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Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To overcome the serve energy consumption problem in indoor wireless networks, a deep Q-Learning based transmit power allocation algorithm for home base station is proposed. Firstly, a deep learning network (DLN) is built to optimize the energy efficiency of indoor wireless networks. Then, the energy consumption rating is regarded as the rewards, the batch gradient descent method is used to continuously train the weights of DLN. Finally, the simulation results show that the proposed algorithm can dynamically adjust the transmit power, and is significantly superior to Q-Learning and water-filling algorithms in terms of convergence speed and EC optimization, which can effectively reduce the energy consumption of indoor wireless networks. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)