// Advanced DBA Implementation for 50G-PON Systems
// Python implementation with ML-enhanced prediction

import numpy as np
from sklearn.ensemble import RandomForestRegressor
from collections import deque
import time

class AdvancedDBA:
    def __init__(self, num_onus=128, frame_time=125e-6):
        self.num_onus = num_onus
        self.frame_time = frame_time  # 125 microseconds
        self.onus = [ONU(i) for i in range(num_onus)]
        self.bandwidth_predictor = RandomForestRegressor(n_estimators=100)
        self.traffic_history = deque(maxlen=1000)
        
    def calculate_bandwidth_allocation(self):
        """
        Main DBA algorithm implementing weighted round-robin with 
        traffic prediction and QoS prioritization
        """
        total_requests = sum(onu.bandwidth_request for onu in self.onus)
        available_bandwidth = self.get_available_upstream_bandwidth()
        
        # Priority-based allocation
        allocations = {}
        remaining_bandwidth = available_bandwidth
        
        # Phase 1: Guaranteed bandwidth for high-priority traffic
        for onu in self.onus:
            if onu.has_priority_traffic():
                guaranteed = min(onu.sla_guaranteed_rate, onu.bandwidth_request)
                allocations[onu.id] = guaranteed
                remaining_bandwidth -= guaranteed
                
        # Phase 2: Predicted traffic allocation using ML
        predicted_demands = self.predict_traffic_demands()
        
        for i, onu in enumerate(self.onus):
            if onu.id not in allocations:
                predicted_bw = predicted_demands[i]
                actual_request = onu.bandwidth_request
                
                # Weighted allocation considering prediction and actual request
                weight_factor = 0.7 * actual_request + 0.3 * predicted_bw
                allocation = min(weight_factor, remaining_bandwidth * 
                               (weight_factor / sum(predicted_demands)))
                
                allocations[onu.id] = int(allocation)
                remaining_bandwidth -= allocation
                
        return allocations
    
    def predict_traffic_demands(self):
        """ML-based traffic prediction using historical patterns"""
        if len(self.traffic_history) < 100:
            # Fallback to historical average for initial operation
            return [onu.get_average_bandwidth() for onu in self.onus]
        
        # Feature extraction: time of day, day of week, historical usage
        features = self.extract_features()
        predictions = self.bandwidth_predictor.predict(features)
        
        return predictions
    
    def update_traffic_model(self, actual_usage):
        """Continuously update ML model with actual traffic patterns"""
        self.traffic_history.append(actual_usage)
        
        if len(self.traffic_history) >= 500:  # Retrain periodically
            features, targets = self.prepare_training_data()
            self.bandwidth_predictor.fit(features, targets)

class ONU:
    def __init__(self, onu_id):
        self.id = onu_id
        self.bandwidth_request = 0
        self.sla_guaranteed_rate = 100e6  # 100 Mbps guaranteed
        self.traffic_classes = {
            'voice': 0,      # Highest priority
            'video': 0,      # High priority
            'data': 0,       # Best effort
            'background': 0  # Lowest priority
        }
        self.bandwidth_history = deque(maxlen=100)
    
    def has_priority_traffic(self):
        """Check if ONU has high-priority traffic requiring immediate allocation"""
        return (self.traffic_classes['voice'] > 0 or 
                self.traffic_classes['video'] > 0)
    
    def get_average_bandwidth(self):
        """Calculate average bandwidth usage for prediction baseline"""
        if not self.bandwidth_history:
            return self.sla_guaranteed_rate * 0.3  # Conservative estimate
        return np.mean(self.bandwidth_history)

// Burst-Mode Receiver DSP Implementation (C++)
// Optimized for 50G-PON with advanced equalization

#include <complex>
#include <vector>
#include <algorithm>

class BurstModeReceiver {
private:
    std::vector<std::complex<double>> equalizer_taps;
    double agc_gain;
    double threshold_level;
    int samples_per_symbol;
    
public:
    BurstModeReceiver(int tap_count = 32, int sps = 2) 
        : equalizer_taps(tap_count, std::complex<double>(0.0, 0.0))
        , agc_gain(1.0)
        , threshold_level(0.0)
        , samples_per_symbol(sps) {
        initialize_equalizer();
    }
    
    std::vector<int> process_burst(const std::vector<std::complex<double>>& rx_samples) {
        // Step 1: Burst detection and synchronization
        auto burst_start = detect_burst_start(rx_samples);
        if (burst_start == -1) return {};  // No valid burst detected
        
        // Step 2: Automatic Gain Control
        auto agc_output = apply_agc(rx_samples, burst_start);
        
        // Step 3: Clock and Data Recovery
        auto cdr_output = clock_data_recovery(agc_output);
        
        // Step 4: Adaptive Equalization
        auto equalized = adaptive_equalization(cdr_output);
        
        // Step 5: Decision and Error Correction
        auto decisions = make_decisions(equalized);
        
        // Step 6: Update equalizer taps for next burst
        update_equalizer_taps(equalized, decisions);
        
        return decisions;
    }
    
private:
    int detect_burst_start(const std::vector<std::complex<double>>& samples) {
        // Correlate with known preamble pattern
        std::vector<std::complex<double>> preamble = generate_preamble();
        double max_correlation = 0.0;
        int best_position = -1;
        
        for (size_t i = 0; i < samples.size() - preamble.size(); ++i) {
            std::complex<double> correlation(0.0, 0.0);
            
            for (size_t j = 0; j < preamble.size(); ++j) {
                correlation += samples[i + j] * std::conj(preamble[j]);
            }
            
            double magnitude = std::abs(correlation);
            if (magnitude > max_correlation) {
                max_correlation = magnitude;
                best_position = static_cast<int>(i);
            }
        }
        
        // Threshold for valid burst detection
        return (max_correlation > 0.7) ? best_position : -1;
    }
    
    std::vector<std::complex<double>> apply_agc(
        const std::vector<std::complex<double>>& samples, int start_pos) {
        
        // Fast AGC convergence for burst-mode operation
        std::vector<std::complex<double>> output;
        output.reserve(samples.size());
        
        // Calculate initial power estimate from preamble
        double power_estimate = 0.0;
        for (int i = start_pos; i < start_pos + 64 && i < samples.size(); ++i) {
            power_estimate += std::norm(samples[i]);
        }
        power_estimate /= 64.0;
        
        // Set AGC gain for target power level
        double target_power = 1.0;
        agc_gain = std::sqrt(target_power / power_estimate);
        
        // Apply AGC with gradual adjustment
        for (const auto& sample : samples) {
            output.push_back(sample * agc_gain);
        }
        
        return output;
    }
    
    std::vector<std::complex<double>> adaptive_equalization(
        const std::vector<std::complex<double>>& samples) {
        
        std::vector<std::complex<double>> output;
        output.reserve(samples.size());
        
        // LMS (Least Mean Squares) adaptive equalization
        double mu = 0.01;  // Adaptation step size
        
        for (size_t i = equalizer_taps.size(); i < samples.size(); ++i) {
            // Convolution with equalizer taps
            std::complex<double> equalized_sample(0.0, 0.0);
            
            for (size_t j = 0; j < equalizer_taps.size(); ++j) {
                equalized_sample += equalizer_taps[j] * samples[i - j];
            }
            
            output.push_back(equalized_sample);
            
            // Decision-directed adaptation during data portion
            if (i > 1000) {  // Skip preamble for adaptation
                auto decision = hard_decision(equalized_sample);
                std::complex<double> error = equalized_sample - decision;
                
                // Update equalizer taps using LMS algorithm
                for (size_t j = 0; j < equalizer_taps.size(); ++j) {
                    equalizer_taps[j] -= mu * error * std::conj(samples[i - j]);
                }
            }
        }
        
        return output;
    }
    
    std::complex<double> hard_decision(const std::complex<double>>& symbol) {
        // QPSK constellation decision for 50G-PON
        double real_part = (symbol.real() > 0) ? 1.0 : -1.0;
        double imag_part = (symbol.imag() > 0) ? 1.0 : -1.0;
        return std::complex<double>(real_part, imag_part);
    }
};

// Coherent PON DSP Chain Implementation
// MATLAB/Octave implementation for 100G Coherent PON

function [recovered_data] = coherent_pon_dsp(rx_signal, params)
    % Main DSP processing chain for coherent PON receiver
    % Input: rx_signal - complex IQ samples from ADC
    % Output: recovered_data - demodulated data bits
    
    % Step 1: Chromatic Dispersion Compensation
    cd_compensated = chromatic_dispersion_compensation(rx_signal, params);
    
    % Step 2: Polarization Demultiplexing (for PDM systems)
    [pol_x, pol_y] = polarization_demux(cd_compensated, params);
    
    % Step 3: Frequency Offset Estimation and Correction
    [freq_corrected_x] = frequency_offset_correction(pol_x, params);
    [freq_corrected_y] = frequency_offset_correction(pol_y, params);
    
    % Step 4: Phase Noise Compensation
    [phase_corrected_x] = phase_recovery(freq_corrected_x, params);
    [phase_corrected_y] = phase_recovery(freq_corrected_y, params);
    
    % Step 5: Symbol Decision and FEC Decoding
    data_x = symbol_decision(phase_corrected_x, params.modulation);
    data_y = symbol_decision(phase_corrected_y, params.modulation);
    
    % Step 6: Forward Error Correction
    recovered_data = [fec_decode(data_x, params); fec_decode(data_y, params)];
end

function [cd_compensated] = chromatic_dispersion_compensation(signal, params)
    % Frequency-domain CD compensation using overlap-save method
    
    fiber_length = params.fiber_length; % km
    lambda = params.wavelength; % wavelength in meters
    D = 17e-6; % dispersion parameter (s/m^2) for SMF
    c = 3e8; % speed of light
    
    % Calculate dispersion parameter beta2
    beta2 = -(lambda^2 * D) / (2 * pi * c);
    
    % Generate frequency vector
    N = length(signal);
    fs = params.sample_rate;
    f = (-N/2:N/2-1) * fs/N;
    omega = 2 * pi * f;
    
    % CD transfer function
    H_cd = exp(1j * beta2 * omega.^2 * fiber_length / 2);
    
    % Apply compensation in frequency domain
    signal_fft = fftshift(fft(signal));
    compensated_fft = signal_fft .* conj(H_cd);
    cd_compensated = ifft(ifftshift(compensated_fft));
end

function [freq_corrected] = frequency_offset_correction(signal, params)
    % Frequency offset estimation using 4th power method for QPSK
    
    % Raise signal to 4th power to remove modulation
    signal_4th = signal.^4;
    
    % FFT to find frequency offset
    fft_4th = fft(signal_4th);
    [~, max_idx] = max(abs(fft_4th));
    
    % Calculate frequency offset
    N = length(signal);
    fs = params.sample_rate;
    freq_offset = (max_idx - 1 - N/2) * fs / (4 * N);
    
    % Generate correction signal
    t = (0:N-1) / fs;
    correction = exp(-1j * 2 * pi * freq_offset * t);
    
    % Apply frequency correction
    freq_corrected = signal .* correction';
end

function [phase_corrected] = phase_recovery(signal, params)
    % Viterbi-Viterbi phase recovery for QPSK
    
    block_size = 64; % Phase estimation block size
    num_blocks = floor(length(signal) / block_size);
    phase_corrected = zeros(size(signal));
    
    for block = 1:num_blocks
        start_idx = (block-1) * block_size + 1;
        end_idx = block * block_size;
        block_signal = signal(start_idx:end_idx);
        
        % Raise to 4th power and average for phase estimation
        phase_estimate = angle(mean(block_signal.^4)) / 4;
        
        % Unwrap phase to handle 2π ambiguity
        if block > 1
            phase_diff = phase_estimate - prev_phase;
            if phase_diff > pi/2
                phase_estimate = phase_estimate - pi/2;
            elseif phase_diff < -pi/2
                phase_estimate = phase_estimate + pi/2;
            end
        end
        
        % Apply phase correction
        phase_corrected(start_idx:end_idx) = block_signal * exp(-1j * phase_estimate);
        prev_phase = phase_estimate;
    end
end