# Notebook from NVlabs/sionna
Path: examples/Sionna_tutorial_part3.ipynb

# Part 3: Advanced Link-level Simulations_____no_output_____This tutorial will guide you through Sionna, from its basic principles to the implementation of a point-to-point link with a 5G NR compliant code and a 3GPP channel model.
You will also learn how to write custom trainable layers by implementing a state of the art neural receiver, and how to train and evaluate end-to-end communication systems.

The tutorial is structured in four notebooks:

- Part I: Getting started with Sionna

- Part II: Differentiable Communication Systems

- **Part III: Advanced Link-level Simulations**

- Part IV: Toward Learned Receivers
_____no_output_____The [official documentation](https://nvlabs.github.io/sionna ) provides key material on how to use Sionna and how its components are implemented._____no_output_____* [Imports](#Imports)
* [OFDM Resource Grid and Stream Management](#OFDM-Resource-Grid-and-Stream-Management)
* [Antenna Arrays](#Antenna-Arrays)
* [Channel Model](#Channel-Model)
* [Uplink Transmission in the Frequency Domain](#Uplink-Transmission-in-the-Frequency-Domain)_____no_output_____## Imports_____no_output_____

<code>
# Import TensorFlow and NumPy
import tensorflow as tf
import numpy as np

# Import Sionna
try:
    import sionna as sn
except ImportError as e:
    # Install Sionna if package is not already installed
    import os
    os.system("pip install sionna")
    import sionna as sn

# For plotting
%matplotlib inline
import matplotlib.pyplot as plt

# For the implementation of the Keras models
from tensorflow.keras import Model_____no_output_____
</code>

## OFDM Resource Grid and Stream Management_____no_output_____We will setup a realistic SIMO point-to-point link between a mobile user terminal (UT) and a base station (BS). The system we will setup is shown in the figure below._____no_output_____![SIMO](data:image/png;base64,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)_____no_output_____### Stream Management

For any type of MIMO simulations, it is required to setup a `StreamManagement` object.
It determines which transmitters and receivers communicate data streams with each other.
In our scenario, we will configure a single UT equipped with a single antenna and a single BS equipped with multiple antennas.
Whether the UT or BS is considered as a transmitter depends on the link direction, which can be
either uplink or downlink. The `StreamManagement` has many properties that are used by other components,
such as precoding and equalization.

We will configure the system here such that the number of streams per transmitter
is equal to the number of UT antennas._____no_output_____

<code>
# Define the number of UT and BS antennas
NUM_UT = 1
NUM_BS = 1
NUM_UT_ANT = 1
NUM_BS_ANT = 4

# The number of transmitted streams is equal to the number of UT antennas
# in both uplink and downlink
NUM_STREAMS_PER_TX = NUM_UT_ANT

# Create an RX-TX association matrix.
# RX_TX_ASSOCIATION[i,j]=1 means that receiver i gets at least one stream
# from transmitter j. Depending on the transmission direction (uplink or downlink),
# the role of UT and BS can change.
# For example, considering a system with 2 RX and 4 TX, the RX-TX
# association matrix could be
# [ [1 , 1, 0, 0],
#   [0 , 0, 1, 1] ]
# which indicates that the RX 0 receives from TX 0 and 1, and RX 1 receives from
# TX 2 and 3.
#
# In this notebook, as we have only a single transmitter and receiver,
# the RX-TX association matrix is simply:
RX_TX_ASSOCIATION = np.array([[1]])

# Instantiate a StreamManagement object
# This determines which data streams are determined for which receiver.
# In this simple setup, this is fairly easy. However, it can get more involved
# for simulations with many transmitters and receivers.
STREAM_MANAGEMENT = sn.mimo.StreamManagement(RX_TX_ASSOCIATION, NUM_STREAMS_PER_TX)_____no_output_____
</code>

### OFDM Resource Grid_____no_output_____Next, we configure an OFDM `ResourceGrid` spanning multiple OFDM symbols.
The resource grid contains data symbols and pilots and is equivalent to a
*slot* in 4G/5G terminology. Although it is not relevant for our simulation, we null the DC subcarrier
and a few guard carriers to the left and right of the spectrum. Also a cyclic prefix is added.

During the creation of the `ResourceGrid`, a `PilotPattern` is automatically generated.
We could have alternatively created a `PilotPattern` first and then provided it as initialization parameter.
When multiple streams are considered, the corresponding pilot patterns must be orthogonal.
By default, orthogonal pilots are setup when considering such systems._____no_output_____

<code>
RESOURCE_GRID = sn.ofdm.ResourceGrid( num_ofdm_symbols=14,
                                      fft_size=76,
                                      subcarrier_spacing=30e3,
                                      num_tx=NUM_UT,
                                      num_streams_per_tx=NUM_STREAMS_PER_TX,
                                      cyclic_prefix_length=6,
                                      pilot_pattern="kronecker",
                                      pilot_ofdm_symbol_indices=[2,11])
RESOURCE_GRID.show();_____no_output_____RESOURCE_GRID.pilot_pattern.show();_____no_output_____
</code>

**Task:** You can try different pilot patterns, FFT size, number of OFDM symbols, and visualize how it affects the resource grid.

See the notebook [MIMO_OFDM_Transmissions_over_CDL](https://nvlabs.github.io/sionna/examples/MIMO_OFDM_Transmissions_over_CDL.html) for more advanced examples._____no_output_____## Antenna Arrays_____no_output_____We need to configure the antenna arrays used by the UT and BS.
This can be ignored for simple channel models, such as `AWGN`, `RayleighBlockFading`, or `TDL` which do not account for antenna array geometries and antenna radiation patterns. However, other models, such as `CDL`, `UMi`, `UMa`, and `RMa` from the 3GPP 38.901 specification, require it.

We will assume here that UT is equipped with one vertically single-polarized antenna and the BS antenna array is composed of dual cross-polarized antenna elements with an antenna pattern defined in the [3GPP 38.901 specification](https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3173). By default, the antenna elements are spaced half of a wavelength apart in both vertical and horizontal directions. You can define your own antenna geometries an radiation patterns if needed.

An `AntennaArray` is always defined in the y-z plane. It's final orientation will be determined by the orientation of the UT or BS. This parameter can be configured in the `ChannelModel` that we will create later._____no_output_____

<code>
CARRIER_FREQUENCY = 2.6e9 # Carrier frequency in Hz.
                          # This is needed here to define the antenna element spacing.

UT_ARRAY = sn.channel.tr38901.Antenna(  polarization="single",
                                        polarization_type="V",
                                        antenna_pattern="38.901",
                                        carrier_frequency=CARRIER_FREQUENCY)
UT_ARRAY.show();

BS_ARRAY = sn.channel.tr38901.AntennaArray( num_rows=1,
                                            num_cols=int(NUM_BS_ANT/2),
                                            polarization="dual",
                                            polarization_type="cross",
                                            antenna_pattern="38.901", # Try 'omni'
                                            carrier_frequency=CARRIER_FREQUENCY)
BS_ARRAY.show();_____no_output_____BS_ARRAY.show_element_radiation_pattern();_____no_output_____
</code>

**Task:** You can try different antenna pattern ("omni"), polarization, and array geometries._____no_output_____## Channel Model_____no_output_____Sionna implements the CDL, TDL, UMi, UMa, and RMa models from [3GPP TR 38.901](https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3173), as well as Rayleigh block fading.

Note that:
* TDL only supports SISO
* CDL only supports single-user, possibly with multiple antenna
* UMi, UMa, and RMa support single- and multi-user

*Remark:* The TDL and CDL models correspond to fixed power delay profiles and fixed angles._____no_output_____![SIMO 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)_____no_output_____We consider the 3GPP CDL model family in this notebook._____no_output_____

<code>
DELAY_SPREAD = 100e-9 # Nominal delay spread in [s]. Please see the CDL documentation
                      # about how to choose this value.

DIRECTION = "uplink"  # The `direction` determines if the UT or BS is transmitting.
                      # In the `uplink`, the UT is transmitting.

CDL_MODEL = "C"       # Suitable values are ["A", "B", "C", "D", "E"]

SPEED = 10.0          # UT speed [m/s]. BSs are always assumed to be fixed.
                     # The direction of travel will chosen randomly within the x-y plane.

# Configure a channel impulse reponse (CIR) generator for the CDL model.
CDL = sn.channel.tr38901.CDL(CDL_MODEL,
                             DELAY_SPREAD,
                             CARRIER_FREQUENCY,
                             UT_ARRAY,
                             BS_ARRAY,
                             DIRECTION,
                             min_speed=SPEED)_____no_output_____
</code>

The instance `CDL` of the CDL model can be used to generate batches of random realizations of continuous-time
channel impulse responses, consisting of complex gains `a` and delays `tau` for each path.
To account for time-varying channels, a channel impulse responses is sampled at the `sampling_frequency` for `num_time_samples` samples.
For more details on this, please have a look at the API documentation of the channel models.

In order to model the channel in the frequency domain, we need `num_ofdm_symbols` samples that are taken once per `ofdm_symbol_duration`, which corresponds to the length of an OFDM symbol plus the cyclic prefix._____no_output_____

<code>
BATCH_SIZE = 128 # How many examples are processed by Sionna in parallel

a, tau = CDL(batch_size=BATCH_SIZE,
             num_time_steps=RESOURCE_GRID.num_ofdm_symbols,
             sampling_frequency=1/RESOURCE_GRID.ofdm_symbol_duration)_____no_output_____
</code>

The path gains `a` have shape\
`[batch size, num_rx, num_rx_ant, num_tx, num_tx_ant, num_paths, num_time_steps]`\
and the delays `tau` have shape\
`[batch_size, num_rx, num_tx, num_paths]`._____no_output_____

<code>
print("Shape of the path gains: ", a.shape)
print("Shape of the delays:", tau.shape)Shape of the path gains:  (128, 1, 4, 1, 1, 24, 14)
Shape of the delays: (128, 1, 1, 24)

</code>

The delays are assumed to be static within the time-window of interest. Only the complex path gains change over time.
The following two figures depict the channel impulse response at a particular time instant and the time-evolution of the gain of one path, respectively._____no_output_____

<code>
plt.figure()
plt.title("Channel impulse response realization")
plt.stem(tau[0,0,0,:]/1e-9, np.abs(a)[0,0,0,0,0,:,0])
plt.xlabel(r"$\tau$ [ns]")
plt.ylabel(r"$|a|$")

plt.figure()
plt.title("Time evolution of path gain")
plt.plot(np.arange(RESOURCE_GRID.num_ofdm_symbols)*RESOURCE_GRID.ofdm_symbol_duration/1e-6, np.real(a)[0,0,0,0,0,0,:])
plt.plot(np.arange(RESOURCE_GRID.num_ofdm_symbols)*RESOURCE_GRID.ofdm_symbol_duration/1e-6, np.imag(a)[0,0,0,0,0,0,:])
plt.legend(["Real part", "Imaginary part"])

plt.xlabel(r"$t$ [us]")
plt.ylabel(r"$a$");_____no_output_____
</code>

See the notebook [Realistic_Multiuser_MIMO_Simulations](https://nvlabs.github.io/sionna/examples/Realistic_Multiuser_MIMO_Simulations.html) for more advanced examples._____no_output_____## Uplink Transmission in the Frequency Domain_____no_output_____We are now ready to simulate a transmission.

In the following, the channel is simulated in the frequency domain. Therefore, the channel is assumed to be constant over the duration of an OFDM symbol, which leads to not simulating the intercarrier interference (ICI) that could occur due to channel aging over the duration of OFDM symbols.

The `OFDMChannel` layer is used to simulate the channel in the frequency domain and takes care of sampling channel impulse responses, computing the frequency responses, and applying the channel transfer function to the channel inputs (including AWGN).

Note that it is also possible to simulate the channel in time domain using the `TimeChannel` layer, which enables simulation of ICI.
For more information, please have a look at the API documentation._____no_output_____

<code>
NUM_BITS_PER_SYMBOL = 2 # QPSK
CODERATE = 0.5

# Number of coded bits in a resource grid
n = int(RESOURCE_GRID.num_data_symbols*NUM_BITS_PER_SYMBOL)
# Number of information bits in a resource groud
k = int(n*CODERATE)

# The binary source will create batches of information bits
binary_source = sn.utils.BinarySource()

# The encoder maps information bits to coded bits
encoder = sn.fec.ldpc.LDPC5GEncoder(k, n)

# The mapper maps blocks of information bits to constellation symbols
mapper = sn.mapping.Mapper("qam", NUM_BITS_PER_SYMBOL)

# The resource grid mapper maps symbols onto an OFDM resource grid
rg_mapper = sn.ofdm.ResourceGridMapper(RESOURCE_GRID)

# Frequency domain channel
channel = sn.channel.OFDMChannel(CDL, RESOURCE_GRID, add_awgn=True, normalize_channel=True, return_channel=True)

# The LS channel estimator will provide channel estimates and error variances
ls_est = sn.ofdm.LSChannelEstimator(RESOURCE_GRID, interpolation_type="nn")

# The LMMSE equalizer will provide soft symbols together with noise variance estimates
lmmse_equ = sn.ofdm.LMMSEEqualizer(RESOURCE_GRID, STREAM_MANAGEMENT)

# The demapper produces LLR for all coded bits
demapper = sn.mapping.Demapper("app", "qam", NUM_BITS_PER_SYMBOL)

# The decoder provides hard-decisions on the information bits
decoder = sn.fec.ldpc.LDPC5GDecoder(encoder, hard_out=True)_____no_output_____
</code>

Let's now simulate the transmission, and look at the shape of the layers outputs at each stage.

The utility function `ebnodb2no` takes as additional input the resource grid to account for the pilots when computing the noise power spectral density ratio $N_0$ from the energy per bit to noise power spectral density ratio $E_b/N_0$ (in dB)._____no_output_____

<code>
no = sn.utils.ebnodb2no(ebno_db=10.0,
                        num_bits_per_symbol=NUM_BITS_PER_SYMBOL,
                        coderate=CODERATE,
                        resource_grid=RESOURCE_GRID)

# Transmitter
bits = binary_source([BATCH_SIZE, NUM_UT, RESOURCE_GRID.num_streams_per_tx, k])
print("Shape of bits: ", bits.shape)
codewords = encoder(bits)
print("Shape of codewords: ", codewords.shape)
x = mapper(codewords)
print("Shape of x: ", x.shape)
x_rg = rg_mapper(x)
print("Shape of x_rg: ", x_rg.shape)

# Channel
y, h_freq = channel([x_rg, no])
print("Shape of y_rg: ", y.shape)
print("Shape of h_freq: ", h_freq.shape)

# Receiver
h_hat, err_var = ls_est ([y, no])
print("Shape of h_hat: ", h_hat.shape)
print("Shape of err_var: ", err_var.shape)
x_hat, no_eff = lmmse_equ([y, h_hat, err_var, no])
print("Shape of x_hat: ", x_hat.shape)
print("Shape of no_eff: ", no_eff.shape)
llr = demapper([x_hat, no_eff])
print("Shape of llr: ", llr.shape)
bits_hat = decoder(llr)
print("Shape of bits_hat: ", bits_hat.shape)Shape of bits:  (128, 1, 1, 912)
Shape of codewords:  (128, 1, 1, 1824)
Shape of x:  (128, 1, 1, 912)
Shape of x_rg:  (128, 1, 1, 14, 76)
Shape of y_rg:  (128, 1, 4, 14, 76)
Shape of h_freq:  (128, 1, 4, 1, 1, 14, 76)
Shape of h_hat:  (128, 1, 4, 1, 1, 14, 76)
Shape of err_var:  (1, 1, 1, 1, 1, 14, 76)
Shape of x_hat:  (128, 1, 1, 912)
Shape of no_eff:  (128, 1, 1, 912)
Shape of llr:  (128, 1, 1, 1824)
Shape of bits_hat:  (128, 1, 1, 912)

</code>

The next cell implements the previous system as a Keras model.

Moreover, a boolean given as parameter to the initializer enables using either LS estimation or perfect CSI, as shown in the figure below._____no_output_____![Simo channel LS PCSI](data:image/png;base64,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)_____no_output_____

<code>
class OFDMSystem(Model): # Inherits from Keras Model

    def __init__(self, perfect_csi):
        super().__init__() # Must call the Keras model initializer

        self.perfect_csi = perfect_csi

        n = int(RESOURCE_GRID.num_data_symbols*NUM_BITS_PER_SYMBOL) # Number of coded bits
        k = int(n*CODERATE) # Number of information bits
        self.k = k

        # The binary source will create batches of information bits
        self.binary_source = sn.utils.BinarySource()

        # The encoder maps information bits to coded bits
        self.encoder = sn.fec.ldpc.LDPC5GEncoder(k, n)

        # The mapper maps blocks of information bits to constellation symbols
        self.mapper = sn.mapping.Mapper("qam", NUM_BITS_PER_SYMBOL)

        # The resource grid mapper maps symbols onto an OFDM resource grid
        self.rg_mapper = sn.ofdm.ResourceGridMapper(RESOURCE_GRID)

        # Frequency domain channel
        self.channel = sn.channel.OFDMChannel(CDL, RESOURCE_GRID, add_awgn=True, normalize_channel=True, return_channel=True)

        # The LS channel estimator will provide channel estimates and error variances
        self.ls_est = sn.ofdm.LSChannelEstimator(RESOURCE_GRID, interpolation_type="nn")

        # The LMMSE equalizer will provide soft symbols together with noise variance estimates
        self.lmmse_equ = sn.ofdm.LMMSEEqualizer(RESOURCE_GRID, STREAM_MANAGEMENT)

        # The demapper produces LLR for all coded bits
        self.demapper = sn.mapping.Demapper("app", "qam", NUM_BITS_PER_SYMBOL)

        # The decoder provides hard-decisions on the information bits
        self.decoder = sn.fec.ldpc.LDPC5GDecoder(self.encoder, hard_out=True)

    @tf.function # Graph execution to speed things up
    def __call__(self, batch_size, ebno_db):
        no = sn.utils.ebnodb2no(ebno_db, num_bits_per_symbol=NUM_BITS_PER_SYMBOL, coderate=CODERATE, resource_grid=RESOURCE_GRID)

        # Transmitter
        bits = self.binary_source([batch_size, NUM_UT, RESOURCE_GRID.num_streams_per_tx, self.k])
        codewords = self.encoder(bits)
        x = self.mapper(codewords)
        x_rg = self.rg_mapper(x)

        # Channel
        y, h_freq = self.channel([x_rg, no])

        # Receiver
        if self.perfect_csi:
            h_hat, err_var = h_freq, 0.
        else:
            h_hat, err_var = self.ls_est ([y, no])
        x_hat, no_eff = self.lmmse_equ([y, h_hat, err_var, no])
        llr = self.demapper([x_hat, no_eff])
        bits_hat = self.decoder(llr)

        return bits, bits_hat_____no_output_____EBN0_DB_MIN = -8.0 # Minimum value of Eb/N0 [dB] for simulations
EBN0_DB_MAX = 3.0 # Maximum value of Eb/N0 [dB] for simulations

ber_plots = sn.utils.PlotBER("OFDM over 3GPP CDL")

model_ls = OFDMSystem(False)
ber_plots.simulate(model_ls,
                  ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 20),
                  batch_size=BATCH_SIZE,
                  num_target_block_errors=100, # simulate until 100 block errors occured
                  legend="LS Estimation",
                  soft_estimates=True,
                  max_mc_iter=100, # run 100 Monte-Carlo simulations (each with batch_size samples)
                  show_fig=False);

model_pcsi = OFDMSystem(True)
ber_plots.simulate(model_pcsi,
                  ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 20),
                  batch_size=BATCH_SIZE,
                  num_target_block_errors=100, # simulate until 100 block errors occured
                  legend="Perfect CSI",
                  soft_estimates=True,
                  max_mc_iter=100, # run 100 Monte-Carlo simulations (each with batch_size samples)
                  show_fig=False);

ber_plots();WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: calling gather (from tensorflow.python.ops.array_ops) with validate_indices is deprecated and will be removed in a future version.
Instructions for updating:
The `validate_indices` argument has no effect. Indices are always validated on CPU and never validated on GPU.
EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status
---------------------------------------------------------------------------------------------------------------------------------------
     -8.0 | 4.2978e-01 | 1.0000e+00 |       50171 |      116736 |          128 |         128 |         7.0 |reached target block errors
   -7.421 | 4.1871e-01 | 1.0000e+00 |       48878 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -6.842 | 4.1140e-01 | 1.0000e+00 |       48025 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -6.263 | 4.0080e-01 | 1.0000e+00 |       46788 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -5.684 | 3.8840e-01 | 1.0000e+00 |       45340 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -5.105 | 3.7817e-01 | 1.0000e+00 |       44146 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -4.526 | 3.6195e-01 | 1.0000e+00 |       42253 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -3.947 | 3.4381e-01 | 1.0000e+00 |       40135 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -3.368 | 3.2687e-01 | 1.0000e+00 |       38158 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -2.789 | 3.0678e-01 | 1.0000e+00 |       35812 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -2.211 | 2.8767e-01 | 1.0000e+00 |       33582 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -1.632 | 2.6431e-01 | 1.0000e+00 |       30855 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -1.053 | 2.4057e-01 | 1.0000e+00 |       28083 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -0.474 | 2.1041e-01 | 1.0000e+00 |       24563 |      116736 |          128 |         128 |         0.2 |reached target block errors
    0.105 | 1.5792e-01 | 9.8438e-01 |       18435 |      116736 |          126 |         128 |         0.2 |reached target block errors
    0.684 | 3.1259e-02 | 4.3750e-01 |        7298 |      233472 |          112 |         256 |         0.3 |reached target block errors
    1.263 | 4.5551e-04 | 1.2525e-02 |        3350 |     7354368 |          101 |        8064 |         9.9 |reached target block errors
    1.842 | 0.0000e+00 | 0.0000e+00 |           0 |    11673600 |            0 |       12800 |        15.8 |reached max iter

Simulation stopped as no error occurred @ EbNo = 1.8 dB.

EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status
---------------------------------------------------------------------------------------------------------------------------------------
     -8.0 | 2.8242e-01 | 1.0000e+00 |       32969 |      116736 |          128 |         128 |         3.4 |reached target block errors
   -7.421 | 2.6455e-01 | 1.0000e+00 |       30883 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -6.842 | 2.4662e-01 | 1.0000e+00 |       28790 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -6.263 | 2.2697e-01 | 1.0000e+00 |       26495 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -5.684 | 2.0201e-01 | 1.0000e+00 |       23582 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -5.105 | 1.7199e-01 | 1.0000e+00 |       20078 |      116736 |          128 |         128 |         0.2 |reached target block errors
   -4.526 | 1.0359e-01 | 9.6094e-01 |       12093 |      116736 |          123 |         128 |         0.2 |reached target block errors
   -3.947 | 1.7351e-02 | 4.1016e-01 |        4051 |      233472 |          105 |         256 |         0.3 |reached target block errors
   -3.368 | 1.4470e-04 | 8.4918e-03 |        1554 |    10739712 |          100 |       11776 |        14.8 |reached target block errors
   -2.789 | 0.0000e+00 | 0.0000e+00 |           0 |    11673600 |            0 |       12800 |        16.1 |reached max iter

Simulation stopped as no error occurred @ EbNo = -2.8 dB.

</code>