UL BLER vs SNR plot

Added a tool for generating UL BLER vs SNR from nr_ulsim runs.
2024-12-16 16:10:36 +01:00
parent 054506f5ae
commit 28b7e0b7d5
4 changed files with 151 additions and 0 deletions
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+# Plotting tools
+
+## `ul_bler_vs_snr_graph.py`
+
+This generates UL BLER vs SNR plots using nr_ulsim. It uses cache so subsequent runs are faster.
+Assumes your nr_ulsim is in `../../cmake_targets/ran_build/build`. Run with -h flag to see options
+
+Example graph:
+
+![image](./example.png)
+
+### Cache usage and modifting `nr_ulsim` command
+
+Modify the script call to `nr_ulsim` if you want a different channel model or other flags in the `nr_ulsim` command.
+Remember to clear the cache (remove `cache.pkl` from this folder) so that the results from a different run are not 
+taken from the cache.
+ 
+Same goes for any software modifications of `nr_ulsim` - the cache is not aware of the software version used to generate
+the data - remember to clear your cache manually if you are comparing different `nr_ulsim` versions.
+
+The script also allows to skip usage of the cache with `--rerun` option.
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+matplotlib==3.8.3
+numpy==1.21.5
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+#!/bin/python3
+import os
+import subprocess
+import numpy as np
+import re
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+import pickle
+import argparse
+import concurrent.futures
+import traceback
+import multiprocessing
+
+default_ulsim_cmd = "../../cmake_targets/ran_build/build/nr_ulsim  -i 1,0 -t 100 -R 273"
+ulsim_args = "-n {} -s {} -S {} -m {} -r {}"
+
+def find_throughput(line):
+    ### Example line
+    ### SNR 10.000000: Channel BLER (0.000000e+00,-nan,-nan,-nan Channel BER (1.548551e-03,-nan,-nan,-nan) Avg round 1.00, Eff Rate 1672.0000 bits/slot, Eff Throughput 100.00, TBS 1672 bits/slot
+    ### SNR -0.000000: Channel BLER (0.000000e+00,-nan,-nan,-nan Channel BER (1.888043e-01,-nan,-nan,-nan) Avg round 1.00, Eff Rate 2152.0000 bits/slot, Eff Throughput 100.00, TBS 2152 bits/slot
+    pattern = r"SNR (-?\d+.\d+).*BLER \((-?\d+\.\d*(?:[eE][+-]\d+)?),.*Eff Throughput (\d+.\d+)"
+    match = re.search(pattern, line)
+    if match:
+        snr, bler, thp = match.groups()
+        return float(snr), float(bler), float(thp)
+    return None, None, None
+
+def generate_mcs_thps(ulsim_cmd_with_args, snr, mcs, num_runs = 10, num_rbs = 50, cache = {}):
+    thp = 0
+    bler = 0
+    num_runs_cached = 0
+    cache_string = "{},{},{}".format(snr, mcs, num_rbs)
+    if cache_string in cache:
+        thp, bler, num_runs_cached = cache[cache_string]
+        if num_runs_cached >= num_runs:
+            return bler, thp
+    
+    num_runs_left = num_runs - num_runs_cached
+    
+    ulsim_cmd_formatted = ulsim_cmd_with_args.format(num_runs_left, snr, snr+0.01, mcs, num_rbs)
+    print(ulsim_cmd_formatted, "result insufficient in cache, running")
+    process = subprocess.Popen(ulsim_cmd_formatted.split(" "), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    stdout_data = process.communicate()[0]
+    output = ""
+    if stdout_data is not None:
+        output = stdout_data.decode()
+    for line in output.splitlines():
+        _snr, _bler, _thp = find_throughput(line)
+        if _snr is not None:
+            bler = (bler * num_runs_cached + _bler * num_runs_left) / num_runs
+            thp = (thp * num_runs_cached + _thp * num_runs_left) / num_runs
+            cache[cache_string] = thp, bler, num_runs
+            return bler, thp
+    print("error processing "+ ulsim_cmd_with_args)
+    print(output)
+    print(process.communicate()[1].decode())
+    return None, None
+
+
+
+parser = argparse.ArgumentParser(description="UL SNR/BLER plot generator")
+parser.add_argument("--snr-start", help="Starting SNR value")
+parser.add_argument("--snr-stop", help="Final SNR value")
+parser.add_argument("--snr-step", help="SNR step value")
+parser.add_argument("--mcs-start", help="First MCS to test")
+parser.add_argument("--mcs-stop", help="Last MCS to test")
+parser.add_argument("--num-runs", help="Number of simulations per SNR/MCS value")
+parser.add_argument("--rerun", help="Do not use cache to read data, run from scrach", action="store_true")
+parser.add_argument("--num-rbs", help="Number of RBs")
+parser.add_argument("--ulsim-cmd", help="ULSIM command to run. Do not use `sSrnm` flags, as these are used by the script", default=default_ulsim_cmd)
+
+cpu_count = multiprocessing.cpu_count()
+cpu_to_use = max(1, cpu_count - 2)
+
+parser.add_argument("--num-threads", help="Number of threads to run", default=cpu_to_use)
+args = parser.parse_args()
+
+_snr_start = float(args.snr_start)
+_snr_step = float(args.snr_step)
+_snr_end = float(args.snr_stop) + _snr_step/2
+_mcs_start = int(args.mcs_start)
+_mcs_stop = int(args.mcs_stop)
+_num_runs = int(args.num_runs)
+_num_rbs = int(args.num_rbs)
+ulsim_cmd = args.ulsim_cmd + " " + ulsim_args
+num_threads = int(args.num_threads)
+
+cmap = cm.viridis
+plt.figure()
+plt.ylim(-0.1, 1.1)
+plt.title("BLER vs SNR for MCS{} to MCS{}, nRB={} (num_runs = {})".format(_mcs_start, _mcs_stop, _num_rbs, _num_runs))
+plt.xlabel("SNR [dB]")
+plt.ylabel("BLER")
+
+cache = {}
+if not args.rerun:
+    try:
+        with open("cache.pkl", "rb+") as f:
+            cache = pickle.load(f)
+    except FileNotFoundError:
+        pass # no cache found
+
+snrs_list = np.arange(_snr_start, _snr_end, _snr_step)
+mcss_list = np.arange(_mcs_start, _mcs_stop + 1, 1)
+plot_index = 0
+for mcs in mcss_list:
+    results = {}
+    with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
+        result_to_snr_index = {executor.submit(generate_mcs_thps, ulsim_cmd, snrs_list[snr_index], mcs, _num_runs, _num_rbs, cache): snr_index for (snr_index, _) in enumerate(snrs_list)}
+        for future in concurrent.futures.as_completed(result_to_snr_index):
+            snr_index = result_to_snr_index[future]
+            bler, thp = future.result()
+            results[snr_index] = bler
+    blers = np.zeros_like(snrs_list)
+    for snr_index in results:
+        blers[snr_index] = results[snr_index]
+
+    num_plots = len(mcss_list)
+    percentage = float(plot_index) / max(1, (num_plots - 1))
+    color = cmap(percentage)
+    plot_index += 1
+    plt.plot(snrs_list, blers, label="MCS{}".format(mcs), color = color)
+    plt.legend()
+    plt.savefig('MCS={}-{},SNR={}-{},RBs={}graph.png'.format(_mcs_start, _mcs_stop, _snr_start, _snr_end, _num_rbs))
+if not args.rerun:
+    with open("cache.pkl", "wb+") as f:
+        pickle.dump(cache, f)
+