Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks
Python code to reproduce our DROO algorithm for Wireless-powered Mobile-Edge Computing [1], which uses the time-varying wireless channel gains as the input and generates the binary offloading decisions. It includes:
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memory.py: the DNN structure for the WPMEC, inclduing training structure and test structure, implemented based on Tensorflow 1.x.
- memoryTF2.py: Implemented based on Tensorflow 2.
- memoryPyTorch.py: Implemented based on PyTorch.
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optimization.py: solve the resource allocation problem
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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, implemented based on Tensorflow 1.x
- mainTF2.py: Implemented based on Tensorflow 2. Run this file for DROO if you code with Tensorflow 2.
- mainPyTorch.py: Implemented based on PyTorch. Run this file for DROO if you code with PyTorch.
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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., vol. 19, no. 11, pp. 2581-2593, November 2020.
@ARTICLE{huang2020DROO,
author={Huang, Liang and Bi, Suzhi and Zhang, Ying-Jun Angela},
journal={IEEE Transactions on Mobile Computing},
title={Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks},
year={2020},
month={November},
volume={19},
number={11},
pages={2581-2593},
doi={10.1109/TMC.2019.2928811}
}
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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. If you code with Tenforflow 2 or PyTorch, run mainTF2.py or mainPyTorch.py, respectively. The original DROO algorithm is coded based on Tensorflow 1.x. If you are fresh to deep learning, please start with Tensorflow 2 or PyTorch, whose codes are much cleaner and easier to follow.
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For more DROO demos:
- Laternating-weight WDs, run the file, [demo_alternate_weights.py](demo_alternate_weights.
- ON-OFF WDs, run the file, demo_on_off.py
- Remember to respectively edit the import MemoryDNN code from
to
from memory import MemoryDNNorfrom memoryTF2 import MemoryDNNif you are using Tensorflow 2 or PyTorch.from memoryPyTorch import MemoryDNN