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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import gym
import tensorflow as tf
import tf_slim as slim
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import json
from tensorflow import keras
from ns3gym import ns3env
import gym
from gym import spaces
import numpy as np
class AntiJamEnv(gym.Env):
def __init__(self, jammer_type, channel_switching_cost):
super(AntiJamEnv, self).__init__()
self.observation = None
self.jammer_frequency = None
self.jammer_mode = None
self.current_channel = None
self.num_channels = 8
self.channel_bandwidth = 20 # MHz
self.frequency_range = [5180, 5320] # MHz
self.frequency_lists = range(5180, 5340, 20) # MHz
self.observation_space = spaces.Box(low=-30, high=40, shape=(self.num_channels,), dtype=np.float32)
self.action_space = spaces.Discrete(self.num_channels)
self.jammer_type = jammer_type
self.jammer_modes = ['constant', 'random', 'sweeping']
self.csc = channel_switching_cost
self._max_episode_steps = None
def reset(self):
self.current_channel = np.random.randint(self.num_channels)
if self.jammer_type == 'dynamic':
self.jammer_mode = np.random.choice(self.jammer_modes)
else:
self.jammer_mode = self.jammer_type
self.jammer_frequency = self.frequency_lists[self.current_channel]
self.observation = np.array([self._get_received_power(i) for i in range(self.num_channels)])
return self.observation
def step(self, action):
assert self.action_space.contains(action), "Invalid action"
received_power = self._get_received_power(action)
if received_power >= 0:
reward = 0.0
else:
reward = 1.0
if self.current_channel != action:
reward -= self.csc # Channel switching cost
self.current_channel = action
if self.jammer_mode == 'random':
self.jammer_frequency = np.random.uniform(self.frequency_range[0], self.frequency_range[1])
elif self.jammer_mode == 'sweeping':
self.jammer_frequency += self.channel_bandwidth
if self.jammer_frequency > self.frequency_range[1]:
self.jammer_frequency = self.frequency_range[0]
self.observation = np.array([self._get_received_power(i) for i in range(self.num_channels)])
return self.observation, reward, False, {}
def _get_received_power(self, channel_idx):
# Simulate received jamming power using normal distribution
jammed_power = np.random.normal(loc=30, scale=5)
adjacent_power = np.random.normal(loc=13, scale=3)
far_away_power = np.random.normal(loc=-7, scale=1)
if channel_idx == self.current_channel:
return jammed_power
elif abs(channel_idx - self.current_channel) == 1:
return adjacent_power
else:
return far_away_power
def render(self, mode='human'):
pass
def close(self):
pass |