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A DDPG based reinforcement learning algorithm for cognitive radio inspired non-orthogonal multiple access (CR-NOMA) systems

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CRNOMA_DDPG

These are the codes for the following paper

Z. Ding, R. Schober, and H. V. Poor, No-Pain No-Gain: DRL Assisted Optimization in Energy-Constrained CR-NOMA Networks, IEEE Trans. Communications, submitted. (a copy of this paper is included in this folder)

A good starting point is to run the file 'two_user_case.py', which is fast and generates Fig. 1 in the paper. In order to simulate the case with fading, the file 'special_case.py' should be used, where Figs. 2, 3, and 4 in the paper can be generated. The file 'random_location_with_fading K.py' will take quite long time to run.


The paper and the codes have been updated to ensure that the DDPG algorithm can also work in the case with time-varying channels. The revision of the paper and the codes can be found in the timevarying folder. A good starting point is to run the file 'special_case.py', which generates Fig. 5 in the revised paper. The use of file, 'fading K varying.py', generates Fig. 6, but it takes quite long time to run.

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A DDPG based reinforcement learning algorithm for cognitive radio inspired non-orthogonal multiple access (CR-NOMA) systems

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