simulations/models/end_to_end.py

import math

from tensorflow import keras
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import collections as matcoll
from sklearn.preprocessing import OneHotEncoder
from tensorflow.keras import layers, losses


class ExtractCentralMessage(layers.Layer):
    def __init__(self, messages_per_block, samples_per_symbol):
        """
        :param messages_per_block: Total number of messages in transmission block
        :param samples_per_symbol: Number of samples per transmitted symbol
        """
        super(ExtractCentralMessage, self).__init__()

        temp_w = np.zeros((messages_per_block * samples_per_symbol, samples_per_symbol))
        i = np.identity(samples_per_symbol)
        begin = int(samples_per_symbol * ((messages_per_block - 1) / 2))
        end = int(samples_per_symbol * ((messages_per_block + 1) / 2))
        temp_w[begin:end, :] = i

        self.w = tf.convert_to_tensor(temp_w, dtype=tf.float32)

    def call(self, inputs, **kwargs):
        return tf.matmul(inputs, self.w)


class DigitizationLayer(layers.Layer):
    def __init__(self,
                 fs,
                 num_of_samples,
                 lpf_cutoff=32e9,
                 q_stddev=0.1):
        """
        :param fs: Sampling frequency of the simulation in Hz
        :param num_of_samples: Total number of samples in the input
        :param lpf_cutoff: Cutoff frequency of LPF modelling finite bandwidth in ADC/DAC
        :param q_stddev: Standard deviation of quantization noise at ADC/DAC
        """
        super(DigitizationLayer, self).__init__()

        self.noise_layer = layers.GaussianNoise(q_stddev)
        freq = np.fft.fftfreq(num_of_samples, d=1/fs)
        temp = np.ones(freq.shape)

        for idx, val in np.ndenumerate(freq):
            if np.abs(val) > lpf_cutoff:
                temp[idx] = 0

        self.lpf_multiplier = tf.convert_to_tensor(temp, dtype=tf.complex64)

    def call(self, inputs, **kwargs):
        complex_in = tf.cast(inputs, dtype=tf.complex64)
        val_f = tf.signal.fft(complex_in)
        filtered_f = tf.math.multiply(self.lpf_multiplier, val_f)
        filtered_t = tf.signal.ifft(filtered_f)
        real_t = tf.cast(filtered_t, dtype=tf.float32)
        noisy = self.noise_layer.call(real_t, training=True)
        return noisy


class OpticalChannel(layers.Layer):
    def __init__(self,
                 fs,
                 num_of_samples,
                 dispersion_factor,
                 fiber_length,
                 lpf_cutoff=32e9,
                 rx_stddev=0.01,
                 q_stddev=0.01):
        """
        :param fs: Sampling frequency of the simulation in Hz
        :param num_of_samples: Total number of samples in the input
        :param dispersion_factor: Dispersion factor in s^2/km
        :param fiber_length: Length of fiber to model in km
        :param lpf_cutoff: Cutoff frequency of LPF modelling finite bandwidth in ADC/DAC
        :param rx_stddev: Standard deviation of receiver noise (due to e.g. TIA circuit)
        :param q_stddev: Standard deviation of quantization noise at ADC/DAC
        """
        super(OpticalChannel, self).__init__()

        self.noise_layer = layers.GaussianNoise(rx_stddev)
        self.digitization_layer = DigitizationLayer(fs=fs,
                                                    num_of_samples=num_of_samples,
                                                    lpf_cutoff=lpf_cutoff,
                                                    q_stddev=q_stddev)
        self.flatten_layer = layers.Flatten()

        self.fs = fs
        self.freq = tf.convert_to_tensor(np.fft.fftfreq(num_of_samples, d=1/fs), dtype=tf.complex128)
        self.multiplier = tf.math.exp(0.5j*dispersion_factor*fiber_length*tf.math.square(2*math.pi*self.freq))

    def call(self, inputs, **kwargs):
        # DAC LPF and noise
        dac_out = self.digitization_layer(inputs)

        # Chromatic Dispersion
        complex_val = tf.cast(dac_out, dtype=tf.complex128)
        val_f = tf.signal.fft(complex_val)
        disp_f = tf.math.multiply(val_f, self.multiplier)
        disp_t = tf.signal.ifft(disp_f)

        # Squared-Law Detection
        pd_out = tf.square(tf.abs(disp_t))

        # Casting back to floatx
        real_val = tf.cast(pd_out, dtype=tf.float32)

        # Adding photo-diode receiver noise
        rx_signal = self.noise_layer.call(real_val, training=True)

        # ADC LPF and noise
        adc_out = self.digitization_layer(rx_signal)

        return adc_out


class EndToEndAutoencoder(tf.keras.Model):
    def __init__(self,
                 cardinality,
                 samples_per_symbol,
                 messages_per_block,
                 channel):
        """
        :param cardinality: Number of different messages. Chosen such that each message encodes log_2(cardinality) bits
        :param samples_per_symbol: Number of samples per transmitted symbol
        :param messages_per_block: Total number of messages in transmission block
        :param channel: Channel Layer object. Must be a subclass of keras.layers.Layer with an implemented forward pass
        """
        super(EndToEndAutoencoder, self).__init__()

        # Labelled M in paper
        self.cardinality = cardinality
        # Labelled n in paper
        self.samples_per_symbol = samples_per_symbol
        # Labelled N in paper
        if messages_per_block % 2 == 0:
            messages_per_block += 1
        self.messages_per_block = messages_per_block
        # Channel Model Layer
        if isinstance(channel, layers.Layer):
            self.channel = tf.keras.Sequential([
                layers.Flatten(),
                channel,
                ExtractCentralMessage(self.messages_per_block, self.samples_per_symbol)
            ])
        else:
            raise TypeError("Channel must be a subclass of keras.layers.layer!")

        # Encoding Neural Network
        self.encoder = tf.keras.Sequential([
            layers.Input(shape=(self.messages_per_block, self.cardinality)),
            layers.Dense(2 * self.cardinality, activation='relu'),
            layers.Dense(2 * self.cardinality, activation='relu'),
            layers.Dense(self.samples_per_symbol),
            layers.ReLU(max_value=1.0)
        ])

        # Decoding Neural Network
        self.decoder = tf.keras.Sequential([
            layers.Dense(self.samples_per_symbol, activation='relu'),
            layers.Dense(2 * self.cardinality, activation='relu'),
            layers.Dense(2 * self.cardinality, activation='relu'),
            layers.Dense(self.cardinality, activation='softmax')
        ])

    def generate_random_inputs(self, num_of_blocks, return_vals=False):
        """
        :param num_of_blocks: Number of blocks to generate. A block contains multiple messages to be transmitted in
        consecutively to model ISI. The central message in a block is returned as the label for training.
        :param return_vals: If true, the raw decimal values of the input sequence will be returned
        """
        rand_int = np.random.randint(self.cardinality, size=(num_of_blocks * self.messages_per_block, 1))

        cat = [np.arange(self.cardinality)]
        enc = OneHotEncoder(handle_unknown='ignore', sparse=False, categories=cat)

        out = enc.fit_transform(rand_int)
        out_arr = np.reshape(out, (num_of_blocks, self.messages_per_block, self.cardinality))

        mid_idx = int((self.messages_per_block-1)/2)

        if return_vals:
            out_val = np.reshape(rand_int, (num_of_blocks, self.messages_per_block, 1))
            return out_val, out_arr, out_arr[:, mid_idx, :]

        return out_arr, out_arr[:, mid_idx, :]

    def train(self, num_of_blocks=1e6, batch_size=None, train_size=0.8, lr=1e-3):
        """
        :param num_of_blocks: Number of blocks to generate for training. Analogous to the dataset size.
        :param batch_size: Number of samples to consider on each update iteration of the optimization algorithm
        :param train_size: Float less than 1 representing the proportion of the dataset to use for training
        :param lr: The learning rate of the optimizer. Defines how quickly the algorithm converges
        """
        X_train, y_train = self.generate_random_inputs(int(num_of_blocks*train_size))
        X_test, y_test = self.generate_random_inputs(int(num_of_blocks*(1-train_size)))

        opt = keras.optimizers.Adam(learning_rate=lr)

        self.compile(optimizer=opt,
                     loss=losses.BinaryCrossentropy(),
                     metrics=['accuracy'],
                     loss_weights=None,
                     weighted_metrics=None,
                     run_eagerly=False
                     )

        self.fit(x=X_train,
                 y=y_train,
                 batch_size=batch_size,
                 epochs=1,
                 shuffle=True,
                 validation_data=(X_test, y_test)
                 )

    def view_encoder(self):
        # Generate inputs for encoder
        messages = np.zeros((self.cardinality, self.messages_per_block, self.cardinality))

        mid_idx = int((self.messages_per_block-1)/2)

        idx = 0
        for msg in messages:
            msg[mid_idx, idx] = 1
            idx += 1

        # Pass input through encoder and select middle messages
        encoded = self.encoder(messages)
        enc_messages = encoded[:, mid_idx, :]

        # Compute subplot grid layout
        i = 0
        while 2**i < self.cardinality**0.5:
            i += 1

        num_x = int(2**i)
        num_y = int(self.cardinality / num_x)

        # Plot all symbols
        fig, axs = plt.subplots(num_y, num_x, figsize=(2.5*num_x, 2*num_y))

        t = np.arange(self.samples_per_symbol)
        if isinstance(self.channel.layers[1], OpticalChannel):
            t = t/self.channel.layers[1].fs

        sym_idx = 0
        for y in range(num_y):
            for x in range(num_x):
                axs[y, x].plot(t, enc_messages[sym_idx], 'x')
                axs[y, x].set_title('Symbol {}'.format(str(sym_idx)))
                sym_idx += 1

        for ax in axs.flat:
            ax.set(xlabel='Time', ylabel='Amplitude', ylim=(0, 1))

        for ax in axs.flat:
            ax.label_outer()

        plt.show()
        pass

    def view_sample_block(self):
        # Generate a random block of messages
        val, inp, _ = self.generate_random_inputs(num_of_blocks=1, return_vals=True)

        # Encode and flatten the messages
        enc = self.encoder(inp)
        flat_enc = layers.Flatten()(enc)

        # Instantiate LPF layer
        lpf = DigitizationLayer(fs=self.channel.layers[1].fs,
                                num_of_samples=self.messages_per_block*self.samples_per_symbol,
                                q_stddev=0)

        # Apply LPF
        lpf_out = lpf(flat_enc)

        # Time axis
        t = np.arange(self.messages_per_block*self.samples_per_symbol)
        if isinstance(self.channel.layers[1], OpticalChannel):
            t = t / self.channel.layers[1].fs

        # Plot the concatenated symbols before and after LPF
        plt.figure(figsize=(2*self.messages_per_block, 6))

        for i in range(1, self.messages_per_block):
            plt.axvline(x=t[i*self.samples_per_symbol], color='black')

        plt.plot(t, flat_enc.numpy().T, 'x')
        plt.plot(t, lpf_out.numpy().T)
        plt.ylim((0, 1))
        plt.xlim((t.min(), t.max()))
        plt.title(str(val[0, :, 0]))
        plt.show()
        pass

    def call(self, inputs, training=None, mask=None):
        tx = self.encoder(inputs)
        rx = self.channel(tx)
        outputs = self.decoder(rx)
        return outputs


if __name__ == '__main__':

    SAMPLING_FREQUENCY = 336e9
    CARDINALITY = 32
    SAMPLES_PER_SYMBOL = 24
    MESSAGES_PER_BLOCK = 9
    DISPERSION_FACTOR = -21.7 * 1e-24
    FIBER_LENGTH = 50

    optical_channel = OpticalChannel(fs=SAMPLING_FREQUENCY,
                                     num_of_samples=MESSAGES_PER_BLOCK*SAMPLES_PER_SYMBOL,
                                     dispersion_factor=DISPERSION_FACTOR,
                                     fiber_length=FIBER_LENGTH)

    ae_model = EndToEndAutoencoder(cardinality=CARDINALITY,
                                   samples_per_symbol=SAMPLES_PER_SYMBOL,
                                   messages_per_block=MESSAGES_PER_BLOCK,
                                   channel=optical_channel)

    ae_model.train(num_of_blocks=1e6, batch_size=100)
    ae_model.view_encoder()
    ae_model.view_sample_block()

    pass