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Commit 4879ea30 authored by GuJingLing's avatar GuJingLing
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      measurement/openairinterface5g-enb/oaiflowclassification/classify.py 0 → 100644
      View file @ 4879ea30
      """
      预测函数：随机森林
      每次运行前，检查：
      四个需要修改的地方，命名是否正确
      最后的运行模式是否正确
      做预测
      1. 直接使用之前的模型做预测 分类结果和五元组封装起来
      2. 做一个线程池 多线程接收发送来的特征信息
      """
      from common_utils import * # 修改了
      from argparse import ArgumentParser
      from collections import namedtuple
      from typing import List, Dict
      from datetime import datetime
      from path_utils import get_prj_root
      import numpy as np
      # from sklearn.externals import joblib
      import joblib
      import pprint
      from sklearn.ensemble import RandomForestClassifier # 训练模型
      random.seed(datetime.now())
      model_dir = os.path.join(get_prj_root(), "classify/models") # 修改：模型model文件夹路径
      predict_model_pkl = os.path.join(model_dir, "dt2.pkl") # 修改：模型的版本，只用修改此处就行
      Instance = namedtuple("Instance", ["features", "label"]) # 实例
      dirs = {
      "video": "./tmp/dt/video",
      "iot": "./tmp/dt/iot",
      "voip": "./tmp/dt/voip",
      "AR": "./tmp/dt/AR",
      }
      instances_dir = os.path.join(get_prj_root(), "classify/instances") # 修改：instances路径
      # 通过元组测试
      test_flow = (1, 2, 3, 4, 5, 6, 7, 8, 6, 10)
      test_flow2 = [221.0, 1350.0, 640.0, 376.26798960315506, 543.0,
      21257877.349853516, 4793407917.022705, 1263437211.5135193,
      2039103758.0566826, 119541525.84075928]
      def train_and_predict():
      iot = load_pkl(os.path.join(instances_dir, "iot.pkl")) # 不同实例的pkl是不同特征的
      videos = load_pkl(os.path.join(instances_dir, "video.pkl"))
      voip = load_pkl(os.path.join(instances_dir, "voip.pkl"))
      AR = load_pkl(os.path.join(instances_dir, "AR.pkl"))
      for i in videos:
      assert i.label == 0
      for i in iot:
      assert i.label == 1
      for i in voip:
      assert i.label == 2
      for i in AR:
      assert i.label == 3
      # print(videos)
      debug("# iot instances {}".format(len(iot)))
      debug("# video instances {}".format(len(videos)))
      debug("# VOIP instances {}".format(len(voip)))
      debug("# AR instances {}".format(len(AR)))
      random.shuffle(voip) # 打乱排序
      random.shuffle(iot)
      random.shuffle(videos)
      random.shuffle(AR)
      n_video_train = int(len(videos) * 0.7)
      n_video_test = len(videos) - n_video_train
      video_train = videos[:n_video_train]
      video_test = videos[n_video_train:]
      iot_train = iot[:n_video_train]
      iot_test = iot[len(iot) - len(video_test):]
      voip_train = voip[:n_video_train]
      voip_test = voip[len(voip) - len(video_test):]
      AR_train = AR[:n_video_train]
      AR_test = AR[len(AR) - len(video_test):]
      info("#video train {}".format(len(video_train)))
      info("#iot train {}".format(len(iot_train)))
      info("#voip train {}".format(len(voip_train)))
      info("#AR train {}".format(len(AR_train)))
      train = []
      train.extend(iot_train)
      train.extend(video_train)
      train.extend(voip_train)
      train.extend(AR_train)
      random.shuffle(train)
      train_x = [x.features for x in train]
      train_y = [x.label for x in train]
      # test 1:1
      test = []
      info("#video test {}".format(len(video_test)))
      info("#iot test {}".format(len(iot_test)))
      info("#voip test {}".format(len(voip_test)))
      info("#AR test {}".format(len(AR_test)))
      test.extend(video_test)
      test.extend(iot_test)
      test.extend(voip_test)
      test.extend(AR_test)
      random.shuffle(test)
      test_x = [t.features for t in test]
      test_y = [t.label for t in test]
      # 训练以及预测
      predict_model = RandomForestClassifier(oob_score=True) # 引入训练方法
      predict_model.fit(train_x, train_y) # 队训练数据进行拟合
      predicts = predict_model.predict(test_x)
      """
      dt = DT()
      dt.fit((train_x, train_y))
      predicts = dt.predict(test_x)
      """
      """
      # 打印预测的结果
      print(predicts)
      print("-------------------------------")
      """
      # 保存模型
      fn_name = os.path.join(model_dir, predict_model_pkl)
      joblib.dump(predict_model, predict_model_pkl)
      # 评价模型
      count = 0
      for idx in range(len(test_x)):
      if int(predicts[idx]) == int(test_y[idx]):
      count += 1
      # print(count / len(test_x))
      return count / len(test_x)
      def classify_flows(mode: 'int', predict_dir):
      """
      该函数用于训练模型并且测试模型的准确度 或者 预测结果
      :param mode: 0--训练模型 1--预测和分类流并返回
      :param predict_dir: 待预测的流的目录下的pkl文件
      :return: 待分类的流的分类结果列表
      """
      # 判断是只训练模型 还是 只是预测结果
      if mode == 0:
      # 此时训练使用数据训练模型 并且 保存模型 评价模型
      times = 10
      sum_predict = 0
      for _ in range(times):
      res = train_and_predict()
      sum_predict = sum_predict + res
      print("模型准确率为:", sum_predict / times)
      else:
      # 使用传递的文件来预测结果并且返回
      predict = load_pkl(os.path.join(predict_dir, "predict2.pkl"))
      test = []
      info("#video test {}".format(len(predict)))
      test.extend(predict)
      # random.shuffle(test)
      test_x = [t.features for t in test]
      predict_model = joblib.load(predict_model_pkl)
      predict_result = predict_model.predict(test_x)
      res_list = identify_classification(predict_result)
      return res_list
      def classify_flow_list(flow_list):
      """
      该方法用于分类为元组的流
      格式：[[1, 2, 3, 4, 5, 6, 7, 8, 6, 10], ["五元组"]]
      """
      test_x = [flow_list[0]]
      predict_model = joblib.load(predict_model_pkl)
      predict_result = predict_model.predict(test_x)
      # 定义结果的变量 res = ["五元组", "分类结果"]
      res = []
      res.append(flow_list[1][0])
      # 得到字符串的结果
      if predict_result == 0:
      res.append("videos")
      elif predict_result == 1:
      res.append("iot")
      elif predict_result == 2:
      res.append("voip")
      else:
      res.append("AR")
      return res
      def identify_classification(predict_result):
      """
      该函数将分类结果的标签转换为具体内容字符串的结果
      :param predict_result:标签分类结果
      :return: 字符串分类结果
      """
      res_list = []
      for label in predict_result:
      if label == 0:
      res_list.append("videos")
      elif label == 1:
      res_list.append("iot")
      elif label == 2:
      res_list.append("voip")
      elif label == 3:
      res_list.append("AR")
      return res_list
      if __name__ == '__main__':
      # 训练模型
      # classify_flows(mode=0, path=instances_dir)
      # 预测结果
      predict_dir = os.path.join(get_prj_root(), "classify/predict") # 修改：instances路径
      predict_result_list = classify_flows(mode=1, predict_dir=predict_dir)
      pprint.pprint(predict_result_list)
      measurement/openairinterface5g-enb/oaiflowclassification/common_utils.py 0 → 100644
      View file @ 4879ea30
      import json
      import os
      import pickle
      import random
      from pathlib import Path
      import loguru
      import numpy as np
      logger = loguru.logger
      info = logger.info
      debug = logger.debug
      err = logger.error
      def check_dir(dir_name):
      if not Path(dir_name).is_dir():
      raise FileNotFoundError
      def check_file(fn):
      if not Path(fn).is_file():
      raise FileNotFoundError
      def file_exsit(fn):
      return Path(fn).is_file()
      def dir_exsit(fn):
      return Path(fn).is_dir()
      def gaussion(mean: float, std_var: float, size=1):
      if size == 1:
      return np.random.normal(mean, std_var)
      return np.random.normal(mean, std_var, size)
      def exp(mean: float, size=1):
      if 1 == size:
      return np.random.normal(mean)
      return np.random.normal(mean, size)
      def uniform(low, up, size=1):
      if 1 == size:
      return np.random.uniform(low, up)
      return np.random.uniform(low, up, size)
      def load_pkl(filename):
      if Path(filename).is_file():
      data = None
      with open(filename, 'rb') as file:
      data = pickle.load(file)
      file.close()
      return data
      raise FileNotFoundError
      def save_pkl(filename, obj, overwrite=True):
      def write():
      with open(filename, 'wb') as file:
      pickle.dump(obj, file)
      file.flush()
      file.close()
      if Path(filename).is_file() and overwrite:
      write()
      return
      write()
      def load_json(filename):
      if Path(filename).is_file():
      with open(filename) as f:
      return json.load(f)
      raise FileNotFoundError
      def save_json(filename, obj, overwrite=True):
      def write():
      with open(filename, 'w', encoding="utf8") as file:
      json.dump(obj, file, indent=4)
      if Path(filename).is_file and overwrite:
      write()
      return
      write()
      def is_digit(x: str) -> bool:
      try:
      float(x)
      return True
      except:
      return False
      def normalize(x):
      mi = min(x)
      ma = max(x)
      diff = ma - mi
      x = [(xx - mi) / diff for xx in x]
      return x
      if __name__ == "__main__":
      pass
      measurement/openairinterface5g-enb/oaiflowclassification/instructions.txt 0 → 100644
      View file @ 4879ea30
      由于路径的问题，在import上作了一些删除
      在classify文件夹下是运行的数据
      先运行parser产生解析PCAP之后的文件，放在./tmp/dt的目录下，statistics.json为各个类型流的数据
      再运行train，先把最下面的模式设置为1，产生对statistics.json中包大小，包间隔进行处理产生特征，保存在instances文件夹的pkl中
      最后是运行predict，预测
      train_dt_1
      设置的是8个特征的（前四个包大小，后四个包间隔）+决策树 。。。
      特征与标签都保存到instances文件夹，
      训练模型保存到models文件夹中
      正确率可以达到90%
      加了PCA降维之后为88.6%
      train_dt_2
      设置的是10个特征的（前五个包大小，后五个包间隔）：最小值，最大值，平均值，方差，中位数 +决策树
      特征与标签都保存到instances2文件夹，
      训练模型保存到models2文件夹中
      正确率可以达到91.73%
      加了PCA降维之后为88.7%
      train_dt_3
      11个特征（前五个包大小，后五个包间隔）：最小值，最大值，平均值，方差，中位数 ；每秒包的数量
      预测函数：决策树
      特征与标签都保存到instances3文件夹
      训练模型保存到models3文件夹中
      正确率可以达到91.7%
      加了PCA降维之后为88.7%
      instances装的是 8个特征的（前四个包大小，后四个包间隔）
      pkl文件其实可以处理成Excel表格的形式，然后用MATLAB来跑
      predit_1_1 K邻近+8特征 85%
      predit_1_2 K邻近+10特征 86.59%
      predit_2 支持向量机 + 8特征 25.9%
      predict_3 随机森林 +8特征 93.5%
      predict_3 随机森林 +10特征 94%
      predict_3 随机森林 +11特征 93.5%
      models文件下
      model_predict文件夹装的是各种预测函数
      dt1为 K邻近+8特征 85%
      dt1_2 为 K邻近+10特征 86.59%
      dt3_1 为 随机森林 +8特征 93.5%
      dt3_2 为 随机森林 +10特征 94%
      有后缀_9 说明是用9个窗口
      measurement/openairinterface5g-enb/oaiflowclassification/main.py 0 → 100644
      View file @ 4879ea30
      from classify import *
      from multithread_server import TcpServer, boot_server
      from pool import *
      import json
      def load_flows():
      # 一直执行
      while True:
      # 判断是否停止识别
      """
      mutex.acquire()
      if len(STOP_CLASSIFY) == 1:
      print("程序结束")
      mutex.release()
      break
      mutex.release()
      """
      mutex.acquire()
      if PREDECT_FLOWS:
      # 读出所有的结果
      for feature in PREDECT_FLOWS:
      if type(feature) == list:
      # print("type: ", type(feature))
      # 预测结果
      predict_result = classify_flow_list(feature)
      # 将分类结果放到全局变量中
      if predict_result not in CLASSIFY_RESULT:
      CLASSIFY_RESULT.append(predict_result)
      print("缓冲池中有: ", len(CLASSIFY_RESULT), "个已经分类好的结果，请取走")
      print("分类结果为：", predict_result)
      # 清空列表
      PREDECT_FLOWS.clear()
      mutex.release()
      # 定义一个函数作为主线程 让其他的守护他 判断关键字close后关闭主线称
      def main_threading():
      global STOP_CLASSIFY
      # 创建线程
      t_server = threading.Thread(target = boot_server)
      t_classify = threading.Thread(target = load_flows)
      # 设置其他的为守护线程
      t_server.setDaemon(True)
      t_classify.setDaemon(True)
      # 启动线程
      t_server.start()
      t_classify.start()
      while True:
      mutex.acquire()
      if len(STOP_CLASSIFY) == 1:
      print("程序结束")
      mutex.release()
      break
      mutex.release()
      if __name__ == "__main__":
      main_threading()
      measurement/openairinterface5g-enb/oaiflowclassification/model.py 0 → 100644
      View file @ 4879ea30
      from sklearn.tree import DecisionTreeClassifier
      from path_utils import get_prj_root
      from pathlib import Path
      import os
      from common_utils import save_pkl, load_pkl, info #修改了
      import numpy as np
      import random
      root_dir = get_prj_root()
      model_dir = os.path.join(root_dir, "models")
      class Classifier:
      # def __init__(self,fn_name=None):
      # self.model=None
      # if fn_name is not None:
      # self.load_model(fn_name)
      def fit(self, data):
      raise NotImplementedError
      def predict(self, features):
      raise NotImplementedError
      def save_model(self, fn_name):
      raise NotImplementedError
      def load_model(self, fn_name):
      raise NotImplementedError
      '''
      min_pkt|max_pkt|mean_pkt|var_pkt
      min_idt|max_idt|mean_idt|var_idt
      '''
      class DT(Classifier):
      def __init__(self):
      super(DT, self).__init__()
      self.model: DecisionTreeClassifier = DecisionTreeClassifier()
      def fit(self, data):
      assert len(data) == 2
      features = data[0]
      y = data[1]
      assert len(features) == len(y)
      info("# instances {}".format(len(features)))
      self.model.fit(features, y)
      def predict(self, features):
      # info("# instances {}".format(len(features)))
      return self.model.predict(features)
      def save_model(self, fn_name):
      if self.model is None: return
      fn_name = os.path.join(model_dir, fn_name)
      save_pkl(fn_name, self.model)
      def load_model(self, fn_name):
      fn_name = os.path.join(model_dir, fn_name)
      self.model: DecisionTreeClassifier = load_pkl(fn_name)
      class Dumb(Classifier):
      def fit(self, data):
      pass
      def predict(self, features):
      if random.random() >= 0.5:
      return 1
      return 0
      def save_model(self, fn_name):
      pass
      def load_model(self, fn_name):
      pass
      if __name__ == '__main__':
      from sklearn.datasets import load_iris
      x, y = load_iris(return_X_y=True)
      model = DT()
      model.fit((x, y))
      model.save_model("test.pkl")
      model.load_model("test.pkl")
      measurement/openairinterface5g-enb/oaiflowclassification/multithread_client.py 0 → 100644
      View file @ 4879ea30
      from socket import *
      from threading import Thread
      from random import choice
      import json
      import time
      BUFFER_SIZE = 8192
      # 随机发送数据测试的库:
      data_list = [
      [[221.0, 1350.0, 640.0, 376.26798960315506, 543.0, 21257877.349853516,
      4793407917.022705, 1263437211.5135193, 2039103758.0566826, 119541525.84075928],["1"]],
      [[171.0, 1460.0, 888.4, 514.1558518581695, 853.0, 11920.928955078125,
      17442424058.914185, 4385495722.293854, 7538530505.708111, 49773454.666137695],["2"]],
      [[498.0, 1460.0, 1088.8, 455.13356281425786, 1460.0, 36954.87976074219,
      43512821.197509766, 11164724.826812744, 18679961.749486398, 554561.6149902344],["3"]],
      [[498.0, 1460.0, 1088.8, 455.13356281425786, 1460.0, 30994.415283203125,
      44764995.57495117, 16246497.631072998, 18385686.144529935, 10095000.267028809],["4"]],
      [[566.0, 1460.0, 1281.2, 357.59999999999997, 1460.0, 22172.927856445312,
      21413803.100585938, 5526959.8960876465, 9175244.715715846, 335931.77795410156],["5"]],
      [[69.0, 629.0, 246.6, 205.1522361564699, 172.0, 190973.28186035156,
      457492113.1134033, 219977498.0545044, 220039359.48775682, 211113452.91137695],["6"]],
      [[69.0, 278.0, 137.8, 78.91108920804477, 85.0, 144958.49609375,
      462785959.2437744, 210317969.3222046, 211827960.3342278, 189170479.7744751],["7"]],
      [[302.0, 1460.0, 807.6, 537.1113850962387, 498.0, 12874.603271484375,
      205285072.32666016, 56649982.92922974, 86251862.02980827, 10650992.393493652],["8"]],
      [[266.0, 1460.0, 892.8, 490.89730086852177, 780.0, 24080.276489257812,
      301223039.6270752, 76725006.10351562, 129634012.70485088, 2826452.2552490234],["9"]],
      [[514.0, 1460.0, 1191.6, 371.89762032043177, 1460.0, 10013.580322265625,
      890016.5557861328, 236511.23046875, 377434.79666208074, 23007.39288330078],["10"]],
      "hhh", "exit", "send", "send"]
      class TcpClient(object):
      """Tcp客户端"""
      def __init__(self, IP="127.0.0.1", Port=5002):
      """初始化对象"""
      self.code_mode = "utf-8" #收发数据编码/解码格式
      self.IP = IP
      self.Port = Port
      self.my_socket = socket(AF_INET, SOCK_STREAM) #创建socket
      def run(self):
      """启动"""
      self.my_socket.connect((self.IP, self.Port)) #连接服务器
      tr = Thread(target=self.recv_data) #创建线程收数据
      ts = Thread(target=self.send_data) #创建线程发数据
      tr.start() #开启线程
      ts.start()
      def recv_data(self):
      """收数据"""
      while True:
      data = self.my_socket.recv(BUFFER_SIZE).decode(self.code_mode)
      if data:
      if data == "close connecting":
      print("已下线")
      break
      else:
      print("{}".format(data))
      else:
      break
      self.my_socket.close()
      def send_data(self):
      """发数据"""
      while True:
      # 接收数据
      data = choice(data_list)
      if type(data) == list:
      print("已发送五元组{}的特征".format(data[1][0]))
      else:
      print("发送命令: ", data)
      # 两s发送一次 测试
      time.sleep(2)
      # 判断data的类型 如果是列表用 json传输 字符串就直接传输
      if type(data) == list:
      send_dat = json.dumps(data)
      self.my_socket.sendall(send_dat.encode(self.code_mode))
      elif type(data) == str:
      self.my_socket.sendall(data.encode(self.code_mode))
      else:
      continue
      # 如果是退出 直接推出程序
      if data == "exit":
      break
      def main():
      # 服务器IP和端口
      ip = "127.0.0.1"
      port = 12345
      my_socket = TcpClient(ip, port)
      my_socket.run()
      if __name__ == "__main__":
      main()
      \ No newline at end of file
      measurement/openairinterface5g-enb/oaiflowclassification/multithread_server.py 0 → 100644
      View file @ 4879ea30
      from socket import *
      from threading import Thread
      from pool import *
      import json
      import struct
      class TcpServer(object):
      """Tcp服务器"""
      def __init__(self, Port):
      """初始化对象"""
      self.code_mode = "utf-8" #收发数据编码/解码格式
      self.server_socket = socket(AF_INET, SOCK_STREAM) #创建socket
      self.server_socket.setsockopt(SOL_SOCKET, SO_REUSEADDR, True) #设置端口复用
      self.server_socket.bind((SEVER_HOST, Port)) #绑定IP和Port
      self.server_socket.listen(100) #设置为被动socket
      print("服务器正在监听...")
      def run(self):
      """运行"""
      while True:
      # 判断程序是否结束
      """
      mutex.acquire()
      if len(STOP_CLASSIFY) == 1:
      print("程序结束")
      mutex.release()
      break
      mutex.release()
      """
      client_socket, client_addr = self.server_socket.accept() #等待客户端连接
      print("{} 已上线".format(client_addr))
      #创建线程为客户端服务
      tr = Thread(target=self.recv_data, args=(client_socket, client_addr))
      # if !tr.is_alive():
      tr.start() #开启线程
      self.server_socket.close()
      def recv_data(self, client_socket, client_addr):
      """收发数据"""
      while True:
      # 判断程序是否结束
      """
      mutex.acquire()
      if len(STOP_CLASSIFY) == 1:
      print("程序结束")
      mutex.release()
      break
      mutex.release()
      """
      recv = client_socket.recv(77)
      # a = []
      data = []
      if recv:
      # print('服务端收到客户端发来的消息:%s' % (recv))
      a = struct.unpack('3i1f1i3l1d1l13B', recv)
      #print(a)
      data = [[], []]
      idStr = ""
      mystr = a[10:]
      for i in range(10):
      data[0].append(a[i]+0.0)
      for i in range(8):
      if i % 4 == 3:
      idStr += str(mystr[i])+" "
      else:
      idStr += str(mystr[i]) + "."
      idStr += str(int(mystr[8])*256+int(mystr[9])) + " "
      idStr += str(int(mystr[10])*256+int(mystr[11])) + " "
      if int(mystr[12]) == 6:
      idStr += "TCP"
      else:
      idStr += "UDP"
      data[1].append(idStr)
      # data = client_socket.recv(BUFFER_SIZE).decode(self.code_mode)
      # if len(data) > 8:
      # data = json.loads(data)
      if data:
      # 关闭连接的客户端的线程
      if data == "exit":
      # 发送关闭客户端的命令
      client_socket.send("close connecting".encode(self.code_mode))
      print("{} 已下线".format(client_addr))
      break
      # 如果接收的命令是close 修改标志位 停止程序
      elif data == "close":
      mutex.acquire()
      close_program()
      mutex.release()
      break
      # 如果发送的命令是 send 则将分类结果发送过来
      elif data == "send":
      mutex.acquire()
      if CLASSIFY_RESULT:
      for res in CLASSIFY_RESULT:
      client_socket.send(str(res).encode(self.code_mode))
      CLASSIFY_RESULT.clear()
      else:
      client_socket.send("暂无分类结果".encode(self.code_mode))
      mutex.release()
      else:
      # 将数据保存在PREDECT_FLOWS中 加锁
      mutex.acquire()
      if data not in PREDECT_FLOWS:
      PREDECT_FLOWS.append(data)
      mutex.release()
      print("{}:发送特征的五元组为{}".format(client_addr, data[1]))
      print("缓冲池中有: ", len(PREDECT_FLOWS), " 个待分类的特征，请分类")
      # client_socket.send(data.encode(self.code_mode))
      else:
      #客户端断开连接
      print("{} 已下线".format(client_addr))
      break
      client_socket.close()
      def boot_server():
      # print("\033c", end="") #清屏
      # port = int(input("请输入要绑定的Port:"))
      my_server = TcpServer(SEVER_PORT)
      my_server.run()
      if __name__ == "__main__":
      boot_server()
      measurement/openairinterface5g-enb/oaiflowclassification/oai_client/multithread_client.py 0 → 100644
      View file @ 4879ea30
      from socket import *
      from threading import Thread
      from random import choice
      import json
      import time
      BUFFER_SIZE = 8192
      # 随机发送数据测试的库:
      data_list = [
      [[221.0, 1350.0, 640.0, 376.26798960315506, 543.0, 21257877.349853516,
      4793407917.022705, 1263437211.5135193, 2039103758.0566826, 119541525.84075928],["1"]],
      [[171.0, 1460.0, 888.4, 514.1558518581695, 853.0, 11920.928955078125,
      17442424058.914185, 4385495722.293854, 7538530505.708111, 49773454.666137695],["2"]],
      [[498.0, 1460.0, 1088.8, 455.13356281425786, 1460.0, 36954.87976074219,
      43512821.197509766, 11164724.826812744, 18679961.749486398, 554561.6149902344],["3"]],
      [[498.0, 1460.0, 1088.8, 455.13356281425786, 1460.0, 30994.415283203125,
      44764995.57495117, 16246497.631072998, 18385686.144529935, 10095000.267028809],["4"]],
      [[566.0, 1460.0, 1281.2, 357.59999999999997, 1460.0, 22172.927856445312,
      21413803.100585938, 5526959.8960876465, 9175244.715715846, 335931.77795410156],["5"]],
      [[69.0, 629.0, 246.6, 205.1522361564699, 172.0, 190973.28186035156,
      457492113.1134033, 219977498.0545044, 220039359.48775682, 211113452.91137695],["6"]],
      [[69.0, 278.0, 137.8, 78.91108920804477, 85.0, 144958.49609375,
      462785959.2437744, 210317969.3222046, 211827960.3342278, 189170479.7744751],["7"]],
      [[302.0, 1460.0, 807.6, 537.1113850962387, 498.0, 12874.603271484375,
      205285072.32666016, 56649982.92922974, 86251862.02980827, 10650992.393493652],["8"]],
      [[266.0, 1460.0, 892.8, 490.89730086852177, 780.0, 24080.276489257812,
      301223039.6270752, 76725006.10351562, 129634012.70485088, 2826452.2552490234],["9"]],
      [[514.0, 1460.0, 1191.6, 371.89762032043177, 1460.0, 10013.580322265625,
      890016.5557861328, 236511.23046875, 377434.79666208074, 23007.39288330078],["10"]],
      "hhh", "exit", "send", "send"]
      class TcpClient(object):
      """Tcp客户端"""
      def __init__(self, IP="127.0.0.1", Port=5002):
      """初始化对象"""
      self.code_mode = "utf-8" #收发数据编码/解码格式
      self.IP = IP
      self.Port = Port
      self.my_socket = socket(AF_INET, SOCK_STREAM) #创建socket
      def run(self):
      """启动"""
      self.my_socket.connect((self.IP, self.Port)) #连接服务器
      tr = Thread(target=self.recv_data) #创建线程收数据
      ts = Thread(target=self.send_data) #创建线程发数据
      tr.start() #开启线程
      ts.start()
      def recv_data(self):
      """收数据"""
      while True:
      data = self.my_socket.recv(BUFFER_SIZE).decode(self.code_mode)
      if data:
      if data == "close connecting":
      print("已下线")
      break
      else:
      print("{}".format(data))
      else:
      break
      self.my_socket.close()
      def send_data(self):
      """发数据"""
      while True:
      # 接收数据
      data = choice(data_list)
      if type(data) == list:
      print("已发送五元组{}的特征".format(data[1][0]))
      else:
      print("发送命令: ", data)
      # 两s发送一次 测试
      time.sleep(2)
      # 判断data的类型 如果是列表用 json传输 字符串就直接传输
      if type(data) == list:
      send_dat = json.dumps(data)
      self.my_socket.sendall(send_dat.encode(self.code_mode))
      elif type(data) == str:
      self.my_socket.sendall(data.encode(self.code_mode))
      else:
      continue
      # 如果是退出 直接推出程序
      if data == "exit":
      break
      def main():
      # 服务器IP和端口
      ip = "127.0.0.1"
      port = 12345
      my_socket = TcpClient(ip, port)
      my_socket.run()
      if __name__ == "__main__":
      main()
      \ No newline at end of file
      measurement/openairinterface5g-enb/oaiflowclassification/path_utils.py 0 → 100644
      View file @ 4879ea30
      from pathlib import Path
      import os.path
      def get_prj_root():
      return os.path.abspath(os.curdir)
      # return Path(__file__).parent
      if __name__ == '__main__':
      print(get_prj_root())
      measurement/openairinterface5g-enb/oaiflowclassification/pool.py 0 → 100644
      View file @ 4879ea30
      import threading
      """
      该文件定义使用的常量 和 进程锁
      """
      # 设置服务器的IP和端口
      SEVER_HOST = "127.0.0.1" # 发送到windows测试
      SEVER_PORT = 12345
      # 缓冲区大小
      BUFFER_SIZE = 8192
      # 共享的全局变量 保存解析的特征 用于客户端发送和服务器端读取的缓冲区
      PREDECT_FLOWS = []
      # 共享的全局变量 保存分类的结果
      CLASSIFY_RESULT = []
      # 是否停止分类的标志位 长度为1退出程序
      STOP_CLASSIFY = []
      # 定义修改全局变量的函数
      def close_program():
      global STOP_CLASSIFY
      STOP_CLASSIFY.append(1)
      # 创建全局的进程锁
      mutex = threading.Lock()
      # 测试使用的数据
      test = [[221.0, 1350.0, 640.0, 376.26798960315506, 543.0,
      21257877.349853516, 4793407917.022705, 1263437211.5135193,
      2039103758.0566826, 119541525.84075928],["sip sport dip dport protocol"]]
      measurement/openairinterface5g-enb/oaiflowclassification/predict.py 0 → 100644
      View file @ 4879ea30
      """
      预测函数：随机森林
      每次运行前，检查：
      四个需要修改的地方，命名是否正确
      最后的运行模式是否正确
      """
      from common_utils import * # 修改了
      from argparse import ArgumentParser
      from collections import namedtuple
      from typing import List, Dict
      from datetime import datetime
      from path_utils import get_prj_root
      import numpy as np
      # from sklearn.externals import joblib
      import joblib
      from sklearn.ensemble import RandomForestClassifier # 训练模型
      random.seed(datetime.now())
      model_dir = os.path.join(get_prj_root(), "classify/models") # 修改：模型model文件夹路径
      predict_model_pkl = os.path.join(model_dir, "dt2.pkl") # 修改：模型的版本，只用修改此处就行
      Instance = namedtuple("Instance", ["features", "label"]) # 实例
      dirs = {
      "video": "./tmp/dt/video",
      "iot": "./tmp/dt/iot",
      "voip": "./tmp/dt/voip",
      "AR": "./tmp/dt/AR",
      }
      instances_dir = os.path.join(get_prj_root(), "classify/instances") # 修改：instances路径
      def train_and_predict():
      iot = load_pkl(os.path.join(instances_dir, "iot.pkl")) # 不同实例的pkl是不同特征的
      videos = load_pkl(os.path.join(instances_dir, "video.pkl"))
      voip = load_pkl(os.path.join(instances_dir, "voip.pkl"))
      AR = load_pkl(os.path.join(instances_dir, "AR.pkl"))
      for i in videos:
      assert i.label == 0
      for i in iot:
      assert i.label == 1
      for i in voip:
      assert i.label == 2
      for i in AR:
      assert i.label == 3
      # print(videos)
      debug("# iot instances {}".format(len(iot)))
      debug("# video instances {}".format(len(videos)))
      debug("# VOIP instances {}".format(len(voip)))
      debug("# AR instances {}".format(len(AR)))
      random.shuffle(voip) # 打乱排序
      random.shuffle(iot)
      random.shuffle(videos)
      random.shuffle(AR)
      n_video_train = int(len(videos) * 0.7)
      n_video_test = len(videos) - n_video_train
      video_train = videos[:n_video_train]
      video_test = videos[n_video_train:]
      iot_train = iot[:n_video_train]
      iot_test = iot[len(iot) - len(video_test):]
      voip_train = voip[:n_video_train]
      voip_test = voip[len(voip) - len(video_test):]
      AR_train = AR[:n_video_train]
      AR_test = AR[len(AR) - len(video_test):]
      info("#video train {}".format(len(video_train)))
      info("#iot train {}".format(len(iot_train)))
      info("#voip train {}".format(len(voip_train)))
      info("#AR train {}".format(len(AR_train)))
      train = []
      train.extend(iot_train)
      train.extend(video_train)
      train.extend(voip_train)
      train.extend(AR_train)
      random.shuffle(train)
      train_x = [x.features for x in train]
      train_y = [x.label for x in train]
      # test 1:1
      test = []
      info("#video test {}".format(len(video_test)))
      info("#iot test {}".format(len(iot_test)))
      info("#voip test {}".format(len(voip_test)))
      info("#AR test {}".format(len(AR_test)))
      test.extend(video_test)
      test.extend(iot_test)
      test.extend(voip_test)
      test.extend(AR_test)
      random.shuffle(test)
      test_x = [t.features for t in test]
      test_y = [t.label for t in test]
      # 训练以及预测
      predict_model = RandomForestClassifier(oob_score=True) # 引入训练方法
      predict_model.fit(train_x, train_y) # 队训练数据进行拟合
      predicts = predict_model.predict(test_x)
      """
      dt = DT()
      dt.fit((train_x, train_y))
      predicts = dt.predict(test_x)
      """
      """
      # 打印预测的结果
      print(predicts)
      print("-------------------------------")
      """
      # 保存模型
      fn_name = os.path.join(model_dir, predict_model_pkl)
      joblib.dump(predict_model, predict_model_pkl)
      # 评价模型
      count = 0
      for idx in range(len(test_x)):
      if int(predicts[idx]) == int(test_y[idx]):
      count += 1
      # print(count / len(test_x))
      return count / len(test_x)
      # save_pkl(predict_model_pkl, knn.model)
      # knn.save_model(dt_model_pkl) #储存模型
      if __name__ == '__main__':
      n = 10
      s = 0
      predict_sum = 0
      for i in range(n):
      s = train_and_predict()
      predict_sum = predict_sum + s
      print(predict_sum / n)
      measurement/openairinterface5g-enb/oaiflowclassification/process_bar.py 0 → 100644
      View file @ 4879ea30
      #!/usr/bin/env python
      # -*- coding: utf-8 -*-
      # @Time : 18-5-21 下午3:44
      # @Author : LK
      # @File : 进度条.py
      # @Software: PyCharm
      import sys
      import time
      def process_bar(precent, width=50):
      use_num = int(precent*width)
      space_num = int(width-use_num)
      precent = precent*100
      # 第一个和最后一个一样梯形显示, 中间两个正确,但是在python2中报错
      #
      # print('[%s%s]%d%%'%(use_num*'#', space_num*' ',precent))
      # print('[%s%s]%d%%'%(use_num*'#', space_num*' ',precent), end='\r')
      print('[%s%s]%d%%'%(use_num*'#', space_num*' ',precent),file=sys.stdout,flush=True, end='\r')
      # print('[%s%s]%d%%'%(use_num*'#', space_num*' ',precent),file=sys.stdout,flush=True)
      # for i in range(21):
      # precent = i/20
      # process_bar(precent)
      # time.sleep(0.2)
      measurement/openairinterface5g-enb/oaiflowclassification/train.py 0 → 100644
      View file @ 4879ea30
      """
      10个特征的（前五个包大小，后五个包间隔）：最小值，最大值，平均值，方差，中位数
      每次运行前，检查：
      四个需要修改的地方，命名是否正确
      最后的运行模式是否正确
      这个文件用于把pcap解析的文件生成特征和标签的形式 并且5个包一组
      """
      from common_utils import * # 修改了
      from argparse import ArgumentParser
      from collections import namedtuple
      from typing import List, Dict
      from path_utils import get_prj_root
      from model import DT # 修改了
      from datetime import datetime
      from path_utils import get_prj_root
      import numpy as np
      from sklearn.decomposition import PCA
      import time
      # start counting time
      start = time.time()
      random.seed(datetime.now())
      # get the path of the models
      model_dir = os.path.join(get_prj_root(), "classify/models") # 修改：模型models路径
      dt_model_pkl = os.path.join(model_dir, "dt2_9.pkl") # 修改：模型的版本，只用修改此处就行
      Instance = namedtuple("Instance", ["features", "label"]) # 实例
      win_size = 5 # 窗口大小
      limit = 100000
      # the path
      # iot-物联网流 video-视频流 voip-音频流 AR-AR流用的高清视频流代替
      dirs = {
      "video": "./tmp/dt/video",
      "iot": "./tmp/dt/iot",
      "voip": "./tmp/dt/voip",
      "AR": "./tmp/dt/AR",
      }
      instances_dir = os.path.join(get_prj_root(), "classify/instances") # 修改：instances路径
      # 获取特征
      def get_median(data): # 产生中位数
      data.sort()
      half = len(data) // 2
      return (data[half] + data[~half]) / 2
      def gen_single_instance(dir_name, flow, flow_type):
      # debug("generate {}".format(flow["file"]))
      def extract_features(raw_features: List[float]): # 修改特征
      extracted_features = []
      raw_features = [r for r in raw_features if int(r) >= 0]
      extracted_features.append(min(raw_features))
      extracted_features.append(max(raw_features))
      extracted_features.append(sum(raw_features) / len(raw_features))
      extracted_features.append(np.std(raw_features)) # 标准差
      extracted_features.append(get_median(raw_features)) # 中位数
      return extracted_features
      features = []
      idts = []
      ps = []
      idt_file = os.path.join(dir_name, flow["idt"]) # 包大小
      ps_file = os.path.join(dir_name, flow["ps"]) # 包间隔
      with open(idt_file, 'r') as fp:
      lines = fp.readlines()
      fp.close()
      lines = [l.strip() for l in lines] # .strip()用于移除字符串头尾指定的字符（默认为空格或换行符）或字符序列。
      lines = [l for l in lines if len(l) > -1]
      if len(lines) > win_size:
      lines = lines[:win_size]
      for l in lines:
      idts.append(float(l))
      with open(ps_file, "r") as fp:
      lines = fp.readlines()
      fp.close()
      lines = [l.strip() for l in lines]
      lines = [l for l in lines if len(l) > 0]
      if len(lines) > win_size:
      lines = lines[:win_size]
      for l in lines:
      ps.append(float(l))
      # 有很奇怪的现象
      ps = [p for p in ps if p > 0]
      if len(ps) == 0:
      print(flow["ps"])
      return None
      idts = [i for i in idts if i >= 0]
      if len(idts) == 0:
      return None
      features.extend(extract_features(ps)) # 包间隔的数理统计
      features.extend(extract_features(idts)) # 包大小的数理统计
      if flow_type == "video":
      label = 0
      elif flow_type == "iot":
      label = 1
      elif flow_type == "voip":
      label = 2
      elif flow_type == "AR":
      label = 3
      else:
      err("Unsupported flow type")
      raise Exception("Unsupported flow type")
      return Instance(features=features, label=label)
      def generate():
      instances_dir = os.path.join(get_prj_root(), "classify/instances") # 修改：instances_dir实例的路径
      for flow_type, dirname in dirs.items():
      stats_fn = os.path.join(dirname, "statistics.json") # statistics.json流量统计的文件
      debug(stats_fn)
      statistics = load_json(os.path.join(dirname, "statistics.json"))
      debug("#flows {}".format(statistics["count"]))
      flows: List = statistics["flows"]
      sorted(flows, key=lambda f: -f["num_pkt"])
      if len(flows) > limit:
      flows = flows[:limit]
      instances = [gen_single_instance(dirname, f, flow_type) for f in flows]
      instances = [i for i in instances if i is not None]
      debug("#{} instances {}".format(flow_type, len(instances)))
      # print(len(instances))
      save_pkl(os.path.join(instances_dir, "{}.pkl".format(flow_type)), instances) # 保存Python内存数据到文件
      if __name__ == '__main__':
      parser = ArgumentParser()
      print("running mode\n"
      "1. generate instances\n"
      "2. train dt\n")
      parser.add_argument("--mode", type=int, default=1) # default为模式修改
      args = parser.parse_args()
      mode = int(args.mode)
      if mode == 1:
      generate()
      end = time.time()
      print("程序运行时间:%.2f秒" % (end - start))
      measurement/openairinterface5g-enb/openairinterface5g/.gitignore 0 → 100644
      View file @ 4879ea30
      # vim swp
      *.swp
      # log and exec file
      cmake_targets/log/
      cmake_targets/*/build/
      cmake_targets/ran_build/
      cmake_targets/nas_sim_tools/build/
      log/
      lte_build_oai/
      targets/bin/
      # vscode
      .vscode
      # Tags for vim/global
      GPATH
      GRTAGS
      GTAGS
      tags
      measurement/openairinterface5g-enb/openairinterface5g/.idea/.gitignore 0 → 100644
      View file @ 4879ea30
      # Default ignored files
      /shelf/
      /workspace.xml
      # Datasource local storage ignored files
      /dataSources/
      /dataSources.local.xml
      # Editor-based HTTP Client requests
      /httpRequests/
      measurement/openairinterface5g-enb/openairinterface5g/.idea/inspectionProfiles/Project_Default.xml 0 → 100644
      View file @ 4879ea30
      <component name="InspectionProjectProfileManager">
      <profile version="1.0">
      <option name="myName" value="Project Default" />
      <inspection_tool class="PyPep8Inspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
      <option name="ignoredErrors">
      <list>
      <option value="E722" />
      <option value="W191" />
      </list>
      </option>
      </inspection_tool>
      <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
      <option name="ignoredErrors">
      <list>
      <option value="N802" />
      <option value="N803" />
      <option value="N806" />
      </list>
      </option>
      </inspection_tool>
      </profile>
      </component>
      \ No newline at end of file
      measurement/openairinterface5g-enb/openairinterface5g/.idea/inspectionProfiles/profiles_settings.xml 0 → 100644
      View file @ 4879ea30
      <component name="InspectionProjectProfileManager">
      <settings>
      <option name="USE_PROJECT_PROFILE" value="false" />
      <version value="1.0" />
      </settings>
      </component>
      \ No newline at end of file
      measurement/openairinterface5g-enb/openairinterface5g/.idea/misc.xml 0 → 100644
      View file @ 4879ea30
      <?xml version="1.0" encoding="UTF-8"?>
      <project version="4">
      <component name="JavaScriptSettings">
      <option name="languageLevel" value="ES6" />
      </component>
      <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8" project-jdk-type="Python SDK" />
      </project>
      \ No newline at end of file
      measurement/openairinterface5g-enb/openairinterface5g/.idea/modules.xml 0 → 100644
      View file @ 4879ea30
      <?xml version="1.0" encoding="UTF-8"?>
      <project version="4">
      <component name="ProjectModuleManager">
      <modules>
      <module fileurl="file://$PROJECT_DIR$/.idea/openairinterface5g.iml" filepath="$PROJECT_DIR$/.idea/openairinterface5g.iml" />
      </modules>
      </component>
      </project>
      \ No newline at end of file
      measurement/openairinterface5g-enb/openairinterface5g/.idea/openairinterface5g.iml 0 → 100644
      View file @ 4879ea30
      <?xml version="1.0" encoding="UTF-8"?>
      <module type="PYTHON_MODULE" version="4">
      <component name="NewModuleRootManager">
      <content url="file://$MODULE_DIR$" />
      <orderEntry type="inheritedJdk" />
      <orderEntry type="sourceFolder" forTests="false" />
      </component>
      <component name="PyDocumentationSettings">
      <option name="format" value="PLAIN" />
      <option name="myDocStringFormat" value="Plain" />
      </component>
      <component name="TemplatesService">
      <option name="TEMPLATE_CONFIGURATION" value="Jinja2" />
      <option name="TEMPLATE_FOLDERS">
      <list>
      <option value="$MODULE_DIR$/cmake_targets/autotests/templates" />
      </list>
      </option>
      </component>
      </module>
      \ No newline at end of file
      measurement/openairinterface5g-enb/openairinterface5g/.idea/vcs.xml 0 → 100644
      View file @ 4879ea30
      <?xml version="1.0" encoding="UTF-8"?>
      <project version="4">
      <component name="VcsDirectoryMappings">
      <mapping directory="$PROJECT_DIR$" vcs="Git" />
      </component>
      </project>
      \ No newline at end of file
      measurement/openairinterface5g-enb/openairinterface5g/CHANGELOG.md 0 → 100644
      View file @ 4879ea30
      # RELEASE NOTES: #
      ## [v1.2.1](https://gitlab.eurecom.fr/oai/openairinterface5g/-/tags/v1.2.1) -> February 2020. ##
      * Bug fix for mutex lock for wake-up signal
      ## [v1.2.0](https://gitlab.eurecom.fr/oai/openairinterface5g/-/tags/v1.2.0) -> January 2020. ##
      This version adds the following implemented features:
      * LTE-M : eNB support for Mode A repetitions
      - PUSCH CE - 8 Repetitions
      * Improved CDRX implementation for monolithic eNB
      * Experimental eMBMS support (now also on eNB side)
      * Experimental MCE - Multicast Coordination Entity
      * Bug fixes
      This version also has an improved code quality:
      * Better Test Coverage in Continuous Integration:
      - Initial framework to do long-run testing at R2LAB
      ## [v1.1.1](https://gitlab.eurecom.fr/oai/openairinterface5g/-/tags/v1.1.1) -> November 2019. ##
      - Bug fix in the TDD Fair Round-Robin scheduler
      ## [v1.1.0](https://gitlab.eurecom.fr/oai/openairinterface5g/-/tags/v1.1.0) -> July 2019. ##
      This version adds the following implemented features:
      * Experimental support of LTE-M
      - Single LTE-M UE attachment, legacy-LTE UE attachment is disabled
      * X2 interface and handover (also X2-U interface)
      - In FDD and TDD
      * CU/DU split (F1 interface)
      - Tested only in FDD
      * CDRX
      - Tested only in FDD
      * Experimental eMBMS support (only on UE side)
      * Experimental multi-RRU support
      - Tested only in TDD
      This version has an improved code quality:
      * Simplification of the Build System
      - A single build includes all full-stack simulators, S1/noS1 modes and one HW platform (such as USRP, BladeRF, ...)
      * TUN interface is now used as default for the data plane
      - for UE, eNB-noS1 and UE-noS1
      * Code Cleanup
      * Better Static Code Analysis:
      - Limited number of errors in cppcheck
      - Important Decrease on high Impact errors in CoverityScan
      * Better Test Coverage in Continuous Integration:
      - TM2, CDRX, IF4.5, F1
      - OAI UE is tested in S1 and noS1 modes with USRP board
      - Multi-RRU TDD mode
      - X2 Handover in FDD mode
      ## [v1.0.3](https://gitlab.eurecom.fr/oai/openairinterface5g/-/tags/v1.0.3) -> June 2019. ##
      - Bug fix for LimeSuite v19.04.0 API
      ## [v1.0.2](https://gitlab.eurecom.fr/oai/openairinterface5g/-/tags/v1.0.2) -> February 2019. ##
      - Full OAI support for 3.13.1 UHD
      ## [v1.0.1](https://gitlab.eurecom.fr/oai/openairinterface5g/-/tags/v1.0.1) -> February 2019. ##
      - Bug fix for the UE L1 simulator.
      ## [v1.0.0](https://gitlab.eurecom.fr/oai/openairinterface5g/-/tags/v1.0.0) -> January 2019. ##
      This version first implements the architectural split described in the following picture.
      ![Block Diagram](./doc/images/oai_lte_enb_func_split_arch.png)
      * Only FAPI, nFAPI and IF4.5 interfaces are implemented.
      * Repository tree structure prepares future integrations of features such as LTE-M, nbIOT or 5G-NR.
      * Preliminary X2 support has been implemented.
      * S1-flex has been introduced.
      * New tools: config library, telnet server, ...
      * A lot of bugfixes and a proper automated Continuous Integration process validates contributions.
      Old Releases:
      * v0.6.1 -> Mostly bugfixes. This is the last version without NFAPI.
      * v0.6 -> RRH functionality, UE greatly improved, better TDD support, a lot of bugs fixed.
      - WARNING: oaisim in PHY abstraction mode does not work, you need to use v0.5.2 for that.
      * v0.5.2 -> Last version with old code for oaisim (abstraction mode works)
      * v0.5.1 -> Merge of bugfix-137-uplink-fixes. It includes stablity fixes for eNB
      * v0.5 -> Merge of enhancement-10-harmony-lts. It includes fixes for Ubuntu 16.04 support
      * v0.4 -> Merge of feature-131-new-license. It closes issue#131 and changes the license to OAI Public License V1.0
      * v0.3 -> Last stable commit on develop branch before the merge of feature-131-new-license. This is the last commit with GPL License
      * v0.2 -> Merge of enhancement-10-harmony to include NGFI RRH + New Interface for RF/BBU
      * v0.1 -> Last stable commit on develop branch before enhancement-10-harmony
      measurement/openairinterface5g-enb/openairinterface5g/CONTRIBUTING.md 0 → 100644
      View file @ 4879ea30
      # Contributing to OpenAirInterface #
      We want to make contributing to this project as easy and transparent as possible.
      1. Sign and return a Contributor License Agreement to OAI team.
      2. We recommend that you provide us with a professional or student email address
      2. Register on [Eurecom GitLab Server](https://gitlab.eurecom.fr/users/sign_in)
      3. Provide the OAI team with the **username** of this account to (mailto:contact@openairinterface.org) ; we will give you the developer rights on this repository.
      4. The policies are described in these wiki pages: [OAI Policies](https://gitlab.eurecom.fr/oai/openairinterface5g/wikis/oai-policies-home)
      - PLEASE DO NOT FORK the OAI repository on your own Eurecom GitLab account. It just eats up space on our servers.
      - You can fork onto another hosting system. But we will NOT accept a merge request from a forked repository.
      * This decision was made for the license reasons.
      * The Continuous Integration will reject your merge request.
      - All merge requests SHALL have `develop` branch as target branch.
      ## Coding Styles ##
      There are described [here](https://gitlab.eurecom.fr/oai/openairinterface5g/wikis/guidelines/guidelines-home)
      ## License ##
      By contributing to OpenAirInterface, you agree that your contributions will be licensed under the LICENSE file in the root directory of this source tree.
      measurement/openairinterface5g-enb/openairinterface5g/LICENSE 0 → 100644
      View file @ 4879ea30
      This diff is collapsed.
      measurement/openairinterface5g-enb/openairinterface5g/NOTICE.md 0 → 100644
      View file @ 4879ea30
      The source code of openairinterface5g is distributed under **OAI Public License V1.1**.
      For more details of the license, refer to [LICENSE](LICENSE) file in the same directory.
      However, the source code also contains third party software that is acknowledged here for reference.
      ## Credits for LFDS user space source code located in folder openair2/UTILS/LFDS/ ##
      See on [liblfds website](https://liblfds.org/) the license section.
      <pre>
      "There is no license. You are free to use this software in any way, for any purpose. Go forth and create wealth!
      If however for legal reasons a licence is required, the license of your choice will be granted."
      </pre>
      ## Credits for source code common/utils/collection/queue.h: ##
      The Regents of the University of California: BSD 3-Clause Licence.
      ## Credits for source code common/utils/collection/tree.h: ##
      Niels Provos <provos@citi.umich.edu>: BSD 2-Clause Licence.
      ## Credits for source code openair3/GTPV1-U/nw-gtpv1u: ##
      Amit Chawre <http://www.amitchawre.net/contact.html>: BSD 2-Clause Licence.
      measurement/openairinterface5g-enb/openairinterface5g/README.md 0 → 100644
      View file @ 4879ea30
      # OpenAirInterface License #
      OpenAirInterface is under OpenAirInterface Software Alliance license.
      * [OAI License Model](http://www.openairinterface.org/?page_id=101)
      * [OAI License v1.1 on our website](http://www.openairinterface.org/?page_id=698)
      It is distributed under **OAI Public License V1.1**.
      The license information is distributed under [LICENSE](LICENSE) file in the same directory.
      Please see [NOTICE](NOTICE.md) file for third party software that is included in the sources.
      # Where to Start #
      * [The implemented features](./doc/FEATURE_SET.md)
      * [How to build](./doc/BUILD.md)
      * [How to run the modems](./doc/RUNMODEM.md)
      # RAN repository structure #
      The OpenAirInterface (OAI) software is composed of the following parts:
      <pre>
      openairinterface5g
      ├── ci-scripts : Meta-scripts used by the OSA CI process. Contains also configuration files used day-to-day by CI.
      ├── cmake_targets : Build utilities to compile (simulation, emulation and real-time platforms), and generated build files.
      ├── common : Some common OAI utilities, other tools can be found at openair2/UTILS.
      ├── doc : Contains an up-to-date feature set list and starting tutorials.
      ├── executables : Top-level executable source files.
      ├── LICENSE : License file.
      ├── maketags : Script to generate emacs tags.
      ├── nfapi : Contains the NFAPI code. A local Readme file provides more details.
      ├── openair1 : 3GPP LTE Rel-10/12 PHY layer / 3GPP NR Rel-15 layer. A local Readme file provides more details.
      │   ├── PHY
      │   ├── SCHED
      │   ├── SCHED_NBIOT
      │   ├── SCHED_NR
      │   ├── SCHED_NR_UE
      │   ├── SCHED_UE
      │   └── SIMULATION : PHY RF simulation.
      ├── openair2 : 3GPP LTE Rel-10 RLC/MAC/PDCP/RRC/X2AP + LTE Rel-14 M2AP implementation. Also 3GPP NR Rel-15 RLC/MAC/PDCP/RRC/X2AP.
      │   ├── COMMON
      │   ├── DOCS
      │   ├── ENB_APP
      │   ├── F1AP
      │   ├── GNB_APP
      │   ├── LAYER2/RLC/ : with the following subdirectories: UM_v9.3.0, TM_v9.3.0, and AM_v9.3.0.
      │   ├── LAYER2/PDCP/PDCP_v10.1.0
      │   ├── M2AP
      │   ├── MCE_APP
      │   ├── NETWORK_DRIVER
      │   ├── NR_PHY_INTERFACE
      │   ├── NR_UE_PHY_INTERFACE
      │   ├── PHY_INTERFACE
      │   ├── RRC
      │   ├── UTIL
      │   └── X2AP
      ├── openair3 : 3GPP LTE Rel10 for S1AP, NAS GTPV1-U for both ENB and UE.
      │   ├── COMMON
      │   ├── DOCS
      │   ├── GTPV1-U
      │   ├── M3AP
      │   ├── MME_APP
      │   ├── NAS
      │   ├── S1AP
      │   ├── SCTP
      │   ├── SECU
      │   ├── TEST
      │   ├── UDP
      │   └── UTILS
      └── targets : Top-level wrappers for unitary simulation for PHY channels, system-level emulation (eNB-UE with and without S1), and realtime eNB and UE and RRH GW.
      </pre>
      measurement/openairinterface5g-enb/openairinterface5g/ci-scripts/.gitignore 0 → 100644
      View file @ 4879ea30
      r*.raw
      enb_*.log
      ue_*.log
      ping_*.*
      iperf_*.*
      phones_list.txt
      modules_list.txt
      test_results*.html
      pMain*
      __pycache__
      measurement/openairinterface5g-enb/openairinterface5g/ci-scripts/active_net_interfaces.awk 0 → 100644
      View file @ 4879ea30
      #/*
      # * Licensed to the OpenAirInterface (OAI) Software Alliance under one or more
      # * contributor license agreements. See the NOTICE file distributed with
      # * this work for additional information regarding copyright ownership.
      # * The OpenAirInterface Software Alliance licenses this file to You under
      # * the OAI Public License, Version 1.1 (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.openairinterface.org/?page_id=698
      # *
      # * 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.
      # *-------------------------------------------------------------------------------
      # * For more information about the OpenAirInterface (OAI) Software Alliance:
      # * contact@openairinterface.org
      # */
      /inet.*brd/{print "interfaceToUse="$NF"done"}
      measurement/openairinterface5g-enb/openairinterface5g/ci-scripts/add_user_to_conf_file.awk 0 → 100644
      View file @ 4879ea30
      BEGIN{lineIdx=0;captureUEDesc=0}
      {
      if ($0 ~/UE0/) {
      captureUEDesc = 1
      }
      if (captureUEDesc == 1) {
      captureLine[lineIdx] = $0
      lineIdx = lineIdx + 1
      }
      print $0
      }
      END {
      for (ueIdx = 1; ueIdx < num_ues; ueIdx++) {
      print ""
      for (k = 0; k < lineIdx; k++) {
      if (captureLine[k] ~/UE0/) {
      mLine = captureLine[k]
      gsub("UE0", "UE"ueIdx, mLine)
      print mLine
      } else {
      if (captureLine[k] ~/MSIN=/) {
      mLine = captureLine[k]
      MSIN=sprintf("%08d", 1111+int(ueIdx))
      gsub("00001111", MSIN, mLine)
      print mLine
      } else {
      print captureLine[k]
      }
      }
      }
      }
      }
      measurement/openairinterface5g-enb/openairinterface5g/maketags 0 → 100644
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      measurement/openairinterface5g-enb/openairinterface5g/oaienv 0 → 100644
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      This diff is collapsed.
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