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Ensemble deep learning based resource allocation for multi-channel underlay cognitive radio systemopen accessEnsemble deep learning based resource allocation for multi-channel underlay cognitive radio system
- Other Titles
- Ensemble deep learning based resource allocation for multi-channel underlay cognitive radio system
- Authors
- Lee, W.; Chung, B.C.
- Issue Date
- Aug-2023
- Publisher
- Korean Institute of Communication Sciences
- Keywords
- Deep learning; Ensemble machine learning; Non-convex optimization; Resource allocation; Underlay cognitive radio
- Citation
- ICT Express, v.9, no.4, pp 642 - 647
- Pages
- 6
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- ICT Express
- Volume
- 9
- Number
- 4
- Start Page
- 642
- End Page
- 647
- ISSN
- 2405-9595
2405-9595
- Abstract
- This paper proposes a resource allocation strategy for multi-channel underlay cognitive radio (CR) systems by means of an ensemble deep learning framework. The transmit power of secondary users (SUs) allocated to each channel is determined to maximize the overall spectral efficiency (SE), whilst meeting the interference constraint on the primary user (PU). To this end, a deep neural network (DNN) structure is developed, in which multiple DNN units are jointly utilized, to obtain the diversity over different DNNs. Our simulation results confirm that the proposed scheme can achieve near-optimal performance with a low computation time of less than 1.5 ms. © 2022 The Authors
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