| Autor: |
Hyeon Woong Kim, Hyun Jung Park, Sung Ho Chae |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 10, Pp 8994-9003 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3143796 |
| Popis: |
As various Internet of things (IoT) communication services have recently received great attention, the development of resource allocation scheme that can support the connection of a number of IoT devices becomes an important task for next-generation communication systems. Motivated this challenge, we propose deep learning-based optimization algorithms for a joint resource allocation problem in uplink IoT cellular networks, in which the base station uses multiple sub-bands to serve IoT users and inter sub-band interference exists due to spectral leakage. Specifically, to maximize the achievable sum rate of IoT users with low complexity, we develop a two-stage optimization method built on convolutional neural networks (CNNs) that sequentially optimizes sub-band assignment and transmit power control. Moreover, in order to examine the performance according to the neural network structure, the proposed scheme is also implemented through fully-connected neural networks (FNNs) and compared with the CNN-based scheme. Simulation results show that our proposed CNN-based algorithm significantly improves the sum rate and reduces the required computation time compared to previous schemes without deep learning. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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