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"""DeepMIMO → Sionna: 5G NR PUSCH BLER Simulation."""
"""DeepMIMO → Sionna: 5G NR PUSCH BLER Simulation."""
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'DeepMIMO → Sionna: 5G NR PUSCH BLER Simulation.'
DeepMIMO → Sionna: 5G NR PUSCH BLER Simulation¶
What this notebook covers:
- Load a DeepMIMO ray-tracing scenario and extract per-path channel data
- Wire realistic DeepMIMO channels into Sionna's
CIRDataset - Run a complete 5G NR PUSCH link-level simulation with Sionna's PHY stack
- Plot a BLER curve and compare with a Rayleigh fading baseline
Why this workflow?
DeepMIMO provides realistic multipath channels from ray-tracing.
Sionna's CIRDataset accepts any generator that yields (a, tau) tuples
and feeds them through a full 5G NR receiver chain — no manual OFDM math
required. The resulting BLER curve reflects the fading structure of the
real environment, not just a simplified statistical model.
Requirements:
pip install deepmimo
pip install 'deepmimo[sionna]' # sionna-rt
pip install sionna-no-rt # Sionna PHY layer (PUSCH, LDPC, OFDM, …)
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%pip install deepmimo sionna-no-rt # uncomment if not installed
%pip install deepmimo sionna-no-rt # uncomment if not installed
/home/joao/DeepMIMO/.venv/bin/python: No module named pip
Note: you may need to restart the kernel to use updated packages.
Imports¶
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from __future__ import annotations
from typing import TYPE_CHECKING
import matplotlib.pyplot as plt
import numpy as np
import sionna.phy
import torch
from sionna.phy.channel import CIRDataset, OFDMChannel, RayleighBlockFading
from sionna.phy.nr import PUSCHConfig, PUSCHReceiver, PUSCHTransmitter
from sionna.phy.utils import compute_ber, ebnodb2no, sim_ber
import deepmimo as dm
if TYPE_CHECKING:
from collections.abc import Generator
sionna.phy.config.seed = 42 # reproducible Monte Carlo
from __future__ import annotations
from typing import TYPE_CHECKING
import matplotlib.pyplot as plt
import numpy as np
import sionna.phy
import torch
from sionna.phy.channel import CIRDataset, OFDMChannel, RayleighBlockFading
from sionna.phy.nr import PUSCHConfig, PUSCHReceiver, PUSCHTransmitter
from sionna.phy.utils import compute_ber, ebnodb2no, sim_ber
import deepmimo as dm
if TYPE_CHECKING:
from collections.abc import Generator
sionna.phy.config.seed = 42 # reproducible Monte Carlo
Configuration¶
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SCENARIO = "asu_campus_3p5" # ASU campus, 3.5 GHz
N_PATHS = 5 # max multipath components to use
N_BS_ANT = 8 # BS receive antennas (1-RX SIMO uplink)
# Simulation parameters — increase for smoother, more accurate curves
BATCH_SIZE = 64 # UEs per Monte Carlo step
MAX_MC_ITER = 100 # max batches per SNR point
NUM_TARGET_ERRORS = 100 # stop early once this many block errors collected
# Eb/N0 sweep — values sent to sim_ber
EBNO_DB_RANGE = np.arange(-5, 26, 2.5)
SCENARIO = "asu_campus_3p5" # ASU campus, 3.5 GHz
N_PATHS = 5 # max multipath components to use
N_BS_ANT = 8 # BS receive antennas (1-RX SIMO uplink)
# Simulation parameters — increase for smoother, more accurate curves
BATCH_SIZE = 64 # UEs per Monte Carlo step
MAX_MC_ITER = 100 # max batches per SNR point
NUM_TARGET_ERRORS = 100 # stop early once this many block errors collected
# Eb/N0 sweep — values sent to sim_ber
EBNO_DB_RANGE = np.arange(-5, 26, 2.5)
Load DeepMIMO Scenario¶
We load per-path complex coefficients (a) and delays (tau) for each UE.
Path loss is baked into the raw coefficients; we normalise it out below
because link-level simulation controls SNR via the noise variance only.
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dm.download(SCENARIO)
dataset = dm.load(SCENARIO)
ch_params = dm.ChannelParameters()
ch_params.freq_domain = False # time-domain: gives per-path (a, tau)
ch_params.num_paths = N_PATHS
dataset.compute_channels(ch_params)
# Keep only UEs that have at least one active path
active_idxs = np.where(np.array(dataset.num_paths) > 0)[0]
dataset = dataset.trim(idxs=active_idxs)
print(f"Active UEs: {dataset.n_ue:,}")
# Extract arrays once so the generator is fast
all_channels = np.array(dataset.channels) # [n_ue, 1, N_BS_ANT, N_PATHS]
all_toas = np.array(dataset.toa) # [n_ue, max_paths]
all_num_paths = np.array(dataset.num_paths) # [n_ue]
print(f"Channel array: {all_channels.shape} (n_ue, n_ue_ant, n_bs_ant, n_paths)")
dm.download(SCENARIO)
dataset = dm.load(SCENARIO)
ch_params = dm.ChannelParameters()
ch_params.freq_domain = False # time-domain: gives per-path (a, tau)
ch_params.num_paths = N_PATHS
dataset.compute_channels(ch_params)
# Keep only UEs that have at least one active path
active_idxs = np.where(np.array(dataset.num_paths) > 0)[0]
dataset = dataset.trim(idxs=active_idxs)
print(f"Active UEs: {dataset.n_ue:,}")
# Extract arrays once so the generator is fast
all_channels = np.array(dataset.channels) # [n_ue, 1, N_BS_ANT, N_PATHS]
all_toas = np.array(dataset.toa) # [n_ue, max_paths]
all_num_paths = np.array(dataset.num_paths) # [n_ue]
print(f"Channel array: {all_channels.shape} (n_ue, n_ue_ant, n_bs_ant, n_paths)")
Scenario "asu_campus_3p5" already exists in /home/joao/DeepMIMO/docs/applications/deepmimo_scenarios Loading TXRX PAIR: TXset 1 (tx_idx 0) & RXset 0 (rx_idxs 131931)
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Active UEs: 85,157 Channel array: (85157, 1, 8, 5) (n_ue, n_ue_ant, n_bs_ant, n_paths)
Build a 5G NR PUSCH Transmitter / Receiver¶
We use Sionna's default PUSCHConfig (15 kHz SCS, QPSK, rate-1/2 LDPC,
one 5G NR slot). The transmitter produces frequency-domain resource grids;
the receiver runs LS channel estimation, LMMSE equalisation, and LDPC
decoding.
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pusch_config = PUSCHConfig() # 5G NR defaults: 1 UE, 1 TX antenna
pusch_tx = PUSCHTransmitter(pusch_config, output_domain="freq")
pusch_rx = PUSCHReceiver(pusch_tx, input_domain="freq")
resource_grid = pusch_tx.resource_grid
print(f"Subcarrier spacing : {resource_grid.subcarrier_spacing / 1e3:.0f} kHz")
print(f"OFDM symbols : {resource_grid.num_ofdm_symbols}")
print(f"Subcarriers : {resource_grid.fft_size}")
print(f"Bandwidth : {resource_grid.bandwidth / 1e6:.2f} MHz")
# _tb_size is not part of the public API yet — derive it from one forward pass
_, _b_probe = pusch_tx(1)
print(f"Transport block : {_b_probe.shape[-1]} bits")
pusch_config = PUSCHConfig() # 5G NR defaults: 1 UE, 1 TX antenna
pusch_tx = PUSCHTransmitter(pusch_config, output_domain="freq")
pusch_rx = PUSCHReceiver(pusch_tx, input_domain="freq")
resource_grid = pusch_tx.resource_grid
print(f"Subcarrier spacing : {resource_grid.subcarrier_spacing / 1e3:.0f} kHz")
print(f"OFDM symbols : {resource_grid.num_ofdm_symbols}")
print(f"Subcarriers : {resource_grid.fft_size}")
print(f"Bandwidth : {resource_grid.bandwidth / 1e6:.2f} MHz")
# _tb_size is not part of the public API yet — derive it from one forward pass
_, _b_probe = pusch_tx(1)
print(f"Transport block : {_b_probe.shape[-1]} bits")
Subcarrier spacing : 15 kHz OFDM symbols : 14 Subcarriers : 48 Bandwidth : 0.72 MHz Transport block : 1352 bits
Create a DeepMIMO CIR Generator¶
Sionna's CIRDataset wraps any Python generator that yields (a, tau):
a— complex path coefficients, shape[num_rx, num_rx_ant, num_tx, num_tx_ant, num_paths, num_time_steps]tau— path delays in seconds, shape[num_rx, num_tx, num_paths]
Our uplink setup: UE is the single-antenna transmitter (TX), BS is the 8-antenna receiver (RX). DeepMIMO stores the downlink channel (BS → UE), so we apply channel reciprocity: H_UL = H_DL^H (conjugate transpose).
We also normalise each channel realisation to unit total power so that
the noise variance no fully controls SNR — the standard convention for
link-level BLER simulation.
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def deepmimo_ul_cir_gen(rng_seed: int = 0) -> Generator[tuple[torch.Tensor, torch.Tensor]]:
"""Infinite generator: yields uplink CIR tuples from the DeepMIMO dataset.
Each call draws a random UE, converts its downlink channel to uplink via
reciprocity, normalises it to unit power, and packs it into the shape
expected by CIRDataset.
"""
n_ue = dataset.n_ue
rng = np.random.default_rng(rng_seed)
# Shuffle once; cycle through the dataset infinitely so sim_ber never
# runs out of UEs regardless of batch size or iteration count.
order = rng.permutation(n_ue).tolist()
cursor = 0
while True:
i = order[cursor % n_ue]
cursor += 1
h_dl = all_channels[i] # [1, N_BS_ANT, N_PATHS]
# Reciprocity: h_ul[rx_bs, tx_ue] = conj(h_dl[rx_ue, tx_bs])
h_ul = np.conj(h_dl.transpose(1, 0, 2)) # [N_BS_ANT, 1, N_PATHS]
n_act = min(int(all_num_paths[i]), N_PATHS)
# Normalise to unit power so SNR = 1/no
pwr = np.sum(np.abs(h_ul[:, :, :n_act]) ** 2)
if pwr > 0:
h_ul = h_ul / np.sqrt(pwr)
# Pack into CIRDataset shape
a = np.zeros((1, N_BS_ANT, 1, 1, N_PATHS, 1), dtype=np.csingle)
tau = np.zeros((1, 1, N_PATHS), dtype=np.single)
a[0, :, 0, :, :n_act, 0] = h_ul[:, :, :n_act]
tau[0, 0, :n_act] = all_toas[i, :n_act]
yield (torch.from_numpy(a).to(torch.complex64), torch.from_numpy(tau).to(torch.float32))
# Wrap the generator in a CIRDataset so it behaves like any Sionna channel model
dm_cir_dataset = CIRDataset(
cir_generator=deepmimo_ul_cir_gen,
batch_size=BATCH_SIZE,
num_rx=1,
num_rx_ant=N_BS_ANT,
num_tx=1,
num_tx_ant=1, # single-antenna UE
num_paths=N_PATHS,
num_time_steps=1,
)
dm_channel = OFDMChannel(dm_cir_dataset, resource_grid)
# Also build a Rayleigh block-fading baseline for comparison
rayleigh_model = RayleighBlockFading(num_rx=1, num_rx_ant=N_BS_ANT, num_tx=1, num_tx_ant=1)
rayleigh_channel = OFDMChannel(rayleigh_model, resource_grid)
def deepmimo_ul_cir_gen(rng_seed: int = 0) -> Generator[tuple[torch.Tensor, torch.Tensor]]:
"""Infinite generator: yields uplink CIR tuples from the DeepMIMO dataset.
Each call draws a random UE, converts its downlink channel to uplink via
reciprocity, normalises it to unit power, and packs it into the shape
expected by CIRDataset.
"""
n_ue = dataset.n_ue
rng = np.random.default_rng(rng_seed)
# Shuffle once; cycle through the dataset infinitely so sim_ber never
# runs out of UEs regardless of batch size or iteration count.
order = rng.permutation(n_ue).tolist()
cursor = 0
while True:
i = order[cursor % n_ue]
cursor += 1
h_dl = all_channels[i] # [1, N_BS_ANT, N_PATHS]
# Reciprocity: h_ul[rx_bs, tx_ue] = conj(h_dl[rx_ue, tx_bs])
h_ul = np.conj(h_dl.transpose(1, 0, 2)) # [N_BS_ANT, 1, N_PATHS]
n_act = min(int(all_num_paths[i]), N_PATHS)
# Normalise to unit power so SNR = 1/no
pwr = np.sum(np.abs(h_ul[:, :, :n_act]) ** 2)
if pwr > 0:
h_ul = h_ul / np.sqrt(pwr)
# Pack into CIRDataset shape
a = np.zeros((1, N_BS_ANT, 1, 1, N_PATHS, 1), dtype=np.csingle)
tau = np.zeros((1, 1, N_PATHS), dtype=np.single)
a[0, :, 0, :, :n_act, 0] = h_ul[:, :, :n_act]
tau[0, 0, :n_act] = all_toas[i, :n_act]
yield (torch.from_numpy(a).to(torch.complex64), torch.from_numpy(tau).to(torch.float32))
# Wrap the generator in a CIRDataset so it behaves like any Sionna channel model
dm_cir_dataset = CIRDataset(
cir_generator=deepmimo_ul_cir_gen,
batch_size=BATCH_SIZE,
num_rx=1,
num_rx_ant=N_BS_ANT,
num_tx=1,
num_tx_ant=1, # single-antenna UE
num_paths=N_PATHS,
num_time_steps=1,
)
dm_channel = OFDMChannel(dm_cir_dataset, resource_grid)
# Also build a Rayleigh block-fading baseline for comparison
rayleigh_model = RayleighBlockFading(num_rx=1, num_rx_ant=N_BS_ANT, num_tx=1, num_tx_ant=1)
rayleigh_channel = OFDMChannel(rayleigh_model, resource_grid)
Define the Monte Carlo Function¶
sim_ber expects a callable mc_fun(batch_size, ebno_db) -> (b, b_hat).
We build one factory that wires the chosen channel into the PUSCH pipeline.
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def make_mc_fun(channel: torch.nn.Module) -> object:
"""Return a Monte Carlo step function that uses the given channel block."""
def mc_fun(batch_size: int, ebno_db: float) -> tuple[torch.Tensor, torch.Tensor]:
# Convert Eb/N0 [dB] to noise variance per complex symbol
no = ebnodb2no(
ebno_db,
pusch_config.tb.num_bits_per_symbol,
pusch_config.tb.target_coderate,
resource_grid,
)
x, b = pusch_tx(batch_size) # transmit resource grid + info bits
y = channel(x, no) # apply fading channel + AWGN noise
b_hat = pusch_rx(y, no) # LS estimation + LMMSE + LDPC decode
return b, b_hat
return mc_fun
# Quick sanity check at 10 dB before running the full sweep.
# Note: CIRDataset has a fixed batch size, so we must use BATCH_SIZE here.
no_test = ebnodb2no(
10.0, pusch_config.tb.num_bits_per_symbol, pusch_config.tb.target_coderate, resource_grid
)
x_t, b_t = pusch_tx(BATCH_SIZE)
y_t = dm_channel(x_t, no_test)
b_hat_t = pusch_rx(y_t, no_test)
print(f"Sanity check at 10 dB Eb/N0: BER = {compute_ber(b_t, b_hat_t).item():.4f}")
def make_mc_fun(channel: torch.nn.Module) -> object:
"""Return a Monte Carlo step function that uses the given channel block."""
def mc_fun(batch_size: int, ebno_db: float) -> tuple[torch.Tensor, torch.Tensor]:
# Convert Eb/N0 [dB] to noise variance per complex symbol
no = ebnodb2no(
ebno_db,
pusch_config.tb.num_bits_per_symbol,
pusch_config.tb.target_coderate,
resource_grid,
)
x, b = pusch_tx(batch_size) # transmit resource grid + info bits
y = channel(x, no) # apply fading channel + AWGN noise
b_hat = pusch_rx(y, no) # LS estimation + LMMSE + LDPC decode
return b, b_hat
return mc_fun
# Quick sanity check at 10 dB before running the full sweep.
# Note: CIRDataset has a fixed batch size, so we must use BATCH_SIZE here.
no_test = ebnodb2no(
10.0, pusch_config.tb.num_bits_per_symbol, pusch_config.tb.target_coderate, resource_grid
)
x_t, b_t = pusch_tx(BATCH_SIZE)
y_t = dm_channel(x_t, no_test)
b_hat_t = pusch_rx(y_t, no_test)
print(f"Sanity check at 10 dB Eb/N0: BER = {compute_ber(b_t, b_hat_t).item():.4f}")
Sanity check at 10 dB Eb/N0: BER = 0.0210
Run BLER Simulation¶
sim_ber collects block errors at each SNR point until NUM_TARGET_ERRORS
are seen or MAX_MC_ITER batches are exhausted, then moves to the next
SNR point. Early stopping kicks in once BLER drops to zero.
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print("Simulating DeepMIMO channel BLER ...")
ber_dm, bler_dm = sim_ber(
mc_fun=make_mc_fun(dm_channel),
ebno_dbs=torch.tensor(EBNO_DB_RANGE, dtype=torch.float32),
batch_size=BATCH_SIZE,
max_mc_iter=MAX_MC_ITER,
num_target_block_errors=NUM_TARGET_ERRORS,
early_stop=True,
verbose=True,
)
print("\nSimulating Rayleigh fading BLER ...")
ber_rl, bler_rl = sim_ber(
mc_fun=make_mc_fun(rayleigh_channel),
ebno_dbs=torch.tensor(EBNO_DB_RANGE, dtype=torch.float32),
batch_size=BATCH_SIZE,
max_mc_iter=MAX_MC_ITER,
num_target_block_errors=NUM_TARGET_ERRORS,
early_stop=True,
verbose=True,
)
print("Simulating DeepMIMO channel BLER ...")
ber_dm, bler_dm = sim_ber(
mc_fun=make_mc_fun(dm_channel),
ebno_dbs=torch.tensor(EBNO_DB_RANGE, dtype=torch.float32),
batch_size=BATCH_SIZE,
max_mc_iter=MAX_MC_ITER,
num_target_block_errors=NUM_TARGET_ERRORS,
early_stop=True,
verbose=True,
)
print("\nSimulating Rayleigh fading BLER ...")
ber_rl, bler_rl = sim_ber(
mc_fun=make_mc_fun(rayleigh_channel),
ebno_dbs=torch.tensor(EBNO_DB_RANGE, dtype=torch.float32),
batch_size=BATCH_SIZE,
max_mc_iter=MAX_MC_ITER,
num_target_block_errors=NUM_TARGET_ERRORS,
early_stop=True,
verbose=True,
)
Simulating DeepMIMO channel BLER ...
EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status
---------------------------------------------------------------------------------------------------------------------------------------
-5.0 | 4.1068e-01 | 1.0000e+00 | 35535 | 86528 | 64 | 64 | 0.0 |iter: 0/100
-5.0 | 4.0800e-01 | 1.0000e+00 | 70607 | 173056 | 128 | 128 | 0.0 |iter: 1/100
-5.0 | 4.0800e-01 | 1.0000e+00 | 70607 | 173056 | 128 | 128 | 0.0 |reached target block errors
-2.5 | 3.4642e-01 | 1.0000e+00 | 29975 | 86528 | 64 | 64 | 0.0 |iter: 0/100
-2.5 | 3.5134e-01 | 1.0000e+00 | 60802 | 173056 | 128 | 128 | 0.0 |iter: 1/100
-2.5 | 3.5134e-01 | 1.0000e+00 | 60802 | 173056 | 128 | 128 | 0.0 |reached target block errors
0.0 | 3.0357e-01 | 1.0000e+00 | 26267 | 86528 | 64 | 64 | 0.0 |iter: 0/100
0.0 | 3.0324e-01 | 1.0000e+00 | 52478 | 173056 | 128 | 128 | 0.0 |iter: 1/100
0.0 | 3.0324e-01 | 1.0000e+00 | 52478 | 173056 | 128 | 128 | 0.0 |reached target block errors
2.5 | 2.4305e-01 | 1.0000e+00 | 21031 | 86528 | 64 | 64 | 0.0 |iter: 0/100
2.5 | 2.4071e-01 | 9.9219e-01 | 41656 | 173056 | 127 | 128 | 0.0 |iter: 1/100
2.5 | 2.4071e-01 | 9.9219e-01 | 41656 | 173056 | 127 | 128 | 0.0 |reached target block errors
5.0 | 1.2759e-01 | 7.3438e-01 | 11040 | 86528 | 47 | 64 | 0.0 |iter: 0/100
5.0 | 1.4541e-01 | 7.8125e-01 | 25164 | 173056 | 100 | 128 | 0.0 |iter: 1/100
5.0 | 1.4541e-01 | 7.8125e-01 | 25164 | 173056 | 100 | 128 | 0.0 |reached target block errors
7.5 | 7.3595e-02 | 3.9062e-01 | 6368 | 86528 | 25 | 64 | 0.0 |iter: 0/100
7.5 | 5.7334e-02 | 3.5938e-01 | 9922 | 173056 | 46 | 128 | 0.0 |iter: 1/100
7.5 | 6.2404e-02 | 3.6979e-01 | 16199 | 259584 | 71 | 192 | 0.1 |iter: 2/100
7.5 | 6.4996e-02 | 3.7109e-01 | 22496 | 346112 | 95 | 256 | 0.1 |iter: 3/100
7.5 | 6.5828e-02 | 3.7500e-01 | 28480 | 432640 | 120 | 320 | 0.1 |iter: 4/100
7.5 | 6.5828e-02 | 3.7500e-01 | 28480 | 432640 | 120 | 320 | 0.1 |reached target block errors
10.0 | 3.2914e-02 | 1.4062e-01 | 2848 | 86528 | 9 | 64 | 0.0 |iter: 0/100
10.0 | 4.3252e-02 | 2.1094e-01 | 7485 | 173056 | 27 | 128 | 0.0 |iter: 1/100
10.0 | 3.6416e-02 | 1.9792e-01 | 9453 | 259584 | 38 | 192 | 0.1 |iter: 2/100
10.0 | 3.2235e-02 | 1.8359e-01 | 11157 | 346112 | 47 | 256 | 0.1 |iter: 3/100
10.0 | 3.2172e-02 | 1.7813e-01 | 13919 | 432640 | 57 | 320 | 0.1 |iter: 4/100
10.0 | 3.2057e-02 | 1.7969e-01 | 16643 | 519168 | 69 | 384 | 0.1 |iter: 5/100
10.0 | 3.2193e-02 | 1.7857e-01 | 19499 | 605696 | 80 | 448 | 0.1 |iter: 6/100
10.0 | 3.4037e-02 | 1.8555e-01 | 23561 | 692224 | 95 | 512 | 0.2 |iter: 7/100
10.0 | 3.3832e-02 | 1.8403e-01 | 26347 | 778752 | 106 | 576 | 0.2 |iter: 8/100
10.0 | 3.3832e-02 | 1.8403e-01 | 26347 | 778752 | 106 | 576 | 0.2 |reached target block errors
12.5 | 1.1129e-02 | 6.2500e-02 | 963 | 86528 | 4 | 64 | 0.0 |iter: 0/100
12.5 | 8.8237e-03 | 5.4688e-02 | 1527 | 173056 | 7 | 128 | 0.0 |iter: 1/100
12.5 | 1.0536e-02 | 6.7708e-02 | 2735 | 259584 | 13 | 192 | 0.1 |iter: 2/100
12.5 | 1.0343e-02 | 7.4219e-02 | 3580 | 346112 | 19 | 256 | 0.1 |iter: 3/100
12.5 | 1.3785e-02 | 8.7500e-02 | 5964 | 432640 | 28 | 320 | 0.1 |iter: 4/100
12.5 | 1.7174e-02 | 9.8958e-02 | 8916 | 519168 | 38 | 384 | 0.1 |iter: 5/100
12.5 | 1.6333e-02 | 9.3750e-02 | 9893 | 605696 | 42 | 448 | 0.1 |iter: 6/100
12.5 | 1.6301e-02 | 9.3750e-02 | 11284 | 692224 | 48 | 512 | 0.2 |iter: 7/100
12.5 | 1.4539e-02 | 8.5069e-02 | 11322 | 778752 | 49 | 576 | 0.2 |iter: 8/100
12.5 | 1.4932e-02 | 8.5938e-02 | 12920 | 865280 | 55 | 640 | 0.2 |iter: 9/100
12.5 | 1.6749e-02 | 9.2330e-02 | 15942 | 951808 | 65 | 704 | 0.2 |iter: 10/100
12.5 | 1.7918e-02 | 1.0026e-01 | 18605 | 1038336 | 77 | 768 | 0.2 |iter: 11/100
12.5 | 1.8352e-02 | 1.0337e-01 | 20643 | 1124864 | 86 | 832 | 0.3 |iter: 12/100
12.5 | 1.7780e-02 | 1.0156e-01 | 21538 | 1211392 | 91 | 896 | 0.3 |iter: 13/100
12.5 | 1.7828e-02 | 1.0208e-01 | 23139 | 1297920 | 98 | 960 | 0.3 |iter: 14/100
12.5 | 1.7159e-02 | 9.8633e-02 | 23756 | 1384448 | 101 | 1024 | 0.3 |iter: 15/100
12.5 | 1.7159e-02 | 9.8633e-02 | 23756 | 1384448 | 101 | 1024 | 0.3 |reached target block errors
15.0 | 1.3418e-02 | 6.2500e-02 | 1161 | 86528 | 4 | 64 | 0.0 |iter: 0/100
15.0 | 6.7088e-03 | 3.1250e-02 | 1161 | 173056 | 4 | 128 | 0.0 |iter: 1/100
15.0 | 6.1213e-03 | 2.6042e-02 | 1589 | 259584 | 5 | 192 | 0.1 |iter: 2/100
15.0 | 7.4860e-03 | 3.5156e-02 | 2591 | 346112 | 9 | 256 | 0.1 |iter: 3/100
15.0 | 7.0497e-03 | 3.4375e-02 | 3050 | 432640 | 11 | 320 | 0.1 |iter: 4/100
15.0 | 6.6279e-03 | 3.3854e-02 | 3441 | 519168 | 13 | 384 | 0.1 |iter: 5/100
15.0 | 6.0657e-03 | 3.1250e-02 | 3674 | 605696 | 14 | 448 | 0.1 |iter: 6/100
15.0 | 6.9703e-03 | 3.5156e-02 | 4825 | 692224 | 18 | 512 | 0.2 |iter: 7/100
15.0 | 7.2655e-03 | 3.6458e-02 | 5658 | 778752 | 21 | 576 | 0.2 |iter: 8/100
15.0 | 6.5389e-03 | 3.2813e-02 | 5658 | 865280 | 21 | 640 | 0.2 |iter: 9/100
15.0 | 6.3616e-03 | 3.4091e-02 | 6055 | 951808 | 24 | 704 | 0.2 |iter: 10/100
15.0 | 6.8773e-03 | 3.7760e-02 | 7141 | 1038336 | 29 | 768 | 0.2 |iter: 11/100
15.0 | 6.6488e-03 | 3.7260e-02 | 7479 | 1124864 | 31 | 832 | 0.3 |iter: 12/100
15.0 | 6.1739e-03 | 3.4598e-02 | 7479 | 1211392 | 31 | 896 | 0.3 |iter: 13/100
15.0 | 6.1868e-03 | 3.3333e-02 | 8030 | 1297920 | 32 | 960 | 0.3 |iter: 14/100
15.0 | 6.4459e-03 | 3.4180e-02 | 8924 | 1384448 | 35 | 1024 | 0.3 |iter: 15/100
15.0 | 6.5643e-03 | 3.4926e-02 | 9656 | 1470976 | 38 | 1088 | 0.3 |iter: 16/100
15.0 | 6.4963e-03 | 3.4722e-02 | 10118 | 1557504 | 40 | 1152 | 0.4 |iter: 17/100
15.0 | 6.6519e-03 | 3.6184e-02 | 10936 | 1644032 | 44 | 1216 | 0.4 |iter: 18/100
15.0 | 7.0919e-03 | 3.8281e-02 | 12273 | 1730560 | 49 | 1280 | 0.4 |iter: 19/100
15.0 | 6.9369e-03 | 3.7946e-02 | 12605 | 1817088 | 51 | 1344 | 0.4 |iter: 20/100
15.0 | 7.4332e-03 | 4.1193e-02 | 14150 | 1903616 | 58 | 1408 | 0.4 |iter: 21/100
15.0 | 7.1100e-03 | 3.9402e-02 | 14150 | 1990144 | 58 | 1472 | 0.5 |iter: 22/100
15.0 | 6.8138e-03 | 3.7760e-02 | 14150 | 2076672 | 58 | 1536 | 0.5 |iter: 23/100
15.0 | 7.0974e-03 | 3.8125e-02 | 15353 | 2163200 | 61 | 1600 | 0.5 |iter: 24/100
15.0 | 7.0102e-03 | 3.7260e-02 | 15771 | 2249728 | 62 | 1664 | 0.5 |iter: 25/100
15.0 | 6.9158e-03 | 3.7037e-02 | 16157 | 2336256 | 64 | 1728 | 0.5 |iter: 26/100
15.0 | 6.8628e-03 | 3.6830e-02 | 16627 | 2422784 | 66 | 1792 | 0.6 |iter: 27/100
15.0 | 6.8991e-03 | 3.6638e-02 | 17312 | 2509312 | 68 | 1856 | 0.6 |iter: 28/100
15.0 | 6.9280e-03 | 3.6979e-02 | 17984 | 2595840 | 71 | 1920 | 0.6 |iter: 29/100
15.0 | 7.1683e-03 | 3.8306e-02 | 19228 | 2682368 | 76 | 1984 | 0.6 |iter: 30/100
15.0 | 7.3957e-03 | 3.9062e-02 | 20478 | 2768896 | 80 | 2048 | 0.6 |iter: 31/100
15.0 | 7.2644e-03 | 3.8826e-02 | 20743 | 2855424 | 82 | 2112 | 0.6 |iter: 32/100
15.0 | 7.1357e-03 | 3.8143e-02 | 20993 | 2941952 | 83 | 2176 | 0.7 |iter: 33/100
15.0 | 7.0095e-03 | 3.7946e-02 | 21228 | 3028480 | 85 | 2240 | 0.7 |iter: 34/100
15.0 | 7.2042e-03 | 3.8628e-02 | 22441 | 3115008 | 89 | 2304 | 0.7 |iter: 35/100
15.0 | 7.1653e-03 | 3.8429e-02 | 22940 | 3201536 | 91 | 2368 | 0.7 |iter: 36/100
15.0 | 6.9822e-03 | 3.7829e-02 | 22958 | 3288064 | 92 | 2432 | 0.7 |iter: 37/100
15.0 | 6.8719e-03 | 3.7260e-02 | 23190 | 3374592 | 93 | 2496 | 0.8 |iter: 38/100
15.0 | 6.7992e-03 | 3.7109e-02 | 23533 | 3461120 | 95 | 2560 | 0.8 |iter: 39/100
15.0 | 6.7777e-03 | 3.7348e-02 | 24045 | 3547648 | 98 | 2624 | 0.8 |iter: 40/100
15.0 | 6.7768e-03 | 3.7202e-02 | 24628 | 3634176 | 100 | 2688 | 0.8 |iter: 41/100
15.0 | 6.7768e-03 | 3.7202e-02 | 24628 | 3634176 | 100 | 2688 | 0.8 |reached target block errors
17.5 | 0.0000e+00 | 0.0000e+00 | 0 | 86528 | 0 | 64 | 0.0 |iter: 0/100
17.5 | 0.0000e+00 | 0.0000e+00 | 0 | 173056 | 0 | 128 | 0.0 |iter: 1/100
17.5 | 0.0000e+00 | 0.0000e+00 | 0 | 259584 | 0 | 192 | 0.1 |iter: 2/100
17.5 | 0.0000e+00 | 0.0000e+00 | 0 | 346112 | 0 | 256 | 0.1 |iter: 3/100
17.5 | 6.7955e-04 | 3.1250e-03 | 294 | 432640 | 1 | 320 | 0.1 |iter: 4/100
17.5 | 1.0189e-03 | 5.2083e-03 | 529 | 519168 | 2 | 384 | 0.1 |iter: 5/100
17.5 | 2.5541e-03 | 1.1161e-02 | 1547 | 605696 | 5 | 448 | 0.1 |iter: 6/100
17.5 | 3.0973e-03 | 1.3672e-02 | 2144 | 692224 | 7 | 512 | 0.2 |iter: 7/100
17.5 | 2.8584e-03 | 1.3889e-02 | 2226 | 778752 | 8 | 576 | 0.2 |iter: 8/100
17.5 | 3.2648e-03 | 1.8750e-02 | 2825 | 865280 | 12 | 640 | 0.2 |iter: 9/100
17.5 | 3.2738e-03 | 1.8466e-02 | 3116 | 951808 | 13 | 704 | 0.2 |iter: 10/100
17.5 | 3.1916e-03 | 1.8229e-02 | 3314 | 1038336 | 14 | 768 | 0.2 |iter: 11/100
17.5 | 3.2733e-03 | 1.8029e-02 | 3682 | 1124864 | 15 | 832 | 0.3 |iter: 12/100
17.5 | 3.0395e-03 | 1.6741e-02 | 3682 | 1211392 | 15 | 896 | 0.3 |iter: 13/100
17.5 | 3.1165e-03 | 1.7708e-02 | 4045 | 1297920 | 17 | 960 | 0.3 |iter: 14/100
17.5 | 2.9535e-03 | 1.7578e-02 | 4089 | 1384448 | 18 | 1024 | 0.3 |iter: 15/100
17.5 | 3.0979e-03 | 2.0221e-02 | 4557 | 1470976 | 22 | 1088 | 0.3 |iter: 16/100
17.5 | 3.2668e-03 | 2.2569e-02 | 5088 | 1557504 | 26 | 1152 | 0.4 |iter: 17/100
17.5 | 3.0948e-03 | 2.1382e-02 | 5088 | 1644032 | 26 | 1216 | 0.4 |iter: 18/100
17.5 | 3.1366e-03 | 2.1875e-02 | 5428 | 1730560 | 28 | 1280 | 0.4 |iter: 19/100
17.5 | 2.9872e-03 | 2.0833e-02 | 5428 | 1817088 | 28 | 1344 | 0.4 |iter: 20/100
17.5 | 2.8514e-03 | 1.9886e-02 | 5428 | 1903616 | 28 | 1408 | 0.4 |iter: 21/100
17.5 | 2.7882e-03 | 1.9701e-02 | 5549 | 1990144 | 29 | 1472 | 0.4 |iter: 22/100
17.5 | 2.7770e-03 | 1.9531e-02 | 5767 | 2076672 | 30 | 1536 | 0.5 |iter: 23/100
17.5 | 2.7482e-03 | 1.9375e-02 | 5945 | 2163200 | 31 | 1600 | 0.5 |iter: 24/100
17.5 | 2.7546e-03 | 1.9231e-02 | 6197 | 2249728 | 32 | 1664 | 0.5 |iter: 25/100
17.5 | 2.6525e-03 | 1.8519e-02 | 6197 | 2336256 | 32 | 1728 | 0.5 |iter: 26/100
17.5 | 2.7716e-03 | 1.8973e-02 | 6715 | 2422784 | 34 | 1792 | 0.5 |iter: 27/100
17.5 | 2.6760e-03 | 1.8319e-02 | 6715 | 2509312 | 34 | 1856 | 0.6 |iter: 28/100
17.5 | 2.7983e-03 | 1.8750e-02 | 7264 | 2595840 | 36 | 1920 | 0.6 |iter: 29/100
17.5 | 2.7081e-03 | 1.8145e-02 | 7264 | 2682368 | 36 | 1984 | 0.6 |iter: 30/100
17.5 | 2.6776e-03 | 1.8066e-02 | 7414 | 2768896 | 37 | 2048 | 0.6 |iter: 31/100
17.5 | 2.5965e-03 | 1.7519e-02 | 7414 | 2855424 | 37 | 2112 | 0.6 |iter: 32/100
17.5 | 2.5925e-03 | 1.7463e-02 | 7627 | 2941952 | 38 | 2176 | 0.7 |iter: 33/100
17.5 | 2.5204e-03 | 1.7411e-02 | 7633 | 3028480 | 39 | 2240 | 0.7 |iter: 34/100
17.5 | 2.5859e-03 | 1.7795e-02 | 8055 | 3115008 | 41 | 2304 | 0.7 |iter: 35/100
17.5 | 2.5160e-03 | 1.7314e-02 | 8055 | 3201536 | 41 | 2368 | 0.7 |iter: 36/100
17.5 | 2.6468e-03 | 1.7681e-02 | 8703 | 3288064 | 43 | 2432 | 0.7 |iter: 37/100
17.5 | 2.5790e-03 | 1.7228e-02 | 8703 | 3374592 | 43 | 2496 | 0.8 |iter: 38/100
17.5 | 2.6006e-03 | 1.7188e-02 | 9001 | 3461120 | 44 | 2560 | 0.8 |iter: 39/100
17.5 | 2.6804e-03 | 1.7912e-02 | 9509 | 3547648 | 47 | 2624 | 0.8 |iter: 40/100
17.5 | 2.6176e-03 | 1.7857e-02 | 9513 | 3634176 | 48 | 2688 | 0.8 |iter: 41/100
17.5 | 2.7062e-03 | 1.8169e-02 | 10069 | 3720704 | 50 | 2752 | 0.8 |iter: 42/100
17.5 | 2.7366e-03 | 1.8111e-02 | 10419 | 3807232 | 51 | 2816 | 0.9 |iter: 43/100
17.5 | 2.6758e-03 | 1.7708e-02 | 10419 | 3893760 | 51 | 2880 | 0.9 |iter: 44/100
17.5 | 2.6719e-03 | 1.7663e-02 | 10635 | 3980288 | 52 | 2944 | 0.9 |iter: 45/100
17.5 | 2.7865e-03 | 1.7952e-02 | 11332 | 4066816 | 54 | 3008 | 0.9 |iter: 46/100
17.5 | 2.7802e-03 | 1.8229e-02 | 11547 | 4153344 | 56 | 3072 | 0.9 |iter: 47/100
17.5 | 2.8355e-03 | 1.8814e-02 | 12022 | 4239872 | 59 | 3136 | 0.9 |iter: 48/100
17.5 | 2.8273e-03 | 1.8750e-02 | 12232 | 4326400 | 60 | 3200 | 1.0 |iter: 49/100
17.5 | 2.8140e-03 | 1.8689e-02 | 12418 | 4412928 | 61 | 3264 | 1.0 |iter: 50/100
17.5 | 2.8588e-03 | 1.8930e-02 | 12863 | 4499456 | 63 | 3328 | 1.0 |iter: 51/100
17.5 | 2.8410e-03 | 1.8868e-02 | 13029 | 4585984 | 64 | 3392 | 1.0 |iter: 52/100
17.5 | 2.7884e-03 | 1.8519e-02 | 13029 | 4672512 | 64 | 3456 | 1.0 |iter: 53/100
17.5 | 2.7377e-03 | 1.8182e-02 | 13029 | 4759040 | 64 | 3520 | 1.1 |iter: 54/100
17.5 | 2.6888e-03 | 1.7857e-02 | 13029 | 4845568 | 64 | 3584 | 1.1 |iter: 55/100
17.5 | 2.6877e-03 | 1.7818e-02 | 13256 | 4932096 | 65 | 3648 | 1.1 |iter: 56/100
17.5 | 2.7021e-03 | 1.8050e-02 | 13561 | 5018624 | 67 | 3712 | 1.1 |iter: 57/100
17.5 | 2.7012e-03 | 1.8008e-02 | 13790 | 5105152 | 68 | 3776 | 1.1 |iter: 58/100
17.5 | 2.6562e-03 | 1.7708e-02 | 13790 | 5191680 | 68 | 3840 | 1.2 |iter: 59/100
17.5 | 2.6126e-03 | 1.7418e-02 | 13790 | 5278208 | 68 | 3904 | 1.2 |iter: 60/100
17.5 | 2.5752e-03 | 1.7389e-02 | 13815 | 5364736 | 69 | 3968 | 1.2 |iter: 61/100
17.5 | 2.5346e-03 | 1.7361e-02 | 13817 | 5451264 | 70 | 4032 | 1.2 |iter: 62/100
17.5 | 2.5721e-03 | 1.7578e-02 | 14244 | 5537792 | 72 | 4096 | 1.2 |iter: 63/100
17.5 | 2.5326e-03 | 1.7308e-02 | 14244 | 5624320 | 72 | 4160 | 1.3 |iter: 64/100
17.5 | 2.4942e-03 | 1.7045e-02 | 14244 | 5710848 | 72 | 4224 | 1.3 |iter: 65/100
17.5 | 2.4570e-03 | 1.6791e-02 | 14244 | 5797376 | 72 | 4288 | 1.3 |iter: 66/100
17.5 | 2.4718e-03 | 1.6774e-02 | 14544 | 5883904 | 73 | 4352 | 1.3 |iter: 67/100
17.5 | 2.4360e-03 | 1.6531e-02 | 14544 | 5970432 | 73 | 4416 | 1.3 |iter: 68/100
17.5 | 2.4048e-03 | 1.6518e-02 | 14566 | 6056960 | 74 | 4480 | 1.4 |iter: 69/100
17.5 | 2.3837e-03 | 1.6505e-02 | 14644 | 6143488 | 75 | 4544 | 1.4 |iter: 70/100
17.5 | 2.4860e-03 | 1.6927e-02 | 15488 | 6230016 | 78 | 4608 | 1.4 |iter: 71/100
17.5 | 2.6131e-03 | 1.7337e-02 | 16506 | 6316544 | 81 | 4672 | 1.4 |iter: 72/100
17.5 | 2.5778e-03 | 1.7103e-02 | 16506 | 6403072 | 81 | 4736 | 1.4 |iter: 73/100
17.5 | 2.6387e-03 | 1.7292e-02 | 17124 | 6489600 | 83 | 4800 | 1.5 |iter: 74/100
17.5 | 2.6294e-03 | 1.7270e-02 | 17291 | 6576128 | 84 | 4864 | 1.5 |iter: 75/100
17.5 | 2.6186e-03 | 1.7248e-02 | 17447 | 6662656 | 85 | 4928 | 1.5 |iter: 76/100
17.5 | 2.6301e-03 | 1.7628e-02 | 17751 | 6749184 | 88 | 4992 | 1.5 |iter: 77/100
17.5 | 2.5968e-03 | 1.7405e-02 | 17751 | 6835712 | 88 | 5056 | 1.5 |iter: 78/100
17.5 | 2.7072e-03 | 1.7773e-02 | 18740 | 6922240 | 91 | 5120 | 1.6 |iter: 79/100
17.5 | 2.6738e-03 | 1.7554e-02 | 18740 | 7008768 | 91 | 5184 | 1.6 |iter: 80/100
17.5 | 2.6671e-03 | 1.7530e-02 | 18924 | 7095296 | 92 | 5248 | 1.6 |iter: 81/100
17.5 | 2.6801e-03 | 1.7696e-02 | 19248 | 7181824 | 94 | 5312 | 1.6 |iter: 82/100
17.5 | 2.7123e-03 | 1.7857e-02 | 19714 | 7268352 | 96 | 5376 | 1.6 |iter: 83/100
17.5 | 2.6804e-03 | 1.7647e-02 | 19714 | 7354880 | 96 | 5440 | 1.7 |iter: 84/100
17.5 | 2.6492e-03 | 1.7442e-02 | 19714 | 7441408 | 96 | 5504 | 1.7 |iter: 85/100
17.5 | 2.6188e-03 | 1.7241e-02 | 19714 | 7527936 | 96 | 5568 | 1.7 |iter: 86/100
17.5 | 2.6515e-03 | 1.7401e-02 | 20190 | 7614464 | 98 | 5632 | 1.7 |iter: 87/100
17.5 | 2.6586e-03 | 1.7381e-02 | 20474 | 7700992 | 99 | 5696 | 1.7 |iter: 88/100
17.5 | 2.6546e-03 | 1.7361e-02 | 20673 | 7787520 | 100 | 5760 | 1.8 |iter: 89/100
17.5 | 2.6546e-03 | 1.7361e-02 | 20673 | 7787520 | 100 | 5760 | 1.8 |reached target block errors
20.0 | 0.0000e+00 | 0.0000e+00 | 0 | 86528 | 0 | 64 | 0.0 |iter: 0/100
20.0 | 1.3695e-03 | 7.8125e-03 | 237 | 173056 | 1 | 128 | 0.0 |iter: 1/100
20.0 | 1.7990e-03 | 1.0417e-02 | 467 | 259584 | 2 | 192 | 0.1 |iter: 2/100
20.0 | 1.3493e-03 | 7.8125e-03 | 467 | 346112 | 2 | 256 | 0.1 |iter: 3/100
20.0 | 1.0794e-03 | 6.2500e-03 | 467 | 432640 | 2 | 320 | 0.1 |iter: 4/100
20.0 | 8.9952e-04 | 5.2083e-03 | 467 | 519168 | 2 | 384 | 0.1 |iter: 5/100
20.0 | 7.7101e-04 | 4.4643e-03 | 467 | 605696 | 2 | 448 | 0.1 |iter: 6/100
20.0 | 9.1011e-04 | 5.8594e-03 | 630 | 692224 | 3 | 512 | 0.2 |iter: 7/100
20.0 | 8.0899e-04 | 5.2083e-03 | 630 | 778752 | 3 | 576 | 0.2 |iter: 8/100
20.0 | 7.2809e-04 | 4.6875e-03 | 630 | 865280 | 3 | 640 | 0.2 |iter: 9/100
20.0 | 6.6190e-04 | 4.2614e-03 | 630 | 951808 | 3 | 704 | 0.2 |iter: 10/100
20.0 | 6.0770e-04 | 5.2083e-03 | 631 | 1038336 | 4 | 768 | 0.2 |iter: 11/100
20.0 | 5.6096e-04 | 4.8077e-03 | 631 | 1124864 | 4 | 832 | 0.3 |iter: 12/100
20.0 | 6.6122e-04 | 5.5804e-03 | 801 | 1211392 | 5 | 896 | 0.3 |iter: 13/100
20.0 | 8.4828e-04 | 6.2500e-03 | 1101 | 1297920 | 6 | 960 | 0.3 |iter: 14/100
20.0 | 7.9526e-04 | 5.8594e-03 | 1101 | 1384448 | 6 | 1024 | 0.3 |iter: 15/100
20.0 | 8.9124e-04 | 6.4338e-03 | 1311 | 1470976 | 7 | 1088 | 0.3 |iter: 16/100
20.0 | 8.4173e-04 | 6.0764e-03 | 1311 | 1557504 | 7 | 1152 | 0.3 |iter: 17/100
20.0 | 1.0024e-03 | 6.5789e-03 | 1648 | 1644032 | 8 | 1216 | 0.4 |iter: 18/100
20.0 | 9.5229e-04 | 6.2500e-03 | 1648 | 1730560 | 8 | 1280 | 0.4 |iter: 19/100
20.0 | 9.0695e-04 | 5.9524e-03 | 1648 | 1817088 | 8 | 1344 | 0.4 |iter: 20/100
20.0 | 8.6572e-04 | 5.6818e-03 | 1648 | 1903616 | 8 | 1408 | 0.4 |iter: 21/100
20.0 | 8.2808e-04 | 5.4348e-03 | 1648 | 1990144 | 8 | 1472 | 0.4 |iter: 22/100
20.0 | 7.9358e-04 | 5.2083e-03 | 1648 | 2076672 | 8 | 1536 | 0.5 |iter: 23/100
20.0 | 7.6183e-04 | 5.0000e-03 | 1648 | 2163200 | 8 | 1600 | 0.5 |iter: 24/100
20.0 | 7.3253e-04 | 4.8077e-03 | 1648 | 2249728 | 8 | 1664 | 0.5 |iter: 25/100
20.0 | 7.0540e-04 | 4.6296e-03 | 1648 | 2336256 | 8 | 1728 | 0.5 |iter: 26/100
20.0 | 6.8021e-04 | 4.4643e-03 | 1648 | 2422784 | 8 | 1792 | 0.5 |iter: 27/100
20.0 | 7.9305e-04 | 4.8491e-03 | 1990 | 2509312 | 9 | 1856 | 0.6 |iter: 28/100
20.0 | 7.6661e-04 | 4.6875e-03 | 1990 | 2595840 | 9 | 1920 | 0.6 |iter: 29/100
20.0 | 7.4188e-04 | 4.5363e-03 | 1990 | 2682368 | 9 | 1984 | 0.6 |iter: 30/100
20.0 | 7.1870e-04 | 4.3945e-03 | 1990 | 2768896 | 9 | 2048 | 0.6 |iter: 31/100
20.0 | 7.8867e-04 | 4.7348e-03 | 2252 | 2855424 | 10 | 2112 | 0.6 |iter: 32/100
20.0 | 7.6548e-04 | 4.5956e-03 | 2252 | 2941952 | 10 | 2176 | 0.7 |iter: 33/100
20.0 | 7.4361e-04 | 4.4643e-03 | 2252 | 3028480 | 10 | 2240 | 0.7 |iter: 34/100
20.0 | 7.2295e-04 | 4.3403e-03 | 2252 | 3115008 | 10 | 2304 | 0.7 |iter: 35/100
20.0 | 7.0341e-04 | 4.2230e-03 | 2252 | 3201536 | 10 | 2368 | 0.7 |iter: 36/100
20.0 | 6.8490e-04 | 4.1118e-03 | 2252 | 3288064 | 10 | 2432 | 0.7 |iter: 37/100
20.0 | 6.6734e-04 | 4.0064e-03 | 2252 | 3374592 | 10 | 2496 | 0.8 |iter: 38/100
20.0 | 6.5066e-04 | 3.9062e-03 | 2252 | 3461120 | 10 | 2560 | 0.8 |iter: 39/100
20.0 | 6.3479e-04 | 3.8110e-03 | 2252 | 3547648 | 10 | 2624 | 0.8 |iter: 40/100
20.0 | 6.1967e-04 | 3.7202e-03 | 2252 | 3634176 | 10 | 2688 | 0.8 |iter: 41/100
20.0 | 6.0526e-04 | 3.6337e-03 | 2252 | 3720704 | 10 | 2752 | 0.8 |iter: 42/100
20.0 | 5.9151e-04 | 3.5511e-03 | 2252 | 3807232 | 10 | 2816 | 0.8 |iter: 43/100
20.0 | 7.2295e-04 | 4.1667e-03 | 2815 | 3893760 | 12 | 2880 | 0.9 |iter: 44/100
20.0 | 7.5849e-04 | 4.4158e-03 | 3019 | 3980288 | 13 | 2944 | 0.9 |iter: 45/100
20.0 | 7.4235e-04 | 4.3218e-03 | 3019 | 4066816 | 13 | 3008 | 0.9 |iter: 46/100
20.0 | 8.1356e-04 | 4.8828e-03 | 3379 | 4153344 | 15 | 3072 | 0.9 |iter: 47/100
20.0 | 7.9696e-04 | 4.7832e-03 | 3379 | 4239872 | 15 | 3136 | 0.9 |iter: 48/100
20.0 | 7.8102e-04 | 4.6875e-03 | 3379 | 4326400 | 15 | 3200 | 1.0 |iter: 49/100
20.0 | 7.6570e-04 | 4.5956e-03 | 3379 | 4412928 | 15 | 3264 | 1.0 |iter: 50/100
20.0 | 7.5520e-04 | 4.8077e-03 | 3398 | 4499456 | 16 | 3328 | 1.0 |iter: 51/100
20.0 | 7.7671e-04 | 5.0118e-03 | 3562 | 4585984 | 17 | 3392 | 1.0 |iter: 52/100
20.0 | 8.1733e-04 | 5.2083e-03 | 3819 | 4672512 | 18 | 3456 | 1.0 |iter: 53/100
20.0 | 9.1321e-04 | 5.6818e-03 | 4346 | 4759040 | 20 | 3520 | 1.1 |iter: 54/100
20.0 | 8.9690e-04 | 5.5804e-03 | 4346 | 4845568 | 20 | 3584 | 1.1 |iter: 55/100
20.0 | 8.8117e-04 | 5.4825e-03 | 4346 | 4932096 | 20 | 3648 | 1.1 |iter: 56/100
20.0 | 8.6597e-04 | 5.3879e-03 | 4346 | 5018624 | 20 | 3712 | 1.1 |iter: 57/100
20.0 | 9.2397e-04 | 5.5614e-03 | 4717 | 5105152 | 21 | 3776 | 1.1 |iter: 58/100
20.0 | 9.0857e-04 | 5.4687e-03 | 4717 | 5191680 | 21 | 3840 | 1.2 |iter: 59/100
20.0 | 9.7022e-04 | 5.6352e-03 | 5121 | 5278208 | 22 | 3904 | 1.2 |iter: 60/100
20.0 | 9.5457e-04 | 5.5444e-03 | 5121 | 5364736 | 22 | 3968 | 1.2 |iter: 61/100
20.0 | 9.3942e-04 | 5.4563e-03 | 5121 | 5451264 | 22 | 4032 | 1.2 |iter: 62/100
20.0 | 9.2474e-04 | 5.3711e-03 | 5121 | 5537792 | 22 | 4096 | 1.2 |iter: 63/100
20.0 | 9.8305e-04 | 5.7692e-03 | 5529 | 5624320 | 24 | 4160 | 1.3 |iter: 64/100
20.0 | 9.6816e-04 | 5.6818e-03 | 5529 | 5710848 | 24 | 4224 | 1.3 |iter: 65/100
20.0 | 9.5371e-04 | 5.5970e-03 | 5529 | 5797376 | 24 | 4288 | 1.3 |iter: 66/100
20.0 | 9.3968e-04 | 5.5147e-03 | 5529 | 5883904 | 24 | 4352 | 1.3 |iter: 67/100
20.0 | 9.5219e-04 | 5.6612e-03 | 5685 | 5970432 | 25 | 4416 | 1.3 |iter: 68/100
20.0 | 9.7772e-04 | 5.8036e-03 | 5922 | 6056960 | 26 | 4480 | 1.4 |iter: 69/100
20.0 | 9.9048e-04 | 5.9419e-03 | 6085 | 6143488 | 27 | 4544 | 1.4 |iter: 70/100
20.0 | 9.7672e-04 | 5.8594e-03 | 6085 | 6230016 | 27 | 4608 | 1.4 |iter: 71/100
20.0 | 9.6334e-04 | 5.7791e-03 | 6085 | 6316544 | 27 | 4672 | 1.4 |iter: 72/100
20.0 | 9.5033e-04 | 5.7010e-03 | 6085 | 6403072 | 27 | 4736 | 1.4 |iter: 73/100
20.0 | 9.3765e-04 | 5.6250e-03 | 6085 | 6489600 | 27 | 4800 | 1.4 |iter: 74/100
20.0 | 9.2532e-04 | 5.5510e-03 | 6085 | 6576128 | 27 | 4864 | 1.5 |iter: 75/100
20.0 | 9.1330e-04 | 5.4789e-03 | 6085 | 6662656 | 27 | 4928 | 1.5 |iter: 76/100
20.0 | 9.0159e-04 | 5.4087e-03 | 6085 | 6749184 | 27 | 4992 | 1.5 |iter: 77/100
20.0 | 9.3465e-04 | 5.5380e-03 | 6389 | 6835712 | 28 | 5056 | 1.5 |iter: 78/100
20.0 | 9.2297e-04 | 5.4687e-03 | 6389 | 6922240 | 28 | 5120 | 1.5 |iter: 79/100
20.0 | 9.1157e-04 | 5.4012e-03 | 6389 | 7008768 | 28 | 5184 | 1.6 |iter: 80/100
20.0 | 9.0046e-04 | 5.3354e-03 | 6389 | 7095296 | 28 | 5248 | 1.6 |iter: 81/100
20.0 | 8.8961e-04 | 5.2711e-03 | 6389 | 7181824 | 28 | 5312 | 1.6 |iter: 82/100
20.0 | 8.7902e-04 | 5.2083e-03 | 6389 | 7268352 | 28 | 5376 | 1.6 |iter: 83/100
20.0 | 9.0729e-04 | 5.3309e-03 | 6673 | 7354880 | 29 | 5440 | 1.6 |iter: 84/100
20.0 | 9.4203e-04 | 5.4506e-03 | 7010 | 7441408 | 30 | 5504 | 1.7 |iter: 85/100
20.0 | 9.3120e-04 | 5.3879e-03 | 7010 | 7527936 | 30 | 5568 | 1.7 |iter: 86/100
20.0 | 9.2062e-04 | 5.3267e-03 | 7010 | 7614464 | 30 | 5632 | 1.7 |iter: 87/100
20.0 | 9.1027e-04 | 5.2669e-03 | 7010 | 7700992 | 30 | 5696 | 1.7 |iter: 88/100
20.0 | 9.0016e-04 | 5.2083e-03 | 7010 | 7787520 | 30 | 5760 | 1.7 |iter: 89/100
20.0 | 9.0932e-04 | 5.3228e-03 | 7160 | 7874048 | 31 | 5824 | 1.8 |iter: 90/100
20.0 | 8.9943e-04 | 5.2649e-03 | 7160 | 7960576 | 31 | 5888 | 1.8 |iter: 91/100
20.0 | 9.1685e-04 | 5.3763e-03 | 7378 | 8047104 | 32 | 5952 | 1.8 |iter: 92/100
20.0 | 9.4005e-04 | 5.4854e-03 | 7646 | 8133632 | 33 | 6016 | 1.8 |iter: 93/100
20.0 | 9.7589e-04 | 5.7566e-03 | 8022 | 8220160 | 35 | 6080 | 1.8 |iter: 94/100
20.0 | 9.6573e-04 | 5.6966e-03 | 8022 | 8306688 | 35 | 6144 | 1.9 |iter: 95/100
20.0 | 9.6816e-04 | 5.7990e-03 | 8126 | 8393216 | 36 | 6208 | 1.9 |iter: 96/100
20.0 | 9.9708e-04 | 6.0587e-03 | 8455 | 8479744 | 38 | 6272 | 1.9 |iter: 97/100
20.0 | 9.8701e-04 | 5.9975e-03 | 8455 | 8566272 | 38 | 6336 | 1.9 |iter: 98/100
20.0 | 1.0107e-03 | 6.0938e-03 | 8745 | 8652800 | 39 | 6400 | 1.9 |iter: 99/100
20.0 | 1.0107e-03 | 6.0938e-03 | 8745 | 8652800 | 39 | 6400 | 1.9 |reached max iterations
22.5 | 1.9994e-03 | 1.5625e-02 | 173 | 86528 | 1 | 64 | 0.0 |iter: 0/100
22.5 | 9.9968e-04 | 7.8125e-03 | 173 | 173056 | 1 | 128 | 0.0 |iter: 1/100
22.5 | 1.3329e-03 | 1.0417e-02 | 346 | 259584 | 2 | 192 | 0.1 |iter: 2/100
22.5 | 9.9968e-04 | 7.8125e-03 | 346 | 346112 | 2 | 256 | 0.1 |iter: 3/100
22.5 | 7.9974e-04 | 6.2500e-03 | 346 | 432640 | 2 | 320 | 0.1 |iter: 4/100
22.5 | 6.6645e-04 | 5.2083e-03 | 346 | 519168 | 2 | 384 | 0.1 |iter: 5/100
22.5 | 5.7124e-04 | 4.4643e-03 | 346 | 605696 | 2 | 448 | 0.1 |iter: 6/100
22.5 | 4.9984e-04 | 3.9062e-03 | 346 | 692224 | 2 | 512 | 0.2 |iter: 7/100
22.5 | 4.4430e-04 | 3.4722e-03 | 346 | 778752 | 2 | 576 | 0.2 |iter: 8/100
22.5 | 3.9987e-04 | 3.1250e-03 | 346 | 865280 | 2 | 640 | 0.2 |iter: 9/100
22.5 | 3.6352e-04 | 2.8409e-03 | 346 | 951808 | 2 | 704 | 0.2 |iter: 10/100
22.5 | 5.7400e-04 | 3.9062e-03 | 596 | 1038336 | 3 | 768 | 0.2 |iter: 11/100
22.5 | 7.4498e-04 | 4.8077e-03 | 838 | 1124864 | 4 | 832 | 0.3 |iter: 12/100
22.5 | 6.9177e-04 | 4.4643e-03 | 838 | 1211392 | 4 | 896 | 0.3 |iter: 13/100
22.5 | 6.4565e-04 | 4.1667e-03 | 838 | 1297920 | 4 | 960 | 0.3 |iter: 14/100
22.5 | 6.0530e-04 | 3.9062e-03 | 838 | 1384448 | 4 | 1024 | 0.3 |iter: 15/100
22.5 | 5.6969e-04 | 3.6765e-03 | 838 | 1470976 | 4 | 1088 | 0.3 |iter: 16/100
22.5 | 5.3804e-04 | 3.4722e-03 | 838 | 1557504 | 4 | 1152 | 0.3 |iter: 17/100
22.5 | 5.0972e-04 | 3.2895e-03 | 838 | 1644032 | 4 | 1216 | 0.4 |iter: 18/100
22.5 | 4.8424e-04 | 3.1250e-03 | 838 | 1730560 | 4 | 1280 | 0.4 |iter: 19/100
22.5 | 4.6118e-04 | 2.9762e-03 | 838 | 1817088 | 4 | 1344 | 0.4 |iter: 20/100
22.5 | 4.4021e-04 | 2.8409e-03 | 838 | 1903616 | 4 | 1408 | 0.4 |iter: 21/100
22.5 | 4.2108e-04 | 2.7174e-03 | 838 | 1990144 | 4 | 1472 | 0.4 |iter: 22/100
22.5 | 4.0353e-04 | 2.6042e-03 | 838 | 2076672 | 4 | 1536 | 0.5 |iter: 23/100
22.5 | 5.1729e-04 | 3.1250e-03 | 1119 | 2163200 | 5 | 1600 | 0.5 |iter: 24/100
22.5 | 4.9739e-04 | 3.0048e-03 | 1119 | 2249728 | 5 | 1664 | 0.5 |iter: 25/100
22.5 | 4.7897e-04 | 2.8935e-03 | 1119 | 2336256 | 5 | 1728 | 0.5 |iter: 26/100
22.5 | 4.6187e-04 | 2.7902e-03 | 1119 | 2422784 | 5 | 1792 | 0.5 |iter: 27/100
22.5 | 4.4594e-04 | 2.6940e-03 | 1119 | 2509312 | 5 | 1856 | 0.6 |iter: 28/100
22.5 | 5.2160e-04 | 3.1250e-03 | 1354 | 2595840 | 6 | 1920 | 0.6 |iter: 29/100
22.5 | 5.8120e-04 | 3.5282e-03 | 1559 | 2682368 | 7 | 1984 | 0.6 |iter: 30/100
22.5 | 5.6304e-04 | 3.4180e-03 | 1559 | 2768896 | 7 | 2048 | 0.6 |iter: 31/100
22.5 | 5.4598e-04 | 3.3144e-03 | 1559 | 2855424 | 7 | 2112 | 0.6 |iter: 32/100
22.5 | 5.2992e-04 | 3.2169e-03 | 1559 | 2941952 | 7 | 2176 | 0.7 |iter: 33/100
22.5 | 5.1478e-04 | 3.1250e-03 | 1559 | 3028480 | 7 | 2240 | 0.7 |iter: 34/100
22.5 | 5.0048e-04 | 3.0382e-03 | 1559 | 3115008 | 7 | 2304 | 0.7 |iter: 35/100
22.5 | 4.8695e-04 | 2.9561e-03 | 1559 | 3201536 | 7 | 2368 | 0.7 |iter: 36/100
22.5 | 4.7414e-04 | 2.8783e-03 | 1559 | 3288064 | 7 | 2432 | 0.7 |iter: 37/100
22.5 | 4.6198e-04 | 2.8045e-03 | 1559 | 3374592 | 7 | 2496 | 0.8 |iter: 38/100
22.5 | 4.5043e-04 | 2.7344e-03 | 1559 | 3461120 | 7 | 2560 | 0.8 |iter: 39/100
22.5 | 4.3945e-04 | 2.6677e-03 | 1559 | 3547648 | 7 | 2624 | 0.8 |iter: 40/100
22.5 | 4.2898e-04 | 2.6042e-03 | 1559 | 3634176 | 7 | 2688 | 0.8 |iter: 41/100
22.5 | 4.6792e-04 | 2.9070e-03 | 1741 | 3720704 | 8 | 2752 | 0.8 |iter: 42/100
22.5 | 4.5729e-04 | 2.8409e-03 | 1741 | 3807232 | 8 | 2816 | 0.9 |iter: 43/100
22.5 | 4.4713e-04 | 2.7778e-03 | 1741 | 3893760 | 8 | 2880 | 0.9 |iter: 44/100
22.5 | 4.3741e-04 | 2.7174e-03 | 1741 | 3980288 | 8 | 2944 | 0.9 |iter: 45/100
22.5 | 4.2810e-04 | 2.6596e-03 | 1741 | 4066816 | 8 | 3008 | 0.9 |iter: 46/100
22.5 | 4.1918e-04 | 2.6042e-03 | 1741 | 4153344 | 8 | 3072 | 0.9 |iter: 47/100
22.5 | 4.1063e-04 | 2.5510e-03 | 1741 | 4239872 | 8 | 3136 | 0.9 |iter: 48/100
22.5 | 4.4356e-04 | 2.8125e-03 | 1919 | 4326400 | 9 | 3200 | 1.0 |iter: 49/100
22.5 | 4.3486e-04 | 2.7574e-03 | 1919 | 4412928 | 9 | 3264 | 1.0 |iter: 50/100
22.5 | 4.2650e-04 | 2.7043e-03 | 1919 | 4499456 | 9 | 3328 | 1.0 |iter: 51/100
22.5 | 4.1845e-04 | 2.6533e-03 | 1919 | 4585984 | 9 | 3392 | 1.0 |iter: 52/100
22.5 | 5.4639e-04 | 3.1829e-03 | 2553 | 4672512 | 11 | 3456 | 1.0 |iter: 53/100
22.5 | 5.3645e-04 | 3.1250e-03 | 2553 | 4759040 | 11 | 3520 | 1.1 |iter: 54/100
22.5 | 5.2687e-04 | 3.0692e-03 | 2553 | 4845568 | 11 | 3584 | 1.1 |iter: 55/100
22.5 | 5.1763e-04 | 3.0154e-03 | 2553 | 4932096 | 11 | 3648 | 1.1 |iter: 56/100
22.5 | 5.0871e-04 | 2.9634e-03 | 2553 | 5018624 | 11 | 3712 | 1.1 |iter: 57/100
22.5 | 5.3240e-04 | 3.1780e-03 | 2718 | 5105152 | 12 | 3776 | 1.1 |iter: 58/100
22.5 | 5.2353e-04 | 3.1250e-03 | 2718 | 5191680 | 12 | 3840 | 1.2 |iter: 59/100
22.5 | 5.1608e-04 | 3.3299e-03 | 2724 | 5278208 | 13 | 3904 | 1.2 |iter: 60/100
22.5 | 5.0776e-04 | 3.2762e-03 | 2724 | 5364736 | 13 | 3968 | 1.2 |iter: 61/100
22.5 | 4.9970e-04 | 3.2242e-03 | 2724 | 5451264 | 13 | 4032 | 1.2 |iter: 62/100
22.5 | 4.9189e-04 | 3.1738e-03 | 2724 | 5537792 | 13 | 4096 | 1.2 |iter: 63/100
22.5 | 4.8806e-04 | 3.3654e-03 | 2745 | 5624320 | 14 | 4160 | 1.3 |iter: 64/100
22.5 | 5.1779e-04 | 3.5511e-03 | 2957 | 5710848 | 15 | 4224 | 1.3 |iter: 65/100
22.5 | 5.1006e-04 | 3.4981e-03 | 2957 | 5797376 | 15 | 4288 | 1.3 |iter: 66/100
22.5 | 5.7224e-04 | 3.9062e-03 | 3367 | 5883904 | 17 | 4352 | 1.3 |iter: 67/100
22.5 | 5.6395e-04 | 3.8496e-03 | 3367 | 5970432 | 17 | 4416 | 1.3 |iter: 68/100
22.5 | 5.5589e-04 | 3.7946e-03 | 3367 | 6056960 | 17 | 4480 | 1.4 |iter: 69/100
22.5 | 5.4806e-04 | 3.7412e-03 | 3367 | 6143488 | 17 | 4544 | 1.4 |iter: 70/100
22.5 | 5.7961e-04 | 3.9062e-03 | 3611 | 6230016 | 18 | 4608 | 1.4 |iter: 71/100
22.5 | 5.7167e-04 | 3.8527e-03 | 3611 | 6316544 | 18 | 4672 | 1.4 |iter: 72/100
22.5 | 5.6395e-04 | 3.8007e-03 | 3611 | 6403072 | 18 | 4736 | 1.4 |iter: 73/100
22.5 | 5.5643e-04 | 3.7500e-03 | 3611 | 6489600 | 18 | 4800 | 1.5 |iter: 74/100
22.5 | 5.4911e-04 | 3.7007e-03 | 3611 | 6576128 | 18 | 4864 | 1.5 |iter: 75/100
22.5 | 5.4198e-04 | 3.6526e-03 | 3611 | 6662656 | 18 | 4928 | 1.5 |iter: 76/100
22.5 | 5.3503e-04 | 3.6058e-03 | 3611 | 6749184 | 18 | 4992 | 1.5 |iter: 77/100
22.5 | 5.2826e-04 | 3.5601e-03 | 3611 | 6835712 | 18 | 5056 | 1.5 |iter: 78/100
22.5 | 5.2165e-04 | 3.5156e-03 | 3611 | 6922240 | 18 | 5120 | 1.5 |iter: 79/100
22.5 | 5.1521e-04 | 3.4722e-03 | 3611 | 7008768 | 18 | 5184 | 1.6 |iter: 80/100
22.5 | 5.0893e-04 | 3.4299e-03 | 3611 | 7095296 | 18 | 5248 | 1.6 |iter: 81/100
22.5 | 5.0280e-04 | 3.3886e-03 | 3611 | 7181824 | 18 | 5312 | 1.6 |iter: 82/100
22.5 | 4.9681e-04 | 3.3482e-03 | 3611 | 7268352 | 18 | 5376 | 1.6 |iter: 83/100
22.5 | 4.9097e-04 | 3.3088e-03 | 3611 | 7354880 | 18 | 5440 | 1.6 |iter: 84/100
22.5 | 4.8526e-04 | 3.2703e-03 | 3611 | 7441408 | 18 | 5504 | 1.7 |iter: 85/100
22.5 | 4.7968e-04 | 3.2328e-03 | 3611 | 7527936 | 18 | 5568 | 1.7 |iter: 86/100
22.5 | 4.7423e-04 | 3.1960e-03 | 3611 | 7614464 | 18 | 5632 | 1.7 |iter: 87/100
22.5 | 4.6890e-04 | 3.1601e-03 | 3611 | 7700992 | 18 | 5696 | 1.7 |iter: 88/100
22.5 | 4.6369e-04 | 3.1250e-03 | 3611 | 7787520 | 18 | 5760 | 1.7 |iter: 89/100
22.5 | 4.7345e-04 | 3.2624e-03 | 3728 | 7874048 | 19 | 5824 | 1.8 |iter: 90/100
22.5 | 4.6831e-04 | 3.2269e-03 | 3728 | 7960576 | 19 | 5888 | 1.8 |iter: 91/100
22.5 | 4.6327e-04 | 3.1922e-03 | 3728 | 8047104 | 19 | 5952 | 1.8 |iter: 92/100
22.5 | 4.5834e-04 | 3.1582e-03 | 3728 | 8133632 | 19 | 6016 | 1.8 |iter: 93/100
22.5 | 4.5352e-04 | 3.1250e-03 | 3728 | 8220160 | 19 | 6080 | 1.8 |iter: 94/100
22.5 | 4.4879e-04 | 3.0924e-03 | 3728 | 8306688 | 19 | 6144 | 1.9 |iter: 95/100
22.5 | 4.4417e-04 | 3.0606e-03 | 3728 | 8393216 | 19 | 6208 | 1.9 |iter: 96/100
22.5 | 4.6546e-04 | 3.1888e-03 | 3947 | 8479744 | 20 | 6272 | 1.9 |iter: 97/100
22.5 | 4.6076e-04 | 3.1566e-03 | 3947 | 8566272 | 20 | 6336 | 1.9 |iter: 98/100
22.5 | 4.5615e-04 | 3.1250e-03 | 3947 | 8652800 | 20 | 6400 | 1.9 |iter: 99/100
22.5 | 4.5615e-04 | 3.1250e-03 | 3947 | 8652800 | 20 | 6400 | 1.9 |reached max iterations
25.0 | 4.1489e-03 | 1.5625e-02 | 359 | 86528 | 1 | 64 | 0.0 |iter: 0/100
25.0 | 2.0745e-03 | 7.8125e-03 | 359 | 173056 | 1 | 128 | 0.0 |iter: 1/100
25.0 | 1.3830e-03 | 5.2083e-03 | 359 | 259584 | 1 | 192 | 0.1 |iter: 2/100
25.0 | 1.0372e-03 | 3.9062e-03 | 359 | 346112 | 1 | 256 | 0.1 |iter: 3/100
25.0 | 8.2979e-04 | 3.1250e-03 | 359 | 432640 | 1 | 320 | 0.1 |iter: 4/100
25.0 | 6.9149e-04 | 2.6042e-03 | 359 | 519168 | 1 | 384 | 0.1 |iter: 5/100
25.0 | 5.9271e-04 | 2.2321e-03 | 359 | 605696 | 1 | 448 | 0.1 |iter: 6/100
25.0 | 5.1862e-04 | 1.9531e-03 | 359 | 692224 | 1 | 512 | 0.2 |iter: 7/100
25.0 | 4.6099e-04 | 1.7361e-03 | 359 | 778752 | 1 | 576 | 0.2 |iter: 8/100
25.0 | 4.1489e-04 | 1.5625e-03 | 359 | 865280 | 1 | 640 | 0.2 |iter: 9/100
25.0 | 3.7718e-04 | 1.4205e-03 | 359 | 951808 | 1 | 704 | 0.2 |iter: 10/100
25.0 | 3.4575e-04 | 1.3021e-03 | 359 | 1038336 | 1 | 768 | 0.2 |iter: 11/100
25.0 | 3.1915e-04 | 1.2019e-03 | 359 | 1124864 | 1 | 832 | 0.3 |iter: 12/100
25.0 | 2.9635e-04 | 1.1161e-03 | 359 | 1211392 | 1 | 896 | 0.3 |iter: 13/100
25.0 | 2.7660e-04 | 1.0417e-03 | 359 | 1297920 | 1 | 960 | 0.3 |iter: 14/100
25.0 | 2.5931e-04 | 9.7656e-04 | 359 | 1384448 | 1 | 1024 | 0.3 |iter: 15/100
25.0 | 2.4406e-04 | 9.1912e-04 | 359 | 1470976 | 1 | 1088 | 0.3 |iter: 16/100
25.0 | 2.3050e-04 | 8.6806e-04 | 359 | 1557504 | 1 | 1152 | 0.3 |iter: 17/100
25.0 | 2.1837e-04 | 8.2237e-04 | 359 | 1644032 | 1 | 1216 | 0.4 |iter: 18/100
25.0 | 2.0745e-04 | 7.8125e-04 | 359 | 1730560 | 1 | 1280 | 0.4 |iter: 19/100
25.0 | 1.9757e-04 | 7.4405e-04 | 359 | 1817088 | 1 | 1344 | 0.4 |iter: 20/100
25.0 | 1.8859e-04 | 7.1023e-04 | 359 | 1903616 | 1 | 1408 | 0.4 |iter: 21/100
25.0 | 1.8039e-04 | 6.7935e-04 | 359 | 1990144 | 1 | 1472 | 0.4 |iter: 22/100
25.0 | 1.7287e-04 | 6.5104e-04 | 359 | 2076672 | 1 | 1536 | 0.5 |iter: 23/100
25.0 | 1.6596e-04 | 6.2500e-04 | 359 | 2163200 | 1 | 1600 | 0.5 |iter: 24/100
25.0 | 1.5957e-04 | 6.0096e-04 | 359 | 2249728 | 1 | 1664 | 0.5 |iter: 25/100
25.0 | 1.5366e-04 | 5.7870e-04 | 359 | 2336256 | 1 | 1728 | 0.5 |iter: 26/100
25.0 | 1.4818e-04 | 5.5804e-04 | 359 | 2422784 | 1 | 1792 | 0.5 |iter: 27/100
25.0 | 1.4307e-04 | 5.3879e-04 | 359 | 2509312 | 1 | 1856 | 0.6 |iter: 28/100
25.0 | 1.3830e-04 | 5.2083e-04 | 359 | 2595840 | 1 | 1920 | 0.6 |iter: 29/100
25.0 | 1.3384e-04 | 5.0403e-04 | 359 | 2682368 | 1 | 1984 | 0.6 |iter: 30/100
25.0 | 1.2965e-04 | 4.8828e-04 | 359 | 2768896 | 1 | 2048 | 0.6 |iter: 31/100
25.0 | 1.2573e-04 | 4.7348e-04 | 359 | 2855424 | 1 | 2112 | 0.6 |iter: 32/100
25.0 | 1.2203e-04 | 4.5956e-04 | 359 | 2941952 | 1 | 2176 | 0.7 |iter: 33/100
25.0 | 2.3741e-04 | 8.9286e-04 | 719 | 3028480 | 2 | 2240 | 0.7 |iter: 34/100
25.0 | 2.3082e-04 | 8.6806e-04 | 719 | 3115008 | 2 | 2304 | 0.7 |iter: 35/100
25.0 | 2.2458e-04 | 8.4459e-04 | 719 | 3201536 | 2 | 2368 | 0.7 |iter: 36/100
25.0 | 2.1867e-04 | 8.2237e-04 | 719 | 3288064 | 2 | 2432 | 0.7 |iter: 37/100
25.0 | 2.1306e-04 | 8.0128e-04 | 719 | 3374592 | 2 | 2496 | 0.8 |iter: 38/100
25.0 | 2.0774e-04 | 7.8125e-04 | 719 | 3461120 | 2 | 2560 | 0.8 |iter: 39/100
25.0 | 2.0267e-04 | 7.6220e-04 | 719 | 3547648 | 2 | 2624 | 0.8 |iter: 40/100
25.0 | 1.9784e-04 | 7.4405e-04 | 719 | 3634176 | 2 | 2688 | 0.8 |iter: 41/100
25.0 | 1.9324e-04 | 7.2674e-04 | 719 | 3720704 | 2 | 2752 | 0.8 |iter: 42/100
25.0 | 1.8885e-04 | 7.1023e-04 | 719 | 3807232 | 2 | 2816 | 0.9 |iter: 43/100
25.0 | 1.8465e-04 | 6.9444e-04 | 719 | 3893760 | 2 | 2880 | 0.9 |iter: 44/100
25.0 | 1.8064e-04 | 6.7935e-04 | 719 | 3980288 | 2 | 2944 | 0.9 |iter: 45/100
25.0 | 1.7680e-04 | 6.6489e-04 | 719 | 4066816 | 2 | 3008 | 0.9 |iter: 46/100
25.0 | 1.7311e-04 | 6.5104e-04 | 719 | 4153344 | 2 | 3072 | 0.9 |iter: 47/100
25.0 | 1.6958e-04 | 6.3776e-04 | 719 | 4239872 | 2 | 3136 | 1.0 |iter: 48/100
25.0 | 1.6619e-04 | 6.2500e-04 | 719 | 4326400 | 2 | 3200 | 1.0 |iter: 49/100
25.0 | 1.6293e-04 | 6.1275e-04 | 719 | 4412928 | 2 | 3264 | 1.0 |iter: 50/100
25.0 | 1.5980e-04 | 6.0096e-04 | 719 | 4499456 | 2 | 3328 | 1.0 |iter: 51/100
25.0 | 2.0061e-04 | 8.8443e-04 | 920 | 4585984 | 3 | 3392 | 1.0 |iter: 52/100
25.0 | 1.9690e-04 | 8.6806e-04 | 920 | 4672512 | 3 | 3456 | 1.0 |iter: 53/100
25.0 | 1.9962e-04 | 1.1364e-03 | 950 | 4759040 | 4 | 3520 | 1.1 |iter: 54/100
25.0 | 1.9606e-04 | 1.1161e-03 | 950 | 4845568 | 4 | 3584 | 1.1 |iter: 55/100
25.0 | 2.4959e-04 | 1.3706e-03 | 1231 | 4932096 | 5 | 3648 | 1.1 |iter: 56/100
25.0 | 2.4529e-04 | 1.3470e-03 | 1231 | 5018624 | 5 | 3712 | 1.1 |iter: 57/100
25.0 | 2.4113e-04 | 1.3242e-03 | 1231 | 5105152 | 5 | 3776 | 1.1 |iter: 58/100
25.0 | 2.3711e-04 | 1.3021e-03 | 1231 | 5191680 | 5 | 3840 | 1.2 |iter: 59/100
25.0 | 2.3322e-04 | 1.2807e-03 | 1231 | 5278208 | 5 | 3904 | 1.2 |iter: 60/100
25.0 | 2.2946e-04 | 1.2601e-03 | 1231 | 5364736 | 5 | 3968 | 1.2 |iter: 61/100
25.0 | 2.2582e-04 | 1.2401e-03 | 1231 | 5451264 | 5 | 4032 | 1.2 |iter: 62/100
25.0 | 2.2229e-04 | 1.2207e-03 | 1231 | 5537792 | 5 | 4096 | 1.2 |iter: 63/100
25.0 | 2.1887e-04 | 1.2019e-03 | 1231 | 5624320 | 5 | 4160 | 1.3 |iter: 64/100
25.0 | 2.1555e-04 | 1.1837e-03 | 1231 | 5710848 | 5 | 4224 | 1.3 |iter: 65/100
25.0 | 2.1234e-04 | 1.1660e-03 | 1231 | 5797376 | 5 | 4288 | 1.3 |iter: 66/100
25.0 | 2.0921e-04 | 1.1489e-03 | 1231 | 5883904 | 5 | 4352 | 1.3 |iter: 67/100
25.0 | 2.0618e-04 | 1.1322e-03 | 1231 | 5970432 | 5 | 4416 | 1.3 |iter: 68/100
25.0 | 2.0324e-04 | 1.1161e-03 | 1231 | 6056960 | 5 | 4480 | 1.4 |iter: 69/100
25.0 | 2.0037e-04 | 1.1004e-03 | 1231 | 6143488 | 5 | 4544 | 1.4 |iter: 70/100
25.0 | 1.9759e-04 | 1.0851e-03 | 1231 | 6230016 | 5 | 4608 | 1.4 |iter: 71/100
25.0 | 1.9489e-04 | 1.0702e-03 | 1231 | 6316544 | 5 | 4672 | 1.4 |iter: 72/100
25.0 | 1.9225e-04 | 1.0557e-03 | 1231 | 6403072 | 5 | 4736 | 1.4 |iter: 73/100
25.0 | 1.8969e-04 | 1.0417e-03 | 1231 | 6489600 | 5 | 4800 | 1.5 |iter: 74/100
25.0 | 1.8719e-04 | 1.0280e-03 | 1231 | 6576128 | 5 | 4864 | 1.5 |iter: 75/100
25.0 | 1.8476e-04 | 1.0146e-03 | 1231 | 6662656 | 5 | 4928 | 1.5 |iter: 76/100
25.0 | 1.8239e-04 | 1.0016e-03 | 1231 | 6749184 | 5 | 4992 | 1.5 |iter: 77/100
25.0 | 1.8008e-04 | 9.8892e-04 | 1231 | 6835712 | 5 | 5056 | 1.5 |iter: 78/100
25.0 | 1.7783e-04 | 9.7656e-04 | 1231 | 6922240 | 5 | 5120 | 1.6 |iter: 79/100
25.0 | 1.7564e-04 | 9.6451e-04 | 1231 | 7008768 | 5 | 5184 | 1.6 |iter: 80/100
25.0 | 1.7350e-04 | 9.5274e-04 | 1231 | 7095296 | 5 | 5248 | 1.6 |iter: 81/100
25.0 | 1.7140e-04 | 9.4127e-04 | 1231 | 7181824 | 5 | 5312 | 1.6 |iter: 82/100
25.0 | 1.6936e-04 | 9.3006e-04 | 1231 | 7268352 | 5 | 5376 | 1.6 |iter: 83/100
25.0 | 1.6737e-04 | 9.1912e-04 | 1231 | 7354880 | 5 | 5440 | 1.6 |iter: 84/100
25.0 | 1.6543e-04 | 9.0843e-04 | 1231 | 7441408 | 5 | 5504 | 1.7 |iter: 85/100
25.0 | 1.6352e-04 | 8.9799e-04 | 1231 | 7527936 | 5 | 5568 | 1.7 |iter: 86/100
25.0 | 1.6167e-04 | 8.8778e-04 | 1231 | 7614464 | 5 | 5632 | 1.7 |iter: 87/100
25.0 | 1.5985e-04 | 8.7781e-04 | 1231 | 7700992 | 5 | 5696 | 1.7 |iter: 88/100
25.0 | 1.5807e-04 | 8.6806e-04 | 1231 | 7787520 | 5 | 5760 | 1.7 |iter: 89/100
25.0 | 1.5634e-04 | 8.5852e-04 | 1231 | 7874048 | 5 | 5824 | 1.8 |iter: 90/100
25.0 | 1.5464e-04 | 8.4918e-04 | 1231 | 7960576 | 5 | 5888 | 1.8 |iter: 91/100
25.0 | 1.5297e-04 | 8.4005e-04 | 1231 | 8047104 | 5 | 5952 | 1.8 |iter: 92/100
25.0 | 1.5135e-04 | 8.3112e-04 | 1231 | 8133632 | 5 | 6016 | 1.8 |iter: 93/100
25.0 | 1.4975e-04 | 8.2237e-04 | 1231 | 8220160 | 5 | 6080 | 1.8 |iter: 94/100
25.0 | 1.4819e-04 | 8.1380e-04 | 1231 | 8306688 | 5 | 6144 | 1.9 |iter: 95/100
25.0 | 1.8169e-04 | 9.6649e-04 | 1525 | 8393216 | 6 | 6208 | 1.9 |iter: 96/100
25.0 | 1.7984e-04 | 9.5663e-04 | 1525 | 8479744 | 6 | 6272 | 1.9 |iter: 97/100
25.0 | 1.7802e-04 | 9.4697e-04 | 1525 | 8566272 | 6 | 6336 | 1.9 |iter: 98/100
25.0 | 1.7624e-04 | 9.3750e-04 | 1525 | 8652800 | 6 | 6400 | 1.9 |iter: 99/100
25.0 | 1.7624e-04 | 9.3750e-04 | 1525 | 8652800 | 6 | 6400 | 1.9 |reached max iterations
Simulating Rayleigh fading BLER ...
EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status
---------------------------------------------------------------------------------------------------------------------------------------
-5.0 | 1.6479e-01 | 9.5312e-01 | 14259 | 86528 | 61 | 64 | 0.0 |iter: 0/100
-5.0 | 1.6463e-01 | 9.4531e-01 | 28491 | 173056 | 121 | 128 | 0.0 |iter: 1/100
-5.0 | 1.6463e-01 | 9.4531e-01 | 28491 | 173056 | 121 | 128 | 0.0 |reached target block errors
-2.5 | 6.0582e-02 | 5.0000e-01 | 5242 | 86528 | 32 | 64 | 0.0 |iter: 0/100
-2.5 | 6.3575e-02 | 5.0000e-01 | 11002 | 173056 | 64 | 128 | 0.0 |iter: 1/100
-2.5 | 6.7489e-02 | 5.2604e-01 | 17519 | 259584 | 101 | 192 | 0.1 |iter: 2/100
-2.5 | 6.7489e-02 | 5.2604e-01 | 17519 | 259584 | 101 | 192 | 0.1 |reached target block errors
0.0 | 3.9525e-03 | 7.8125e-02 | 342 | 86528 | 5 | 64 | 0.0 |iter: 0/100
0.0 | 5.6629e-03 | 8.5938e-02 | 980 | 173056 | 11 | 128 | 0.0 |iter: 1/100
0.0 | 7.8125e-03 | 8.8542e-02 | 2028 | 259584 | 17 | 192 | 0.1 |iter: 2/100
0.0 | 5.8709e-03 | 7.4219e-02 | 2032 | 346112 | 19 | 256 | 0.1 |iter: 3/100
0.0 | 5.6537e-03 | 6.8750e-02 | 2446 | 432640 | 22 | 320 | 0.1 |iter: 4/100
0.0 | 5.8151e-03 | 6.7708e-02 | 3019 | 519168 | 26 | 384 | 0.1 |iter: 5/100
0.0 | 6.1764e-03 | 6.9196e-02 | 3741 | 605696 | 31 | 448 | 0.1 |iter: 6/100
0.0 | 6.2812e-03 | 7.2266e-02 | 4348 | 692224 | 37 | 512 | 0.1 |iter: 7/100
0.0 | 6.7647e-03 | 7.2917e-02 | 5268 | 778752 | 42 | 576 | 0.2 |iter: 8/100
0.0 | 6.4338e-03 | 7.0312e-02 | 5567 | 865280 | 45 | 640 | 0.2 |iter: 9/100
0.0 | 6.4068e-03 | 6.8182e-02 | 6098 | 951808 | 48 | 704 | 0.2 |iter: 10/100
0.0 | 7.0921e-03 | 7.2917e-02 | 7364 | 1038336 | 56 | 768 | 0.2 |iter: 11/100
0.0 | 7.3724e-03 | 7.3317e-02 | 8293 | 1124864 | 61 | 832 | 0.2 |iter: 12/100
0.0 | 7.4856e-03 | 7.3661e-02 | 9068 | 1211392 | 66 | 896 | 0.2 |iter: 13/100
0.0 | 7.3394e-03 | 7.1875e-02 | 9526 | 1297920 | 69 | 960 | 0.3 |iter: 14/100
0.0 | 7.3907e-03 | 7.1289e-02 | 10232 | 1384448 | 73 | 1024 | 0.3 |iter: 15/100
0.0 | 7.4624e-03 | 7.1691e-02 | 10977 | 1470976 | 78 | 1088 | 0.3 |iter: 16/100
0.0 | 7.6128e-03 | 7.2917e-02 | 11857 | 1557504 | 84 | 1152 | 0.3 |iter: 17/100
0.0 | 7.4767e-03 | 7.0724e-02 | 12292 | 1644032 | 86 | 1216 | 0.3 |iter: 18/100
0.0 | 7.4381e-03 | 6.9531e-02 | 12872 | 1730560 | 89 | 1280 | 0.4 |iter: 19/100
0.0 | 7.6964e-03 | 7.1429e-02 | 13985 | 1817088 | 96 | 1344 | 0.4 |iter: 20/100
0.0 | 7.7400e-03 | 7.1733e-02 | 14734 | 1903616 | 101 | 1408 | 0.4 |iter: 21/100
0.0 | 7.7400e-03 | 7.1733e-02 | 14734 | 1903616 | 101 | 1408 | 0.4 |reached target block errors
2.5 | 0.0000e+00 | 0.0000e+00 | 0 | 86528 | 0 | 64 | 0.0 |iter: 0/100
2.5 | 3.1204e-04 | 7.8125e-03 | 54 | 173056 | 1 | 128 | 0.0 |iter: 1/100
2.5 | 2.0803e-04 | 5.2083e-03 | 54 | 259584 | 1 | 192 | 0.1 |iter: 2/100
2.5 | 6.3852e-04 | 7.8125e-03 | 221 | 346112 | 2 | 256 | 0.1 |iter: 3/100
2.5 | 5.1082e-04 | 6.2500e-03 | 221 | 432640 | 2 | 320 | 0.1 |iter: 4/100
2.5 | 4.2568e-04 | 5.2083e-03 | 221 | 519168 | 2 | 384 | 0.1 |iter: 5/100
2.5 | 3.6487e-04 | 4.4643e-03 | 221 | 605696 | 2 | 448 | 0.1 |iter: 6/100
2.5 | 5.5329e-04 | 5.8594e-03 | 383 | 692224 | 3 | 512 | 0.1 |iter: 7/100
2.5 | 4.9181e-04 | 5.2083e-03 | 383 | 778752 | 3 | 576 | 0.2 |iter: 8/100
2.5 | 4.4263e-04 | 4.6875e-03 | 383 | 865280 | 3 | 640 | 0.2 |iter: 9/100
2.5 | 4.0239e-04 | 4.2614e-03 | 383 | 951808 | 3 | 704 | 0.2 |iter: 10/100
2.5 | 3.6886e-04 | 3.9062e-03 | 383 | 1038336 | 3 | 768 | 0.2 |iter: 11/100
2.5 | 3.4049e-04 | 3.6058e-03 | 383 | 1124864 | 3 | 832 | 0.2 |iter: 12/100
2.5 | 3.1617e-04 | 3.3482e-03 | 383 | 1211392 | 3 | 896 | 0.2 |iter: 13/100
2.5 | 2.9509e-04 | 3.1250e-03 | 383 | 1297920 | 3 | 960 | 0.3 |iter: 14/100
2.5 | 2.7664e-04 | 2.9297e-03 | 383 | 1384448 | 3 | 1024 | 0.3 |iter: 15/100
2.5 | 2.6037e-04 | 2.7574e-03 | 383 | 1470976 | 3 | 1088 | 0.3 |iter: 16/100
2.5 | 2.4591e-04 | 2.6042e-03 | 383 | 1557504 | 3 | 1152 | 0.3 |iter: 17/100
2.5 | 2.3296e-04 | 2.4671e-03 | 383 | 1644032 | 3 | 1216 | 0.3 |iter: 18/100
2.5 | 2.2132e-04 | 2.3437e-03 | 383 | 1730560 | 3 | 1280 | 0.4 |iter: 19/100
2.5 | 3.4010e-04 | 2.9762e-03 | 618 | 1817088 | 4 | 1344 | 0.4 |iter: 20/100
2.5 | 3.2465e-04 | 2.8409e-03 | 618 | 1903616 | 4 | 1408 | 0.4 |iter: 21/100
2.5 | 3.1053e-04 | 2.7174e-03 | 618 | 1990144 | 4 | 1472 | 0.4 |iter: 22/100
2.5 | 2.9759e-04 | 2.6042e-03 | 618 | 2076672 | 4 | 1536 | 0.4 |iter: 23/100
2.5 | 2.8569e-04 | 2.5000e-03 | 618 | 2163200 | 4 | 1600 | 0.4 |iter: 24/100
2.5 | 2.7470e-04 | 2.4038e-03 | 618 | 2249728 | 4 | 1664 | 0.5 |iter: 25/100
2.5 | 2.6453e-04 | 2.3148e-03 | 618 | 2336256 | 4 | 1728 | 0.5 |iter: 26/100
2.5 | 2.5508e-04 | 2.2321e-03 | 618 | 2422784 | 4 | 1792 | 0.5 |iter: 27/100
2.5 | 2.4628e-04 | 2.1552e-03 | 618 | 2509312 | 4 | 1856 | 0.5 |iter: 28/100
2.5 | 2.7852e-04 | 2.6042e-03 | 723 | 2595840 | 5 | 1920 | 0.5 |iter: 29/100
2.5 | 2.6954e-04 | 2.5202e-03 | 723 | 2682368 | 5 | 1984 | 0.6 |iter: 30/100
2.5 | 3.4454e-04 | 2.9297e-03 | 954 | 2768896 | 6 | 2048 | 0.6 |iter: 31/100
2.5 | 3.3410e-04 | 2.8409e-03 | 954 | 2855424 | 6 | 2112 | 0.6 |iter: 32/100
2.5 | 4.0245e-04 | 3.2169e-03 | 1184 | 2941952 | 7 | 2176 | 0.6 |iter: 33/100
2.5 | 3.9096e-04 | 3.1250e-03 | 1184 | 3028480 | 7 | 2240 | 0.6 |iter: 34/100
2.5 | 3.8010e-04 | 3.0382e-03 | 1184 | 3115008 | 7 | 2304 | 0.6 |iter: 35/100
2.5 | 3.6982e-04 | 2.9561e-03 | 1184 | 3201536 | 7 | 2368 | 0.7 |iter: 36/100
2.5 | 3.6009e-04 | 2.8783e-03 | 1184 | 3288064 | 7 | 2432 | 0.7 |iter: 37/100
2.5 | 3.5086e-04 | 2.8045e-03 | 1184 | 3374592 | 7 | 2496 | 0.7 |iter: 38/100
2.5 | 3.6838e-04 | 3.1250e-03 | 1275 | 3461120 | 8 | 2560 | 0.7 |iter: 39/100
2.5 | 3.5939e-04 | 3.0488e-03 | 1275 | 3547648 | 8 | 2624 | 0.7 |iter: 40/100
2.5 | 3.5084e-04 | 2.9762e-03 | 1275 | 3634176 | 8 | 2688 | 0.7 |iter: 41/100
2.5 | 3.4725e-04 | 3.2703e-03 | 1292 | 3720704 | 9 | 2752 | 0.8 |iter: 42/100
2.5 | 3.3935e-04 | 3.1960e-03 | 1292 | 3807232 | 9 | 2816 | 0.8 |iter: 43/100
2.5 | 3.3181e-04 | 3.1250e-03 | 1292 | 3893760 | 9 | 2880 | 0.8 |iter: 44/100
2.5 | 3.3716e-04 | 3.3967e-03 | 1342 | 3980288 | 10 | 2944 | 0.8 |iter: 45/100
2.5 | 3.2999e-04 | 3.3245e-03 | 1342 | 4066816 | 10 | 3008 | 0.8 |iter: 46/100
2.5 | 3.2311e-04 | 3.2552e-03 | 1342 | 4153344 | 10 | 3072 | 0.9 |iter: 47/100
2.5 | 3.6605e-04 | 3.5077e-03 | 1552 | 4239872 | 11 | 3136 | 0.9 |iter: 48/100
2.5 | 3.5873e-04 | 3.4375e-03 | 1552 | 4326400 | 11 | 3200 | 0.9 |iter: 49/100
2.5 | 3.5169e-04 | 3.3701e-03 | 1552 | 4412928 | 11 | 3264 | 0.9 |iter: 50/100
2.5 | 3.7316e-04 | 3.6058e-03 | 1679 | 4499456 | 12 | 3328 | 0.9 |iter: 51/100
2.5 | 3.6612e-04 | 3.5377e-03 | 1679 | 4585984 | 12 | 3392 | 0.9 |iter: 52/100
2.5 | 3.5934e-04 | 3.4722e-03 | 1679 | 4672512 | 12 | 3456 | 1.0 |iter: 53/100
2.5 | 3.5280e-04 | 3.4091e-03 | 1679 | 4759040 | 12 | 3520 | 1.0 |iter: 54/100
2.5 | 3.4650e-04 | 3.3482e-03 | 1679 | 4845568 | 12 | 3584 | 1.0 |iter: 55/100
2.5 | 3.4042e-04 | 3.2895e-03 | 1679 | 4932096 | 12 | 3648 | 1.0 |iter: 56/100
2.5 | 3.3455e-04 | 3.2328e-03 | 1679 | 5018624 | 12 | 3712 | 1.0 |iter: 57/100
2.5 | 3.2888e-04 | 3.1780e-03 | 1679 | 5105152 | 12 | 3776 | 1.1 |iter: 58/100
2.5 | 3.2340e-04 | 3.1250e-03 | 1679 | 5191680 | 12 | 3840 | 1.1 |iter: 59/100
2.5 | 3.1810e-04 | 3.0738e-03 | 1679 | 5278208 | 12 | 3904 | 1.1 |iter: 60/100
2.5 | 3.1297e-04 | 3.0242e-03 | 1679 | 5364736 | 12 | 3968 | 1.1 |iter: 61/100
2.5 | 3.0800e-04 | 2.9762e-03 | 1679 | 5451264 | 12 | 4032 | 1.1 |iter: 62/100
2.5 | 3.0319e-04 | 2.9297e-03 | 1679 | 5537792 | 12 | 4096 | 1.1 |iter: 63/100
2.5 | 2.9852e-04 | 2.8846e-03 | 1679 | 5624320 | 12 | 4160 | 1.2 |iter: 64/100
2.5 | 2.9400e-04 | 2.8409e-03 | 1679 | 5710848 | 12 | 4224 | 1.2 |iter: 65/100
2.5 | 2.8961e-04 | 2.7985e-03 | 1679 | 5797376 | 12 | 4288 | 1.2 |iter: 66/100
2.5 | 2.8535e-04 | 2.7574e-03 | 1679 | 5883904 | 12 | 4352 | 1.2 |iter: 67/100
2.5 | 2.8122e-04 | 2.7174e-03 | 1679 | 5970432 | 12 | 4416 | 1.2 |iter: 68/100
2.5 | 2.7720e-04 | 2.6786e-03 | 1679 | 6056960 | 12 | 4480 | 1.2 |iter: 69/100
2.5 | 2.7330e-04 | 2.6408e-03 | 1679 | 6143488 | 12 | 4544 | 1.3 |iter: 70/100
2.5 | 2.9743e-04 | 2.8212e-03 | 1853 | 6230016 | 13 | 4608 | 1.3 |iter: 71/100
2.5 | 2.9336e-04 | 2.7825e-03 | 1853 | 6316544 | 13 | 4672 | 1.3 |iter: 72/100
2.5 | 2.8939e-04 | 2.7449e-03 | 1853 | 6403072 | 13 | 4736 | 1.3 |iter: 73/100
2.5 | 2.8553e-04 | 2.7083e-03 | 1853 | 6489600 | 13 | 4800 | 1.3 |iter: 74/100
2.5 | 2.8178e-04 | 2.6727e-03 | 1853 | 6576128 | 13 | 4864 | 1.4 |iter: 75/100
2.5 | 2.7812e-04 | 2.6380e-03 | 1853 | 6662656 | 13 | 4928 | 1.4 |iter: 76/100
2.5 | 2.7455e-04 | 2.6042e-03 | 1853 | 6749184 | 13 | 4992 | 1.4 |iter: 77/100
2.5 | 2.7108e-04 | 2.5712e-03 | 1853 | 6835712 | 13 | 5056 | 1.4 |iter: 78/100
2.5 | 2.6769e-04 | 2.5391e-03 | 1853 | 6922240 | 13 | 5120 | 1.4 |iter: 79/100
2.5 | 2.6438e-04 | 2.5077e-03 | 1853 | 7008768 | 13 | 5184 | 1.4 |iter: 80/100
2.5 | 2.6116e-04 | 2.4771e-03 | 1853 | 7095296 | 13 | 5248 | 1.5 |iter: 81/100
2.5 | 2.5801e-04 | 2.4473e-03 | 1853 | 7181824 | 13 | 5312 | 1.5 |iter: 82/100
2.5 | 2.5494e-04 | 2.4182e-03 | 1853 | 7268352 | 13 | 5376 | 1.5 |iter: 83/100
2.5 | 2.5194e-04 | 2.3897e-03 | 1853 | 7354880 | 13 | 5440 | 1.5 |iter: 84/100
2.5 | 2.4901e-04 | 2.3619e-03 | 1853 | 7441408 | 13 | 5504 | 1.5 |iter: 85/100
2.5 | 2.7312e-04 | 2.5144e-03 | 2056 | 7527936 | 14 | 5568 | 1.6 |iter: 86/100
2.5 | 2.7001e-04 | 2.4858e-03 | 2056 | 7614464 | 14 | 5632 | 1.6 |iter: 87/100
2.5 | 2.6698e-04 | 2.4579e-03 | 2056 | 7700992 | 14 | 5696 | 1.6 |iter: 88/100
2.5 | 2.6401e-04 | 2.4306e-03 | 2056 | 7787520 | 14 | 5760 | 1.6 |iter: 89/100
2.5 | 2.6111e-04 | 2.4038e-03 | 2056 | 7874048 | 14 | 5824 | 1.6 |iter: 90/100
2.5 | 2.5827e-04 | 2.3777e-03 | 2056 | 7960576 | 14 | 5888 | 1.6 |iter: 91/100
2.5 | 2.5550e-04 | 2.3522e-03 | 2056 | 8047104 | 14 | 5952 | 1.7 |iter: 92/100
2.5 | 2.5278e-04 | 2.3271e-03 | 2056 | 8133632 | 14 | 6016 | 1.7 |iter: 93/100
2.5 | 2.5012e-04 | 2.3026e-03 | 2056 | 8220160 | 14 | 6080 | 1.7 |iter: 94/100
2.5 | 2.4751e-04 | 2.2786e-03 | 2056 | 8306688 | 14 | 6144 | 1.7 |iter: 95/100
2.5 | 2.4496e-04 | 2.2552e-03 | 2056 | 8393216 | 14 | 6208 | 1.7 |iter: 96/100
2.5 | 2.4246e-04 | 2.2321e-03 | 2056 | 8479744 | 14 | 6272 | 1.8 |iter: 97/100
2.5 | 2.4001e-04 | 2.2096e-03 | 2056 | 8566272 | 14 | 6336 | 1.8 |iter: 98/100
2.5 | 2.3761e-04 | 2.1875e-03 | 2056 | 8652800 | 14 | 6400 | 1.8 |iter: 99/100
2.5 | 2.3761e-04 | 2.1875e-03 | 2056 | 8652800 | 14 | 6400 | 1.8 |reached max iterations
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 86528 | 0 | 64 | 0.0 |iter: 0/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 173056 | 0 | 128 | 0.0 |iter: 1/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 259584 | 0 | 192 | 0.1 |iter: 2/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 346112 | 0 | 256 | 0.1 |iter: 3/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 432640 | 0 | 320 | 0.1 |iter: 4/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 519168 | 0 | 384 | 0.1 |iter: 5/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 605696 | 0 | 448 | 0.1 |iter: 6/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 692224 | 0 | 512 | 0.1 |iter: 7/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 778752 | 0 | 576 | 0.2 |iter: 8/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 865280 | 0 | 640 | 0.2 |iter: 9/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 951808 | 0 | 704 | 0.2 |iter: 10/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 1038336 | 0 | 768 | 0.2 |iter: 11/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 1124864 | 0 | 832 | 0.2 |iter: 12/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 1211392 | 0 | 896 | 0.2 |iter: 13/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 1297920 | 0 | 960 | 0.3 |iter: 14/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 1384448 | 0 | 1024 | 0.3 |iter: 15/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 1470976 | 0 | 1088 | 0.3 |iter: 16/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 1557504 | 0 | 1152 | 0.3 |iter: 17/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 1644032 | 0 | 1216 | 0.3 |iter: 18/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 1730560 | 0 | 1280 | 0.4 |iter: 19/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 1817088 | 0 | 1344 | 0.4 |iter: 20/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 1903616 | 0 | 1408 | 0.4 |iter: 21/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 1990144 | 0 | 1472 | 0.4 |iter: 22/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 2076672 | 0 | 1536 | 0.4 |iter: 23/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 2163200 | 0 | 1600 | 0.4 |iter: 24/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 2249728 | 0 | 1664 | 0.5 |iter: 25/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 2336256 | 0 | 1728 | 0.5 |iter: 26/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 2422784 | 0 | 1792 | 0.5 |iter: 27/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 2509312 | 0 | 1856 | 0.5 |iter: 28/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 2595840 | 0 | 1920 | 0.5 |iter: 29/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 2682368 | 0 | 1984 | 0.6 |iter: 30/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 2768896 | 0 | 2048 | 0.6 |iter: 31/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 2855424 | 0 | 2112 | 0.6 |iter: 32/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 2941952 | 0 | 2176 | 0.6 |iter: 33/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 3028480 | 0 | 2240 | 0.6 |iter: 34/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 3115008 | 0 | 2304 | 0.6 |iter: 35/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 3201536 | 0 | 2368 | 0.7 |iter: 36/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 3288064 | 0 | 2432 | 0.7 |iter: 37/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 3374592 | 0 | 2496 | 0.7 |iter: 38/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 3461120 | 0 | 2560 | 0.7 |iter: 39/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 3547648 | 0 | 2624 | 0.7 |iter: 40/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 3634176 | 0 | 2688 | 0.7 |iter: 41/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 3720704 | 0 | 2752 | 0.8 |iter: 42/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 3807232 | 0 | 2816 | 0.8 |iter: 43/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 3893760 | 0 | 2880 | 0.8 |iter: 44/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 3980288 | 0 | 2944 | 0.8 |iter: 45/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 4066816 | 0 | 3008 | 0.8 |iter: 46/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 4153344 | 0 | 3072 | 0.9 |iter: 47/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 4239872 | 0 | 3136 | 0.9 |iter: 48/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 4326400 | 0 | 3200 | 0.9 |iter: 49/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 4412928 | 0 | 3264 | 0.9 |iter: 50/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 4499456 | 0 | 3328 | 0.9 |iter: 51/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 4585984 | 0 | 3392 | 0.9 |iter: 52/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 4672512 | 0 | 3456 | 1.0 |iter: 53/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 4759040 | 0 | 3520 | 1.0 |iter: 54/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 4845568 | 0 | 3584 | 1.0 |iter: 55/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 4932096 | 0 | 3648 | 1.0 |iter: 56/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 5018624 | 0 | 3712 | 1.0 |iter: 57/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 5105152 | 0 | 3776 | 1.1 |iter: 58/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 5191680 | 0 | 3840 | 1.1 |iter: 59/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 5278208 | 0 | 3904 | 1.1 |iter: 60/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 5364736 | 0 | 3968 | 1.1 |iter: 61/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 5451264 | 0 | 4032 | 1.1 |iter: 62/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 5537792 | 0 | 4096 | 1.1 |iter: 63/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 5624320 | 0 | 4160 | 1.2 |iter: 64/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 5710848 | 0 | 4224 | 1.2 |iter: 65/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 5797376 | 0 | 4288 | 1.2 |iter: 66/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 5883904 | 0 | 4352 | 1.2 |iter: 67/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 5970432 | 0 | 4416 | 1.2 |iter: 68/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 6056960 | 0 | 4480 | 1.2 |iter: 69/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 6143488 | 0 | 4544 | 1.3 |iter: 70/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 6230016 | 0 | 4608 | 1.3 |iter: 71/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 6316544 | 0 | 4672 | 1.3 |iter: 72/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 6403072 | 0 | 4736 | 1.3 |iter: 73/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 6489600 | 0 | 4800 | 1.3 |iter: 74/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 6576128 | 0 | 4864 | 1.4 |iter: 75/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 6662656 | 0 | 4928 | 1.4 |iter: 76/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 6749184 | 0 | 4992 | 1.4 |iter: 77/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 6835712 | 0 | 5056 | 1.4 |iter: 78/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 6922240 | 0 | 5120 | 1.4 |iter: 79/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 7008768 | 0 | 5184 | 1.4 |iter: 80/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 7095296 | 0 | 5248 | 1.5 |iter: 81/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 7181824 | 0 | 5312 | 1.5 |iter: 82/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 7268352 | 0 | 5376 | 1.5 |iter: 83/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 7354880 | 0 | 5440 | 1.5 |iter: 84/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 7441408 | 0 | 5504 | 1.5 |iter: 85/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 7527936 | 0 | 5568 | 1.6 |iter: 86/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 7614464 | 0 | 5632 | 1.6 |iter: 87/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 7700992 | 0 | 5696 | 1.6 |iter: 88/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 7787520 | 0 | 5760 | 1.6 |iter: 89/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 7874048 | 0 | 5824 | 1.6 |iter: 90/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 7960576 | 0 | 5888 | 1.6 |iter: 91/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 8047104 | 0 | 5952 | 1.7 |iter: 92/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 8133632 | 0 | 6016 | 1.7 |iter: 93/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 8220160 | 0 | 6080 | 1.7 |iter: 94/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 8306688 | 0 | 6144 | 1.7 |iter: 95/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 8393216 | 0 | 6208 | 1.7 |iter: 96/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 8479744 | 0 | 6272 | 1.7 |iter: 97/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 8566272 | 0 | 6336 | 1.8 |iter: 98/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 8652800 | 0 | 6400 | 1.8 |iter: 99/100
5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 8652800 | 0 | 6400 | 1.8 |reached max iterations
Simulation stopped as no error occurred @ EbNo = 5.0 dB.
Plot BLER and BER Curves¶
In [10]:
Copied!
ber_dm = ber_dm.cpu().numpy()
bler_dm = bler_dm.cpu().numpy()
ber_rl = ber_rl.cpu().numpy()
bler_rl = bler_rl.cpu().numpy()
# Replace exact zeros with a floor so log scale plots look clean
FLOOR = 1e-4
bler_dm = np.where(bler_dm == 0, FLOOR, bler_dm)
bler_rl = np.where(bler_rl == 0, FLOOR, bler_rl)
ber_dm = np.where(ber_dm == 0, FLOOR, ber_dm)
ber_rl = np.where(ber_rl == 0, FLOOR, ber_rl)
fig, axes = plt.subplots(1, 2, figsize=(13, 5))
# --- BLER ---
ax = axes[0]
ax.semilogy(EBNO_DB_RANGE, bler_dm, "b-o", label="DeepMIMO (ray-tracing)")
ax.semilogy(EBNO_DB_RANGE, bler_rl, "r--s", label="Rayleigh fading")
ax.axhline(0.1, color="gray", linestyle=":", linewidth=0.8, label="10% BLER")
ax.set_xlabel("Eb/N0 [dB]")
ax.set_ylabel("BLER")
ax.set_title(f"5G NR PUSCH BLER\n({SCENARIO}, {N_BS_ANT}-ant BS, {N_PATHS} paths)")
ax.legend()
ax.grid(visible=True, which="both", alpha=0.3)
ax.set_ylim([FLOOR / 2, 1.5])
# --- BER ---
ax = axes[1]
ax.semilogy(EBNO_DB_RANGE, ber_dm, "b-o", label="DeepMIMO (ray-tracing)")
ax.semilogy(EBNO_DB_RANGE, ber_rl, "r--s", label="Rayleigh fading")
ax.set_xlabel("Eb/N0 [dB]")
ax.set_ylabel("BER")
ax.set_title(f"5G NR PUSCH BER\n({SCENARIO}, {N_BS_ANT}-ant BS, {N_PATHS} paths)")
ax.legend()
ax.grid(visible=True, which="both", alpha=0.3)
ax.set_ylim([FLOOR / 2, 1.0])
plt.tight_layout()
plt.savefig("sionna_pusch_bler.png", dpi=100, bbox_inches="tight")
plt.show()
ber_dm = ber_dm.cpu().numpy()
bler_dm = bler_dm.cpu().numpy()
ber_rl = ber_rl.cpu().numpy()
bler_rl = bler_rl.cpu().numpy()
# Replace exact zeros with a floor so log scale plots look clean
FLOOR = 1e-4
bler_dm = np.where(bler_dm == 0, FLOOR, bler_dm)
bler_rl = np.where(bler_rl == 0, FLOOR, bler_rl)
ber_dm = np.where(ber_dm == 0, FLOOR, ber_dm)
ber_rl = np.where(ber_rl == 0, FLOOR, ber_rl)
fig, axes = plt.subplots(1, 2, figsize=(13, 5))
# --- BLER ---
ax = axes[0]
ax.semilogy(EBNO_DB_RANGE, bler_dm, "b-o", label="DeepMIMO (ray-tracing)")
ax.semilogy(EBNO_DB_RANGE, bler_rl, "r--s", label="Rayleigh fading")
ax.axhline(0.1, color="gray", linestyle=":", linewidth=0.8, label="10% BLER")
ax.set_xlabel("Eb/N0 [dB]")
ax.set_ylabel("BLER")
ax.set_title(f"5G NR PUSCH BLER\n({SCENARIO}, {N_BS_ANT}-ant BS, {N_PATHS} paths)")
ax.legend()
ax.grid(visible=True, which="both", alpha=0.3)
ax.set_ylim([FLOOR / 2, 1.5])
# --- BER ---
ax = axes[1]
ax.semilogy(EBNO_DB_RANGE, ber_dm, "b-o", label="DeepMIMO (ray-tracing)")
ax.semilogy(EBNO_DB_RANGE, ber_rl, "r--s", label="Rayleigh fading")
ax.set_xlabel("Eb/N0 [dB]")
ax.set_ylabel("BER")
ax.set_title(f"5G NR PUSCH BER\n({SCENARIO}, {N_BS_ANT}-ant BS, {N_PATHS} paths)")
ax.legend()
ax.grid(visible=True, which="both", alpha=0.3)
ax.set_ylim([FLOOR / 2, 1.0])
plt.tight_layout()
plt.savefig("sionna_pusch_bler.png", dpi=100, bbox_inches="tight")
plt.show()
Summary¶
| Step | Tool | Role |
|---|---|---|
| 1. Load dataset | dm.load |
Ray-traced paths: coefficients + delays |
| 2. Build CIR generator | deepmimo_ul_cir_gen |
UL reciprocity + normalisation |
| 3. Wrap as channel model | CIRDataset + OFDMChannel |
Sionna-compatible channel block |
| 4. 5G NR transmitter | PUSCHTransmitter |
Coded + modulated OFDM slot |
| 5. 5G NR receiver | PUSCHReceiver |
LS + LMMSE + LDPC |
| 6. Simulate | sim_ber |
BLER / BER vs Eb/N0 |
Key design choices:
- Channel normalisation: path loss is removed; SNR is set entirely by
no. This is standard for link-level simulation. - Reciprocity: we have the DL channel (BS → UE); UL = conjugate transpose of DL is a valid approximation for TDD and slowly-varying channels.
- Rayleigh baseline: unit-variance i.i.d. Rayleigh fading, same SIMO antenna count. Any gap between the two curves reflects the richer scattering structure captured by ray-tracing.