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mainPyTorch.py

+#  #################################################################
+#  Deep Reinforcement Learning for Online Offloading in Wireless Powered Mobile-Edge Computing Networks
+#
+#  This file contains the main code of DROO. It loads the training samples saved in ./data/data_#.mat, splits the samples into two parts (training and testing data constitutes 80% and 20%), trains the DNN with training and validation samples, and finally tests the DNN with test data.
+#
+#  Input: ./data/data_#.mat
+#    Data samples are generated according to the CD method presented in [2]. There are 30,000 samples saved in each ./data/data_#.mat, where # is the user number. Each data sample includes
+#  -----------------------------------------------------------------
+#  |       wireless channel gain           |    input_h            |
+#  -----------------------------------------------------------------
+#  |       computing mode selection        |    output_mode        |
+#  -----------------------------------------------------------------
+#  |       energy broadcasting parameter   |    output_a           |
+#  -----------------------------------------------------------------
+#  |     transmit time of wireless device  |    output_tau         |
+#  -----------------------------------------------------------------
+#  |      weighted sum computation rate    |    output_obj         |
+#  -----------------------------------------------------------------
+#
+#
+#  References:
+#  [1] 1. Liang Huang, Suzhi Bi, and Ying-Jun Angela Zhang, "Deep Reinforcement Learning for Online Offloading in Wireless Powered Mobile-Edge Computing Networks," in IEEE Transactions on Mobile Computing, early access, 2019, DOI:10.1109/TMC.2019.2928811.
+#  [2] S. Bi and Y. J. Zhang, “Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading,” IEEE Trans. Wireless Commun., vol. 17, no. 6, pp. 4177-4190, Jun. 2018.
+#
+# version 1.0 -- July 2018. Written by Liang Huang (lianghuang AT zjut.edu.cn)
+#  #################################################################
+
+
+import scipy.io as sio                     # import scipy.io for .mat file I/
+import numpy as np                         # import numpy
+
+# Implementated based on the PyTorch 
+from memoryPyTorch import MemoryDNN
+from optimization import bisection
+
+import time
+
+
+def plot_rate(rate_his, rolling_intv=50):
+    import matplotlib.pyplot as plt
+    import pandas as pd
+    import matplotlib as mpl
+
+    rate_array = np.asarray(rate_his)
+    df = pd.DataFrame(rate_his)
+
+
+    mpl.style.use('seaborn')
+    fig, ax = plt.subplots(figsize=(15, 8))
+#    rolling_intv = 20
+
+    plt.plot(np.arange(len(rate_array))+1, df.rolling(rolling_intv, min_periods=1).mean(), 'b')
+    plt.fill_between(np.arange(len(rate_array))+1, df.rolling(rolling_intv, min_periods=1).min()[0], df.rolling(rolling_intv, min_periods=1).max()[0], color = 'b', alpha = 0.2)
+    plt.ylabel('Normalized Computation Rate')
+    plt.xlabel('Time Frames')
+    plt.show()
+
+def save_to_txt(rate_his, file_path):
+    with open(file_path, 'w') as f:
+        for rate in rate_his:
+            f.write("%s \n" % rate)
+
+if __name__ == "__main__":
+    '''
+        This algorithm generates K modes from DNN, and chooses with largest
+        reward. The mode with largest reward is stored in the memory, which is
+        further used to train the DNN.
+        Adaptive K is implemented. K = max(K, K_his[-memory_size])
+    '''
+
+    N = 10                       # number of users
+    n = 30000                    # number of time frames
+    K = N                        # initialize K = N
+    decoder_mode = 'OP'          # the quantization mode could be 'OP' (Order-preserving) or 'KNN'
+    Memory = 1024                # capacity of memory structure
+    Delta = 32                   # Update interval for adaptive K
+
+    print('#user = %d, #channel=%d, K=%d, decoder = %s, Memory = %d, Delta = %d'%(N,n,K,decoder_mode, Memory, Delta))
+    # Load data
+    channel = sio.loadmat('./data/data_%d' %N)['input_h']
+    rate = sio.loadmat('./data/data_%d' %N)['output_obj'] # this rate is only used to plot figures; never used to train DROO.
+
+    # increase h to close to 1 for better training; it is a trick widely adopted in deep learning
+    channel = channel * 1000000
+
+    # generate the train and test data sample index
+    # data are splitted as 80:20
+    # training data are randomly sampled with duplication if n > total data size
+
+    split_idx = int(.8 * len(channel))
+    num_test = min(len(channel) - split_idx, n - int(.8 * n)) # training data size
+
+
+    mem = MemoryDNN(net = [N, 120, 80, N],
+                    learning_rate = 0.01,
+                    training_interval=10,
+                    batch_size=128,
+                    memory_size=Memory
+                    )
+
+    start_time = time.time()
+
+    rate_his = []
+    rate_his_ratio = []
+    mode_his = []
+    k_idx_his = []
+    K_his = []
+    for i in range(n):
+        if i % (n//10) == 0:
+           print("%0.1f"%(i/n))
+        if i> 0 and i % Delta == 0:
+            # index counts from 0
+            if Delta > 1:
+                max_k = max(k_idx_his[-Delta:-1]) +1;
+            else:
+                max_k = k_idx_his[-1] +1;
+            K = min(max_k +1, N)
+
+        if i < n - num_test:
+            # training
+            i_idx = i % split_idx
+        else:
+            # test
+            i_idx = i - n + num_test + split_idx
+
+        h = channel[i_idx,:]
+
+        # the action selection must be either 'OP' or 'KNN'
+        m_list = mem.decode(h, K, decoder_mode)
+
+        r_list = []
+        for m in m_list:
+            r_list.append(bisection(h/1000000, m)[0])
+
+        # encode the mode with largest reward
+        mem.encode(h, m_list[np.argmax(r_list)])
+        # the main code for DROO training ends here
+
+
+
+
+        # the following codes store some interested metrics for illustrations
+        # memorize the largest reward
+        rate_his.append(np.max(r_list))
+        rate_his_ratio.append(rate_his[-1] / rate[i_idx][0])
+        # record the index of largest reward
+        k_idx_his.append(np.argmax(r_list))
+        # record K in case of adaptive K
+        K_his.append(K)
+        mode_his.append(m_list[np.argmax(r_list)])
+
+
+    total_time=time.time()-start_time
+    mem.plot_cost()
+    plot_rate(rate_his_ratio)
+
+    print("Averaged normalized computation rate:", sum(rate_his_ratio[-num_test: -1])/num_test)
+    print('Total time consumed:%s'%total_time)
+    print('Average time per channel:%s'%(total_time/n))
+
+    # save data into txt
+    save_to_txt(k_idx_his, "k_idx_his.txt")
+    save_to_txt(K_his, "K_his.txt")
+    save_to_txt(mem.cost_his, "cost_his.txt")
+    save_to_txt(rate_his_ratio, "rate_his_ratio.txt")
+    save_to_txt(mode_his, "mode_his.txt")