||||
El_sig = M8199B("kover",kover).process(Digi_sig);
|
||||
|
||||
%%%%% Electrical Driver Amplifier %%%%%%
|
||||
El_sig = El_sig.normalize("mode","oneone");
|
||||
|
||||
%%%%% MODULATE E/O CONVERSION %%%%%
|
||||
u_pi = 3.2;
|
||||
vbias = -u_pi*nonlin_mod(r);
|
||||
[Opt_sig] = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",laser_wavelength,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth(r),"randomkey",random_key+1).process(El_sig);
|
||||
|
||||
%%%%%% Fiber %%%%%%
|
||||
mpi = 1;
|
||||
if mpi
|
||||
Combined_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",link_length/1000,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig);
|
||||
else
|
||||
|
||||
% 2) ping pong fiber propagation
|
||||
mpi_path = 00;
|
||||
Interference_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",mpi_path*2,"alpha",0,"D",0,"lambda0",1310,"gamma",0).process(Opt_sig);
|
||||
|
||||
Interference_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",-30).process(Interference_sig);
|
||||
|
||||
[Main_sig,dly] = Opt_sig.delay("delay_meter",mpi_path*2);
|
||||
|
||||
% Add
|
||||
Combined_sig = Main_sig + Interference_sig;
|
||||
|
||||
% Cut (due to the delays there is a jump in the signals)
|
||||
if dly == 0;dly = 1;end
|
||||
Combined_sig.signal = Combined_sig.signal(ceil(dly):end);
|
||||
|
||||
% Fiber
|
||||
Combined_sig = Fiber("fsimu",Combined_sig.fs,"fiber_length",2,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.08).process(Combined_sig);
|
||||
end
|
||||
|
||||
|
||||
%%%%%% ROP %%%%%%
|
||||
Opt_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",rop).process(Combined_sig);
|
||||
|
||||
%%%%%% PD Square Law %%%%%%
|
||||
PD_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11,"randomkey",random_key).process(Opt_sig);
|
||||
|
||||
%%%%%% Low-pass RX (PD, El. Connectors and Scope %%%%%%
|
||||
rx_bwl = 70e9;
|
||||
PD_sig = Filter('filtdegree',4,"f_cutoff",rx_bwl,"fs",fdac*kover,"filterType",filtertypes.butterworth,"active",true).process(PD_sig);
|
||||
|
||||
% %%%%%% Low-pass Scope %%%%%%
|
||||
Lp_scpe = Filter('filtdegree',4,"f_cutoff",110e9,"fs",fadc,"filterType",filtertypes.butterworth,"active",true);
|
||||
|
||||
%%%%%% Scope %%%%%%
|
||||
Scpe_sig = Scope("fsimu",fdac*kover,"fadc",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);
|
||||
|
||||
Scpe_sig_2sps = Scpe_sig.resample("fs_out",2*fsym(r));
|
||||
% Symbols.signal = Symbols.signal(1:Scpe_sig_2sps.length/2);
|
||||
|
||||
% 2sps
|
||||
[~, Scpe_cell, ~, found_sync] = Scpe_sig_2sps.tsynch("reference", Symbols, "fs_ref", fsym(r), "debug_plots", 0);
|
||||
Rx_sig_2sps = Scpe_cell{1};
|
||||
Rx_sig_2sps = Rx_sig_2sps.normalize("mode","rms");
|
||||
|
||||
% 1sps
|
||||
Scpe_sig_1sps = Scpe_sig.resample("fs_out",1*fsym(r));
|
||||
|
||||
[~, Scpe_cell_1sps, ~, found_sync] = Scpe_sig_1sps.tsynch("reference", Symbols, "fs_ref", fsym(r), "debug_plots", 0);
|
||||
Rx_sig_1sps = Scpe_cell_1sps{1};
|
||||
Rx_sig_1sps = Rx_sig_1sps.normalize("mode","rms");
|
||||
|
||||
|
||||
%% RUN DSP
|
||||
len_tr = 4096*2;
|
||||
|
||||
mu_ffe1 = 0.0001;
|
||||
mu_ffe2 = 0.0008;
|
||||
mu_ffe3 = 0.001;
|
||||
mu_dc = 0.005;
|
||||
% mu_dc = 0;
|
||||
|
||||
mu_ffe = [mu_ffe1 mu_ffe2 mu_ffe3];
|
||||
mu_dfe = 0.0004;
|
||||
pf_ncoeffs = 1;
|
||||
ffe_order = [50, 3, 3];
|
||||
mu_lms = 0.0005;
|
||||
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");
|
||||
% eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",1,"DCmu",0.00,"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',0,'trellis_exclusion',0,'trellis_state_mode',2,'debug',0);
|
||||
|
||||
[ffe_results{r}, mlse_results_lin{r}] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig_2sps, Symbols, Tx_bits, ...
|
||||
"precode_mode", duob_mode,...
|
||||
'showAnalysis', 0, ...
|
||||
"postFFE", [],...
|
||||
"eth_style_symbol_mapping", 0);
|
||||
|
||||
|
||||
ffe_results{r}.metrics.print;
|
||||
mlse_results_lin{r}.metrics.print;
|
||||
|
||||
end
|
||||
|
||||
|
||||
figure(1);hold on;
|
||||
plot(laser_linewidth.*1e-6,cellfun(@(x) x.metrics.BER, ffe_results),'DisplayName','VNLE')
|
||||
plot(laser_linewidth.*1e-6,cellfun(@(x) x.metrics.BER, mlse_results_lin),'DisplayName','VNLE+MLSE')
|
||||
xlabel('Linewidth [GHz]');
|
||||
ylabel('BER')
|
||||
set(gca,'YScale','log');
|
||||
legend;
|
||||
ylim([1e-5 1e-1]);
|
||||
beautifyBERplot;
|
||||
|
||||
figure();hold on;
|
||||
plot(laser_linewidth.*1e-6,cellfun(@(x) x.metrics.AIR.*1e-9, ffe_results),'DisplayName','FFE')
|
||||
plot(laser_linewidth.*1e-6,cellfun(@(x) x.metrics.AIR.*1e-9, mlse_results_lin),'DisplayName','FFE+MLSE')
|
||||
xlabel('Linewidth [GHz]');
|
||||
ylabel('AIR [GBd]')
|
||||
% set(gca,'YScale','log');
|
||||
legend;
|
||||
beautifyBERplot("logscale",0);
|
||||
|
||||
figure(); hold on
|
||||
stem(calcWavelengthPlan(16,400e9,1310),ones(16,1),'DisplayName','16x400','Marker','.','LineWidth',1);
|
||||
stem(calcWavelengthPlan(8,800e9,1310),ones(8,1),'DisplayName','8x800','Marker','.','LineWidth',1);
|
||||
stem(calcWavelengthPlan(16,800e9,1310),ones(16,1),'DisplayName','16x800','Marker','.','LineWidth',1);
|
||||
ylim([0,1.2]);
|
||||
ylabel('wavelength [nm]');
|
||||
xlim([1270, 1350])
|
||||
@@ -332,12 +332,14 @@ for r = 1:length(fsym)
|
||||
plot_stuff = 0;
|
||||
gmi_mlse_pr_tgt_ = zeros(size(alpha_vec));
|
||||
ber_mlse_pr_tgt_ = zeros(size(alpha_vec));
|
||||
parfor a = 1:numel(alpha_vec)
|
||||
for a = 1:numel(alpha_vec)
|
||||
|
||||
alpha_vec(a) = 0.9;
|
||||
eq_ = EQ("Ne",[vnle_order1,vnle_order2,vnle_order3],"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.00,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
|
||||
Symbols_filt = Symbols.filter([1,alpha_vec(a)],1);
|
||||
[eq_signal_prtgt, eq_noise] = eq_.process(Rx_sig, Symbols_filt);
|
||||
|
||||
showLevelHistogram(eq_signal_prtgt,Symbols_filt,"displayname",'VNLE Out','fignum',201);
|
||||
|
||||
if plot_stuff
|
||||
% Plot the response for respective EQ targets
|
||||
|
||||
213
projects/ML_based_MLSE/minimal_model_gpt.m
Normal file
213
projects/ML_based_MLSE/minimal_model_gpt.m
Normal file
@@ -0,0 +1,213 @@
|
||||
function minimal_model_gpt
|
||||
% Minimal binary IM/DD + ISI (M=3), AWGN. RX: ML-based MLSE vs classic MLSE
|
||||
% Fixes:
|
||||
% 1) Correct (s',s) labeling: s'=[x_{k-1},x_{k-2}], s=[x_k,x_{k-1}]
|
||||
% 2) Window standardization (z-score) from pilot
|
||||
% 3) Non-negative learned BM: c_hat = (w^T y_std + b).^2
|
||||
% 4) Stable training loop bounds; BER length alignment
|
||||
|
||||
clear; clc; rng(1);
|
||||
|
||||
%% Parameters
|
||||
Ktr = 4000; % pilot
|
||||
Kte = 20000; % test
|
||||
M = 3; % true channel memory
|
||||
L = 3; % MLSE memory (states keep L-1 symbols)
|
||||
h = [0.55 1.00 0.45]; % ISI FIR
|
||||
EsN0dB= 12;
|
||||
D = 5*L; % traceback (unused here; full TB)
|
||||
Nwin = 7; % BM feature window
|
||||
Delta = 0;
|
||||
|
||||
%% TX, channel, noise
|
||||
map = @(b) 2*b-1;
|
||||
b_tr = randi([0 1],Ktr,1); x_tr = map(b_tr);
|
||||
b_te = randi([0 1],Kte,1); x_te = map(b_te);
|
||||
|
||||
convpad = @(x) filter(h,1,[x; zeros(M-1,1)]);
|
||||
ytr_clean = convpad(x_tr);
|
||||
yte_clean = convpad(x_te);
|
||||
|
||||
EsN0 = 10^(EsN0dB/10);
|
||||
sigma2 = 1/(2*EsN0);
|
||||
awgn = @(len) sqrt(sigma2)*randn(len,1);
|
||||
y_tr = ytr_clean + awgn(length(ytr_clean));
|
||||
y_te = yte_clean + awgn(length(yte_clean));
|
||||
|
||||
% remove tail to keep equal lengths
|
||||
trim = M-1;
|
||||
y_tr = y_tr(1:end-trim); x_tr = x_tr(1:end-trim); b_tr = b_tr(1:end-trim);
|
||||
y_te = y_te(1:end-trim); x_te = x_te(1:end-trim); b_te = b_te(1:end-trim);
|
||||
|
||||
%% Trellis
|
||||
nStates = 2^(L-1);
|
||||
statesBin = de2bi(0:nStates-1,L-1,'left-msb');
|
||||
symOfBit = @(bit) (2*bit-1);
|
||||
|
||||
% transitions (s'->s) with input symbol u in {±1}
|
||||
trans = struct('sp',[],'s',[],'u',[],'ubit',[]);
|
||||
idx=1;
|
||||
for sp=1:nStates
|
||||
prev_bits = statesBin(sp,:); % [x_{k-1}, x_{k-2}] as bits
|
||||
for ubit=[0 1]
|
||||
u = symOfBit(ubit);
|
||||
nb = [prev_bits(1),ubit]; % new state s = [x_k, x_{k-1}]
|
||||
s = bi2de(nb,'left-msb')+1;
|
||||
trans(idx).sp=sp; trans(idx).s=s; trans(idx).u=u; trans(idx).ubit=ubit;
|
||||
idx=idx+1;
|
||||
end
|
||||
end
|
||||
nTrans = numel(trans);
|
||||
|
||||
%% Classic BM (Gaussian)
|
||||
BM_classic = @(yk, sp, s, u) (( yk - h * [u, symOfBit(statesBin(sp,:))]')^2)/(2*sigma2);
|
||||
|
||||
%% ML-based BM: c_hat = (w^T y_std + b)^2 (nonnegative)
|
||||
padN = Nwin-1;
|
||||
pad = @(y) [zeros(Delta+padN,1); y; zeros(max(0,Nwin-1-Delta),1)];
|
||||
y_tr_p = pad(y_tr); y_te_p = pad(y_te);
|
||||
|
||||
% standardize window features using pilot
|
||||
Ytr_mat = im2col_sliding(y_tr_p, Nwin, Delta); % Nwin × T
|
||||
mu_win = mean(Ytr_mat,2);
|
||||
sd_win = std(Ytr_mat,0,2)+1e-8;
|
||||
|
||||
W = zeros(Nwin,nTrans);
|
||||
b0= zeros(1,nTrans);
|
||||
softmax = @(z) exp(z - max(z))./sum(exp(z - max(z)));
|
||||
mu = 0.005; % LR
|
||||
nEpoch = 6; % light training
|
||||
Ttr = length(y_tr);
|
||||
|
||||
for ep=1:nEpoch
|
||||
v = zeros(nStates,1); v(2:end)=Inf;
|
||||
for k = L : min(Ttr - (L-1), Ttr) % need x(k-1), x(k-2)
|
||||
% true (s',s):
|
||||
sp_bits = [(x_tr(k-1)<0), (x_tr(k-2)<0)];
|
||||
s_bits = [(x_tr(k) <0), (x_tr(k-1)<0)];
|
||||
sp_star = bi2de(sp_bits,'left-msb')+1;
|
||||
s_star = bi2de(s_bits ,'left-msb')+1;
|
||||
|
||||
% feature window (z-scored)
|
||||
yw = y_tr_p(k-Delta : k-Delta+Nwin-1);
|
||||
yw = (yw - mu_win) ./ sd_win;
|
||||
|
||||
% compute extended PM logits over all (s',s)
|
||||
vtil = -inf(nTrans,1);
|
||||
for t=1:nTrans
|
||||
z = W(:,t).'*yw + b0(t);
|
||||
c_hat = z*z; % (.)^2 nonnegative BM
|
||||
vtil(t) = -( v(trans(t).sp) + c_hat);
|
||||
end
|
||||
pext = softmax(vtil).';
|
||||
t_star = find([trans.sp]==sp_star & [trans.s]==s_star,1);
|
||||
|
||||
% CE gradient wrt c_hat, chain rule through square
|
||||
delta = pext; delta(t_star)=delta(t_star)-1; delta = -delta; % sign for vtil=-(...)
|
||||
for t=1:nTrans
|
||||
z = W(:,t).'*yw + b0(t);
|
||||
dc_dz = 2*z; % d(z^2)/dz
|
||||
g = delta(t) * dc_dz; % ∂L/∂z
|
||||
W(:,t) = W(:,t) - mu * g * yw;
|
||||
b0(t) = b0(t) - mu * g;
|
||||
end
|
||||
|
||||
% PM update for next step
|
||||
v_next = inf(nStates,1);
|
||||
for t=1:nTrans
|
||||
sp=trans(t).sp; s=trans(t).s;
|
||||
z = W(:,t).'*yw + b0(t);
|
||||
c_hat = z*z;
|
||||
cand = v(sp)+c_hat;
|
||||
if cand < v_next(s)
|
||||
v_next(s)=cand;
|
||||
end
|
||||
end
|
||||
v = v_next;
|
||||
end
|
||||
end
|
||||
|
||||
%% Decode (full-length traceback)
|
||||
[xhat_ml, bhat_ml] = viterbi_decode(y_te, L, trans, @(k) feat_std(y_te_p,k,Nwin,Delta,mu_win,sd_win), ...
|
||||
@(yk,sp,s,u) learnedBM(W,b0,yk,sp,s,u), 0);
|
||||
[xhat_ex, bhat_ex] = viterbi_decode(y_te, L, trans, @(k) y_te(k), ...
|
||||
@(yk,sp,s,u) BM_classic(yk,sp,s,u), 0);
|
||||
|
||||
%% BER (align by min length)
|
||||
Lmin = min([length(bhat_ml), length(bhat_ex), length(b_te)]);
|
||||
BER_ml = mean(bhat_ml(1:Lmin) ~= (b_te(1:Lmin)>0));
|
||||
BER_ex = mean(bhat_ex(1:Lmin) ~= (b_te(1:Lmin)>0));
|
||||
fprintf('BER (ML-based MLSE): %.3e\n', BER_ml);
|
||||
fprintf('BER (classic MLSE ): %.3e\n', BER_ex);
|
||||
end
|
||||
|
||||
% ----------------- helpers -----------------
|
||||
function Y = im2col_sliding(y_pad, Nwin, Delta)
|
||||
T = length(y_pad) - (Nwin-1) - Delta;
|
||||
Y = zeros(Nwin,T);
|
||||
for k=1:T
|
||||
Y(:,k) = y_pad(k-Delta : k-Delta+Nwin-1);
|
||||
end
|
||||
end
|
||||
|
||||
function yw = feat_std(y_pad,k,Nwin,Delta,mu_win,sd_win)
|
||||
yw = y_pad(k-Delta : k-Delta+Nwin-1);
|
||||
yw = (yw - mu_win) ./ sd_win;
|
||||
end
|
||||
|
||||
function c = learnedBM(W,b0,yw,sp,s,~)
|
||||
% pick parameters of the (sp->s) transition
|
||||
persistent map;
|
||||
if isempty(map)
|
||||
% build once: index of W/b0 for each (sp,s)
|
||||
nStates = size(W,1)*0+1; %#ok<NASGU>
|
||||
end
|
||||
% linear scan is fine at this size:
|
||||
% (use first match of (sp,s))
|
||||
c = inf;
|
||||
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
|
||||
@@ -22,9 +22,9 @@ alpha = 0;
|
||||
|
||||
doub_mode = db_mode.no_db;
|
||||
cols = linspecer(6);
|
||||
rop = [-6];
|
||||
rop = [-5];
|
||||
bwl = [0.5:0.1:1.5];
|
||||
fsym = [208:16:256].*1e9;
|
||||
fsym = [212:16:256].*1e9;
|
||||
% nonlin_mod = [0.5:0.01:0.75];
|
||||
nonlin_mod = ones(size(fsym)).*0.5;
|
||||
|
||||
@@ -41,7 +41,7 @@ for r = 1:length(fsym)
|
||||
apply_pulsef = 1;
|
||||
|
||||
[Digi_sig,Symbols,Tx_bits] = PAMsource(...
|
||||
"fsym",fsym(r),"M",M,"order",17,"useprbs",0,...
|
||||
"fsym",fsym(r),"M",M,"order",18,"useprbs",0,...
|
||||
"fs_out",fdac,...
|
||||
"applyclipping",0,"clipfactor",1.5,...
|
||||
"applypulseform",apply_pulsef,"pulseformer",Pform,...
|
||||
@@ -94,59 +94,87 @@ for r = 1:length(fsym)
|
||||
[~, Scpe_cell_1sps, ~, found_sync] = Scpe_sig_1sps.tsynch("reference", Symbols, "fs_ref", fsym(r), "debug_plots", 1);
|
||||
Rx_sig_1sps = Scpe_cell_1sps{1};
|
||||
Rx_sig_1sps = Rx_sig_1sps.normalize("mode","rms");
|
||||
showLevelHistogram(Rx_sig_1sps,Symbols,"displayname",'ffe','fignum',111);
|
||||
|
||||
|
||||
%% Implement DSP directly here:
|
||||
%%
|
||||
|
||||
mu_lms = 0.0005;
|
||||
% tic
|
||||
% eq = FFE_MLSE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",2,"traceback_depth",32,"L",1);
|
||||
% [Eq_signal,Vit_signal] = eq.process(Rx_sig_2sps,Symbols);
|
||||
% toc
|
||||
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, Symbols);
|
||||
|
||||
Eq_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ffe);
|
||||
[~, errors, ber_ffe, ~] = calc_ber(Eq_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
|
||||
fprintf('FFE: %.2e \n',ber_ffe);
|
||||
|
||||
tic
|
||||
eq = ML_MLSE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",2,"traceback_depth",32,"L",1);
|
||||
[Eq_signal,Vit_signal] = eq.process(Rx_sig_2sps,Symbols);
|
||||
toc
|
||||
|
||||
Eq_bits = PAMmapper(M, 0, "eth_style", 0).demap(Eq_signal);
|
||||
[~, errors, ber, ~] = calc_ber(Eq_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
|
||||
fprintf('FFE: %.2e \n',ber);
|
||||
% Postfilter
|
||||
[y_white,whitened_noise] = pf_.process(y_ffe, ffe_noise);
|
||||
|
||||
Vit_signal_ = Vit_signal;
|
||||
Vit_signal_.signal = circshift(Vit_signal.signal,0);
|
||||
Vit_bits = PAMmapper(M, 0, "eth_style", 0).demap(Vit_signal_);
|
||||
[~, errors, ber, errpos] = calc_ber(Vit_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
|
||||
fprintf('Viterbi: %.2e \n',ber);
|
||||
% 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);
|
||||
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(errpos) = 1;
|
||||
figure(8)
|
||||
stem(e)
|
||||
|
||||
%% 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,...
|
||||
"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_ = 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);
|
||||
fprintf('ML MLSE: %.2e \n',ber(m));
|
||||
end
|
||||
|
||||
|
||||
figure(7); hold on
|
||||
title('ML MLSE')
|
||||
plot(deltas,ber,'DisplayName','BER');
|
||||
yline(ber_ffe,'DisplayName','BER FFE');
|
||||
yline(ber_mlse_normal,'DisplayName','BER MLSE');
|
||||
xlabel('deltas')
|
||||
beautifyBERplot
|
||||
legend
|
||||
ylim([1e-5, 1e-1]);
|
||||
set(gca,'YScale','log');
|
||||
|
||||
|
||||
%% optimize smth.
|
||||
tr_len = 2.^[2:15];
|
||||
tr_len = floor(tr_len);
|
||||
ber = zeros(size(tr_len));
|
||||
for m = 1:numel(tr_len)
|
||||
|
||||
mu_lms = 0.0005;
|
||||
eq = ML_MLSE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",2,"traceback_depth",tr_len(m));
|
||||
[Eq_signal,Vit_signal] = eq.process(Rx_sig_2sps,Symbols);
|
||||
|
||||
% Eq_bits = PAMmapper(M, 0, "eth_style", 0).demap(Eq_signal);
|
||||
% [~, errors, ber(m), ~] = calc_ber(Eq_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
|
||||
|
||||
Vit_bits = PAMmapper(M, 0, "eth_style", 0).demap(Vit_signal);
|
||||
[~, errors, ber(m), errpos] = calc_ber(Vit_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
|
||||
end
|
||||
|
||||
figure(5); hold on
|
||||
title('1-SPS')
|
||||
plot(tr_len,ber,'DisplayName','BER');
|
||||
xlabel('Traceback Length')
|
||||
beautifyBERplot
|
||||
legend
|
||||
ylim([1e-4, 1e-1]);
|
||||
set(gca,'YScale','log');
|
||||
|
||||
|
||||
|
||||
%% RUN Comparison
|
||||
@@ -163,12 +191,12 @@ for r = 1:length(fsym)
|
||||
pf_ncoeffs = 1;
|
||||
ffe_order = [50, 0, 0];
|
||||
mu_lms = 0.0005;
|
||||
eq_ = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",1,"dd_mode",1,"adaption_technique","lms");
|
||||
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");
|
||||
% eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",1,"DCmu",0.00,"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',0,'trellis_exclusion',0,'trellis_state_mode',2,'debug',0);
|
||||
|
||||
[ffe_results{r}, mlse_results_lin{r}] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig_1sps, Symbols, Tx_bits, ...
|
||||
[ffe_results{r}, mlse_results_lin{r}] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig_2sps, Symbols, Tx_bits, ...
|
||||
"precode_mode", duob_mode,...
|
||||
'showAnalysis', 0, ...
|
||||
"postFFE", [],...
|
||||
|
||||
Reference in New Issue
Block a user