frameworks

GEOEnv.py

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+import numpy as np
+from GEO_parameters import GEO_Parameters
+import torch
+
+
+class GEOEnv:
+    def __init__(self, num_satellite = 1, num_carrier_wave = 100, num_beam = 1, num_user_dim = 32, delt_t =5, dataset = "month_2.txt", max_intervals = 8):
+        self.action_space = num_carrier_wave
+        self.user_space_dim = num_user_dim
+        self.user_space = num_user_dim * num_user_dim
+        self.delt_t =  delt_t
+        self.beams = num_beam
+        self.satellite = num_satellite
+        self.steps_before_termination = 0
+        self.datas = []
+        self.dataset = dataset
+        self.num_data = len(self.datas)
+        self.state = np.zeros((self.action_space+1, self.user_space_dim, self.user_space_dim), dtype=np.uint8)
+        self.max_intervals = 8
+
+    def load_data(self):
+        with open(self.dataset, 'r') as f:
+            self.datas = f.readlines()
+            self.num_data = len(self.datas)
+
+    def compute_state(self, index):
+        # state = self.state.copy()
+        self.state[-1] = np.zeros_like(self.state[-1])
+        [date_t, beam_type, user, ap] = self.datas[index].split('\t')
+        user = int(user)
+        ap = int(ap)
+        beam_type = int(beam_type)
+        i = user//10
+        j = user%10
+        t = 0
+        if ap == 0:
+            t = beam_type
+        else:
+            t = -beam_type
+        self.state[-1][i][j] = t
+        return self.state
+
+    def compute_reward(self, action):
+        assert type(action) == int
+        assert action >= 0 and action < self.action_space
+        # 功率暂时不用
+
+        # 频谱效率
+        axis2_sum = np.sum(self.state, axis= 2)
+        matrix_sum = np.sum(axis2_sum, axis=1)
+        if (matrix_sum)
+        t = 0.0
+        for i in range(self.action_space):
+            if matrix_sum[i] == 0:
+                t += 1/self.action_space
+            else:
+                t += 1/self.action_space * matrix_sum[i]/self.max_intervals
+        # 用户满意率，即action所在的信道的最大可用带宽的满足程度
+        b = 0.0
+        acquire = matrix_sum[-1]
+        supply = self.max_intervals - matrix_sum[action]
+        b = min(1.0, acquire/supply)
+        # 如果用户被拒绝了会有一个非常大的惩罚
+        if abs(b) < 1e-7 or supply <= 0:
+            b = -1.0
+
+        # 载波共用的惩罚
+        c:float = 0
+        # 暂时不动
+        return t + b
+
+
+def renew_environment():
+    pass
+def step(self, action):
+    self.state = self.compute_state(self.steps_before_termination)
+    reward = self.compute_reward(action)
+
+    # self.pre
+
+
+
+
+if __name__ == "__main__":
+    e = GEOEnv()
+    e.load_data()
+    print(len(e.datas))
+    state = e.compute_state(0)
+    print(state)
+    state[1][1][2] = 4
+    t = e.compute_reward(state, 1)
+    print(t)
+
+