Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks
Python code to reproduce our works on Wireless-powered Mobile-Edge Computing [1], which uses the wireless channel gains as the input and the binary computing mode selection results as the output of a deep neural network (DNN). It includes:
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memory.py: the DNN structure for the WPMEC, inclduing training structure and test structure
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data: all data are stored in this subdirectory, includes:
- data_#.mat: training and testing data sets, where # = {10, 20, 30} is the user number
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main.py: run this file for DROO, including setting system parameters
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demo_alternate_weights.py: run this file to evaluate the performance of DROO when WDs' weights are alternated
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demo_on_off.py: run this file to evaluate the performance of DROO when some WDs are randomly turning on/off
- L. Huang, S. Bi, and Y. J. Zhang, “Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks,” IEEE Trans. Mobile Compt., DOI:10.1109/TMC.2019.2928811, Jul. 2019.
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Liang HUANG, lianghuang AT zjut.edu.cn
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Suzhi BI, bsz AT szu.edu.cn
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Ying Jun (Angela) Zhang, yjzhang AT ie.cuhk.edu.hk
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Tensorflow
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numpy
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scipy
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For DROO algorithm, run the file, main.py
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For DROO demo with laternating-weight WDs, run the file, demo_alternate_weights.py
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For DROO demo with ON-OFF WDs, run the file, demo_on_off.py