#!/usr/bin/env python3.4
#
# Copyright 2022 - The Android Open Source Project
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import csv
import itertools
import numpy
import json
import os
from acts import context
from acts import base_test
from acts.metrics.loggers.blackbox import BlackboxMappedMetricLogger
from acts_contrib.test_utils.cellular.performance import cellular_performance_test_utils as cputils
from acts_contrib.test_utils.wifi import wifi_performance_test_utils as wputils
from acts_contrib.test_utils.wifi.wifi_performance_test_utils.bokeh_figure import BokehFigure
from acts_contrib.test_utils.cellular.performance.CellularThroughputBaseTest import CellularThroughputBaseTest
from functools import partial
class CellularFr1SensitivityTest(CellularThroughputBaseTest):
"""Class to test single cell FR1 NSA sensitivity"""
def __init__(self, controllers):
base_test.BaseTestClass.__init__(self, controllers)
self.testcase_metric_logger = (
BlackboxMappedMetricLogger.for_test_case())
self.testclass_metric_logger = (
BlackboxMappedMetricLogger.for_test_class())
self.publish_testcase_metrics = True
self.testclass_params = self.user_params['nr_sensitivity_test_params']
self.tests = self.generate_test_cases(
channel_list=['LOW', 'MID', 'HIGH'],
dl_mcs_list=list(numpy.arange(27, -1, -1)),
nr_ul_mcs=4,
lte_dl_mcs_table='QAM256',
lte_dl_mcs=4,
lte_ul_mcs_table='QAM256',
lte_ul_mcs=4,
transform_precoding=0,
schedule_scenario='FULL_TPUT',
schedule_slot_ratio=80
)
def process_testclass_results(self):
# Plot individual test id results raw data and compile metrics
plots = collections.OrderedDict()
compiled_data = collections.OrderedDict()
for testcase_name, testcase_data in self.testclass_results.items():
nr_cell_index = testcase_data['testcase_params'][
'endc_combo_config']['lte_cell_count']
cell_config = testcase_data['testcase_params'][
'endc_combo_config']['cell_list'][nr_cell_index]
test_id = tuple(('band', cell_config['band']))
if test_id not in plots:
# Initialize test id data when not present
compiled_data[test_id] = {
'mcs': [],
'average_throughput': [],
'theoretical_throughput': [],
'cell_power': [],
}
plots[test_id] = BokehFigure(
title='Band {} - BLER Curves'.format(cell_config['band']),
x_label='Cell Power (dBm)',
primary_y_label='BLER (Mbps)')
test_id_rvr = test_id + tuple('RvR')
plots[test_id_rvr] = BokehFigure(
title='Band {} - RvR'.format(cell_config['band']),
x_label='Cell Power (dBm)',
primary_y_label='PHY Rate (Mbps)')
# Compile test id data and metrics
compiled_data[test_id]['average_throughput'].append(
testcase_data['average_throughput_list'])
compiled_data[test_id]['cell_power'].append(
testcase_data['cell_power_list'])
compiled_data[test_id]['mcs'].append(
testcase_data['testcase_params']['nr_dl_mcs'])
# Add test id to plots
plots[test_id].add_line(
testcase_data['cell_power_list'],
testcase_data['bler_list'],
'MCS {}'.format(testcase_data['testcase_params']['nr_dl_mcs']),
width=1)
plots[test_id_rvr].add_line(
testcase_data['cell_power_list'],
testcase_data['average_throughput_list'],
'MCS {}'.format(testcase_data['testcase_params']['nr_dl_mcs']),
width=1,
style='dashed')
for test_id, test_data in compiled_data.items():
test_id_rvr = test_id + tuple('RvR')
cell_power_interp = sorted(set(sum(test_data['cell_power'], [])))
average_throughput_interp = []
for mcs, cell_power, throughput in zip(
test_data['mcs'], test_data['cell_power'],
test_data['average_throughput']):
throughput_interp = numpy.interp(cell_power_interp,
cell_power[::-1],
throughput[::-1])
average_throughput_interp.append(throughput_interp)
rvr = numpy.max(average_throughput_interp, 0)
plots[test_id_rvr].add_line(cell_power_interp, rvr,
'Rate vs. Range')
figure_list = []
for plot_id, plot in plots.items():
plot.generate_figure()
figure_list.append(plot)
output_file_path = os.path.join(self.log_path, 'results.html')
BokehFigure.save_figures(figure_list, output_file_path)
"""Saves CSV with all test results to enable comparison."""
results_file_path = os.path.join(
context.get_current_context().get_full_output_path(),
'results.csv')
with open(results_file_path, 'w', newline='') as csvfile:
field_names = [
'Test Name', 'Sensitivity'
]
writer = csv.DictWriter(csvfile, fieldnames=field_names)
writer.writeheader()
for testcase_name, testcase_results in self.testclass_results.items(
):
row_dict = {
'Test Name': testcase_name,
'Sensitivity': testcase_results['sensitivity']
}
writer.writerow(row_dict)
def process_testcase_results(self):
if self.current_test_name not in self.testclass_results:
return
testcase_data = self.testclass_results[self.current_test_name]
bler_list = []
average_throughput_list = []
theoretical_throughput_list = []
nr_cell_index = testcase_data['testcase_params']['endc_combo_config'][
'lte_cell_count']
cell_power_list = testcase_data['testcase_params']['cell_power_sweep'][
nr_cell_index]
for result in testcase_data['results']:
bler_list.append(result['throughput_measurements']
['nr_bler_result']['total']['DL']['nack_ratio'])
average_throughput_list.append(
result['throughput_measurements']['nr_tput_result']['total']
['DL']['average_tput'])
theoretical_throughput_list.append(
result['throughput_measurements']['nr_tput_result']['total']
['DL']['theoretical_tput'])
padding_len = len(cell_power_list) - len(average_throughput_list)
average_throughput_list.extend([0] * padding_len)
theoretical_throughput_list.extend([0] * padding_len)
bler_above_threshold = [
bler > self.testclass_params['bler_threshold']
for bler in bler_list
]
for idx in range(len(bler_above_threshold)):
if all(bler_above_threshold[idx:]):
sensitivity_idx = max(idx, 1) - 1
break
else:
sensitivity_idx = -1
sensitivity = cell_power_list[sensitivity_idx]
self.log.info('NR Band {} MCS {} Sensitivity = {}dBm'.format(
testcase_data['testcase_params']['endc_combo_config']['cell_list']
[nr_cell_index]['band'],
testcase_data['testcase_params']['nr_dl_mcs'], sensitivity))
testcase_data['bler_list'] = bler_list
testcase_data['average_throughput_list'] = average_throughput_list
testcase_data[
'theoretical_throughput_list'] = theoretical_throughput_list
testcase_data['cell_power_list'] = cell_power_list
testcase_data['sensitivity'] = sensitivity
results_file_path = os.path.join(
context.get_current_context().get_full_output_path(),
'{}.json'.format(self.current_test_name))
with open(results_file_path, 'w') as results_file:
json.dump(wputils.serialize_dict(testcase_data),
results_file,
indent=4)
def get_per_cell_power_sweeps(self, testcase_params):
# get reference test
nr_cell_index = testcase_params['endc_combo_config']['lte_cell_count']
current_band = testcase_params['endc_combo_config']['cell_list'][
nr_cell_index]['band']
reference_test = None
reference_sensitivity = None
for testcase_name, testcase_data in self.testclass_results.items():
if testcase_data['testcase_params']['endc_combo_config'][
'cell_list'][nr_cell_index]['band'] == current_band:
reference_test = testcase_name
reference_sensitivity = testcase_data['sensitivity']
if reference_test and reference_sensitivity and not self.retry_flag:
start_atten = reference_sensitivity + self.testclass_params[
'adjacent_mcs_gap']
self.log.info(
"Reference test {} found. Sensitivity {} dBm. Starting at {} dBm"
.format(reference_test, reference_sensitivity, start_atten))
else:
start_atten = self.testclass_params['nr_cell_power_start']
self.log.info(
"Reference test not found. Starting at {} dBm".format(
start_atten))
# get current cell power start
nr_cell_sweep = list(
numpy.arange(start_atten,
self.testclass_params['nr_cell_power_stop'],
self.testclass_params['nr_cell_power_step']))
lte_sweep = [self.testclass_params['lte_cell_power']
] * len(nr_cell_sweep)
if nr_cell_index == 0:
cell_power_sweeps = [nr_cell_sweep]
else:
cell_power_sweeps = [lte_sweep, nr_cell_sweep]
return cell_power_sweeps
def generate_test_cases(self, channel_list, dl_mcs_list, **kwargs):
test_cases = []
with open(self.testclass_params['nr_single_cell_configs'],
'r') as csvfile:
test_configs = csv.DictReader(csvfile)
for test_config, channel, nr_dl_mcs in itertools.product(
test_configs, channel_list, dl_mcs_list):
if int(test_config['skip_test']):
continue
endc_combo_config = cputils.generate_endc_combo_config_from_csv_row(
test_config)
test_name = 'test_fr1_{}_{}_dl_mcs{}'.format(
test_config['nr_band'], channel.lower(), nr_dl_mcs)
test_params = collections.OrderedDict(
endc_combo_config=endc_combo_config,
nr_dl_mcs=nr_dl_mcs,
**kwargs)
setattr(self, test_name,
partial(self._test_throughput_bler, test_params))
test_cases.append(test_name)
return test_cases
class CellularFr1Sensitivity_SampleMCS_Test(CellularFr1SensitivityTest):
"""Class to test single cell FR1 NSA sensitivity"""
def __init__(self, controllers):
base_test.BaseTestClass.__init__(self, controllers)
self.testcase_metric_logger = (
BlackboxMappedMetricLogger.for_test_case())
self.testclass_metric_logger = (
BlackboxMappedMetricLogger.for_test_class())
self.publish_testcase_metrics = True
self.testclass_params = self.user_params['nr_sensitivity_test_params']
self.tests = self.generate_test_cases(
channel_list=['LOW'],
dl_mcs_list=[27, 25, 16, 9],
nr_ul_mcs=4,
lte_dl_mcs_table='QAM256',
lte_dl_mcs=4,
lte_ul_mcs_table='QAM256',
lte_ul_mcs=4,
transform_precoding=0,
schedule_scenario='FULL_TPUT',
schedule_slot_ratio=80
)