コード例 #1
0
    def transmit(self,
                 duration: float = 0.) -> Tuple[Signal, Symbols, np.ndarray]:
        """Returns an array with the complex base-band samples of a waveform generator.

        The signal may be distorted by RF impairments.

        Args:
            duration (float, optional): Length of signal in seconds.

        Returns:
            transmissions (tuple):

                signal (Signal):
                    Signal model carrying the `data_bits` in multiple streams, each stream encoding multiple
                    radio frequency band communication frames.

                symbols (Symbols):
                    Symbols to which `data_bits` were mapped, used to modulate `signal`.

                data_bits (np.ndarray):
                    Vector of bits mapped to `data_symbols`.
        """

        # By default, the drop duration will be exactly one frame
        if duration <= 0.:
            duration = self.frame_duration

        # Number of data symbols per transmitted frame
        symbols_per_frame = self.waveform_generator.symbols_per_frame

        # Number of frames fitting into the selected drop duration
        frames_per_stream = int(duration / self.waveform_generator.frame_duration)

        # Generate data bits
        data_bits = self.generate_data_bits()

        # Number of code bits required to generate all frames for all streams
        num_code_bits = int(self.waveform_generator.bits_per_frame * frames_per_stream / self.precoding.rate)

        # Encode the data bits
        code_bits = self.encoder_manager.encode(data_bits, num_code_bits)

        # Map data bits to symbols
        symbols = self.waveform_generator.map(code_bits)

        # Apply symbol precoding
        symbol_streams = Symbols(self.precoding.encode(symbols.raw))

        # Check that the number of symbol streams matches the number of required symbol streams
        if symbol_streams.num_streams != self.num_streams:
            raise RuntimeError("Invalid precoding configuration, the number of resulting streams does not "
                               "match the number of transmit antennas")

        # Generate a dedicated base-band signal for each symbol stream
        signal = Signal(np.empty((0, 0), dtype=complex),
                        self.waveform_generator.sampling_rate)

        for stream_idx, stream_symbols in enumerate(symbol_streams):

            stream_signal = Signal(np.empty((0, 0), dtype=complex),
                                   self.waveform_generator.sampling_rate)

            for frame_idx in range(frames_per_stream):

                data_symbols = stream_symbols[frame_idx*symbols_per_frame:(1+frame_idx)*symbols_per_frame]

                frame_signal = self.waveform_generator.modulate(data_symbols)
                stream_signal.append_samples(frame_signal)

            signal.append_streams(stream_signal)

        # Apply stream coding, for instance beam-forming
        # TODO: Not yet supported.

        # Change the signal carrier
        # signal.carrier_frequency = self.carrier_frequency

        # Transmit signal over the occupied device slot (if the modem is attached to a device)
        if self._transmitter.attached:
            self._transmitter.slot.add_transmission(self._transmitter, signal)

        # Cache transmissions
        self.__transmitted_bits = data_bits
        self.__transmitted_symbols = symbols

        # We're finally done, blow the fanfares, throw confetti, etc.
        return signal, symbols, data_bits
コード例 #2
0
class TestSignal(unittest.TestCase):
    """Test the signal model base class."""
    def setUp(self) -> None:

        self.random = default_rng(42)

        self.num_streams = 3
        self.num_samples = 100
        self.sampling_rate = 1e4
        self.carrier_frequency = 1e3
        self.delay = 0.

        self.samples = (self.random.random(
            (self.num_streams, self.num_samples)) + 1j * self.random.random(
                (self.num_streams, self.num_samples)))

        self.signal = Signal(samples=self.samples,
                             sampling_rate=self.sampling_rate,
                             carrier_frequency=self.carrier_frequency,
                             delay=self.delay)

    def test_init(self) -> None:
        """Initialization arguments should be properly stored as object attributes."""

        assert_array_equal(self.samples, self.signal.samples)
        self.assertEqual(self.sampling_rate, self.signal.sampling_rate)
        self.assertEqual(self.carrier_frequency, self.signal.carrier_frequency)
        self.assertEqual(self.delay, self.signal.delay)

    def test_empty(self) -> None:
        """Using the empty initializer should result in an empty signal model."""

        sampling_rate = 2
        num_streams = 5
        num_samples = 6
        empty_signal = Signal.empty(sampling_rate,
                                    num_streams=num_streams,
                                    num_samples=num_samples)

        self.assertEqual(sampling_rate, empty_signal.sampling_rate)
        self.assertEqual(num_samples, empty_signal.num_samples)
        self.assertEqual(num_streams, empty_signal.num_streams)

    def test_samples_setget(self) -> None:
        """Samples property getter should return setter argument."""

        samples = (self.random.random(
            (self.num_streams + 1, self.num_samples + 1)) +
                   1j * self.random.random(
                       (self.num_streams + 1, self.num_samples + 1)))

        self.signal.samples = samples
        assert_array_equal(samples, self.signal.samples)

    def test_samples_validation(self) -> None:
        """Samples property setter should raise ValueError on invalid arguments."""

        with self.assertRaises(ValueError):
            self.signal.samples = self.random.random((1, 2, 3))

    def test_num_streams(self) -> None:
        """Number of streams property should return the correct number of streams."""

        self.assertEqual(self.num_streams, self.signal.num_streams)

    def test_num_samples(self) -> None:
        """Number of samples property should return the correct number of samples."""

        self.assertEqual(self.num_samples, self.signal.num_samples)

    def test_sampling_rate_setget(self) -> None:
        """Sampling rate property getter should return setter argument."""

        sampling_rate = 1.123e4
        self.signal.sampling_rate = sampling_rate

        self.assertEqual(sampling_rate, self.signal.sampling_rate)

    def test_sampling_rate_validation(self) -> None:
        """Sampling rate property setter should raise ValueError on invalid arguments."""

        with self.assertRaises(ValueError):
            self.signal.sampling_rate = -1.23

        with self.assertRaises(ValueError):
            self.signal.sampling_rate = 0.

    def test_carrier_frequency_setget(self) -> None:
        """Carrier frequency property getter should return setter argument."""

        carrier_frequency = 1.123
        self.signal.carrier_frequency = carrier_frequency

        self.assertEqual(carrier_frequency, self.signal.carrier_frequency)

    def test_carrier_frequency_validation(self) -> None:
        """Carrier frequency setter should raise ValueError on invalid arguments."""

        with self.assertRaises(ValueError):
            self.signal.carrier_frequency = -1.0

        try:
            self.signal.carrier_frequency = 0.

        except ValueError:
            self.fail()

    def test_copy(self) -> None:
        """Copying a signal model should result in a completely independent instance."""

        samples = self.signal.samples.copy()
        signal_copy = self.signal.copy()
        signal_copy.samples += 1j

        assert_array_equal(samples, self.signal.samples)

    def test_resampling_power_up(self) -> None:
        """Resampling to a higher sampling rate should not affect the signal power."""

        # Create an oversampled sinusoid signal
        frequency = 0.1 * self.sampling_rate
        self.num_samples = 1000
        timestamps = np.arange(self.num_samples) / self.sampling_rate
        samples = np.outer(np.exp(2j * pi * np.array([0, .33, .66])),
                           np.exp(2j * pi * timestamps * frequency))
        self.signal.samples = samples

        expected_sampling_rate = 2 * self.sampling_rate
        resampled_signal = self.signal.resample(expected_sampling_rate)

        expected_power = self.signal.power
        resampled_power = resampled_signal.power

        assert_array_almost_equal(expected_power, resampled_power, decimal=3)
        self.assertEqual(expected_sampling_rate,
                         resampled_signal.sampling_rate)

    def test_resampling_power_down(self) -> None:
        """Resampling to a lower sampling rate should not affect the signal power."""

        # Create an oversampled sinusoid signal
        frequency = 0.1 * self.sampling_rate
        self.num_samples = 1000
        timestamps = np.arange(self.num_samples) / self.sampling_rate
        samples = np.outer(np.exp(2j * pi * np.array([0, .33, .66])),
                           np.exp(2j * pi * timestamps * frequency))
        self.signal.samples = samples

        expected_sampling_rate = .5 * self.sampling_rate
        resampled_signal = self.signal.resample(expected_sampling_rate)

        expected_power = self.signal.power
        resampled_power = resampled_signal.power

        assert_array_almost_equal(expected_power, resampled_power, decimal=3)
        self.assertEqual(expected_sampling_rate,
                         resampled_signal.sampling_rate)

    def test_resampling_circular(self) -> None:
        """Up- and subsequently down-sampling a signal model should result in the identical signal."""

        # Create an oversampled sinusoid signal
        frequency = 0.3 * self.sampling_rate
        timestamps = np.arange(self.num_samples) / self.sampling_rate
        samples = np.outer(np.exp(2j * pi * np.array([0, 0.33, 0.66])),
                           np.exp(2j * pi * timestamps * frequency))
        self.signal.samples = samples

        # Up-sample and down-sample again
        up_signal = self.signal.resample(1.5 * self.sampling_rate)
        down_signal = up_signal.resample(self.sampling_rate)

        # Compare to the initial samples
        assert_array_almost_equal(samples, down_signal.samples, decimal=1)
        self.assertEqual(self.sampling_rate, down_signal.sampling_rate)

    def test_superimpose_power(self) -> None:
        """Superimposing two signal models should yield approximately the sum of both model's individual power."""

        expected_power = 4 * self.signal.power
        self.signal.superimpose(self.signal)

        assert_array_almost_equal(expected_power, self.signal.power)

    def test_timestamps(self) -> None:
        """Timestamps property should return the correct sampling times."""

        expected_timestamps = np.arange(self.num_samples) / self.sampling_rate
        assert_array_equal(expected_timestamps, self.signal.timestamps)

    def test_plot(self) -> None:
        """The plot routine should not raise any exceptions."""
        pass

    def test_append_samples(self) -> None:
        """Appending a signal model should yield the proper result."""

        samples = self.signal.samples.copy()
        append_samples = self.signal.samples + 1j
        append_signal = Signal(append_samples, self.signal.sampling_rate,
                               self.signal.carrier_frequency)

        self.signal.append_samples(append_signal)

        assert_array_equal(np.append(samples, append_samples, axis=1),
                           self.signal.samples)

    def test_append_samples_assert(self) -> None:
        """Appending to a signal model should raise a ValueError if the models don't match."""

        with self.assertRaises(ValueError):

            samples = self.signal.samples[0, :]
            append_signal = Signal(samples, self.signal.sampling_rate,
                                   self.signal.carrier_frequency)
            self.signal.append_samples(append_signal)

        with self.assertRaises(ValueError):

            samples = self.signal.samples
            append_signal = Signal(samples, self.signal.sampling_rate, 0.)
            self.signal.append_samples(append_signal)

    def test_append_streams(self) -> None:
        """Appending a signal model should yield the proper result."""

        samples = self.signal.samples.copy()
        append_samples = self.signal.samples + 1j
        append_signal = Signal(append_samples, self.signal.sampling_rate,
                               self.signal.carrier_frequency)

        self.signal.append_streams(append_signal)

        assert_array_equal(np.append(samples, append_samples, axis=0),
                           self.signal.samples)

    def test_append_stream_assert(self) -> None:
        """Appending to a signal model should raise a ValueError if the models don't match."""

        with self.assertRaises(ValueError):

            samples = self.signal.samples[:, 0]
            append_signal = Signal(samples, self.signal.sampling_rate,
                                   self.signal.carrier_frequency)
            self.signal.append_streams(append_signal)

        with self.assertRaises(ValueError):

            samples = self.signal.samples
            append_signal = Signal(samples, self.signal.sampling_rate, 0.)
            self.signal.append_streams(append_signal)