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DROO

Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

This folder includs all pre-generated training and testing data set, including:

  • data_#.mat: , where # = {5, 6, 7, 8, 9, 10, 20, 30} is the number of WDs

  • data_10_WeightsAlternated.mat: The data set when all WDs' weights are alternated. It contains the same values of 'input_h' as the ones stored in data_10.mat. However, the optimal offloading mode, resource allocation, and the maximum computation rate are recalculated since WDs' weights are alternated.

Data Format

Data samples are generated by enumerating all 2^N binary offloading actions for N <= 10 and by following the CD method presented in [2] for N = 20, 30. There are 30,000 (for N = 10, 20, 30) or 10,000 (otherwise) samples saved in each *.mat file. Where each data sample includes:

variable description
input_h The wireless channel gain between WDs and the AP $\mathbf{h}$
output_mode The optimal binary offloading action $\mathbf{x}^*$
output_a The optimal fraction of time that the AP broadcasts RF energy for the WDs to harvest $a^*$
output_tau The optimal fraction of time allocated to WDs for task offloading $\mathbf{\tau}^*$
output_obj The optimal weighted sum computation rate $Q^*$

About our works

  1. Liang Huang, Suzhi Bi, and Ying-jun Angela Zhang, "Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks", on arxiv:1808.01977.
  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.

About authors

  • Liang HUANG, lianghuang AT zjut.edu.cn

  • Suzhi BI, bsz AT szu.edu.cn

  • Ying Jun (Angela) Zhang, yjzhang AT ie.cuhk.edu.hk