Gao, J;
Khandaker, MRA;
Tariq, F;
Wong, KK;
Khan, RT;
(2019)
Deep neural network based resource allocation for V2X communications.
In:
2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall).
IEEE: Honolulu, HI, USA.
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Abstract
This paper focuses on optimal transmit power allocation to maximize the overall system throughput in a vehicle-to-everything (V2X) communication system. We propose two methods for solving the power allocation problem namely the weighted minimum mean square error (WMMSE) algorithm and the deep learning-based method. In the WMMSE algorithm, we solve the problem using block coordinate descent (BCD) method. Then we adopt supervised learning technique for the deep neural network (DNN) based approach considering the power allocation from the WMMSE algorithm as the target output. We exploit an efficient implementation of the mini-batch gradient descent algorithm for training the DNN. Extensive simulation results demonstrate that the DNN algorithm can provide very good approximation of the iterative WMMSE algorithm yet reducing the computational overhead significantly.
| Type: | Proceedings paper |
|---|---|
| Title: | Deep neural network based resource allocation for V2X communications |
| Event: | VTC2019 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/VTCFall.2019.8891446 |
| Publisher version: | https://doi.org/10.1109/VTCFall.2019.8891446 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Resource management , Training , Interference , Machine learning , Copper , Vehicle-to-everything |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10087718 |
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