Robust Channel Assignment for Hybrid NOMA Systems with Condition Number Constrainted DRL
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The Hybrid Non-Orthogonal Multiple Access (NOMA) is an alternative solution for future multiple access techniques, and the performance of hybrid NOMA systems relies on the quality of channel assignment. Conventional optimization approaches rely on the perfect Channel State Information (CSI), which hinders the deployment of the Hybrid systems. Deep Reinforcement Learning (DRL) approaches are robust to uncertain environments, and have been applied to deal with the dynamic channel assignment in hybrid NOMA systems. In this paper, a novel DRL approach based on condition number constraint is proposed to further enhance the robustness of the model. The simulation results show that the proposed approach achieves higher average spectral efficiency under imperfect CSI, compared to unconstrained DRL approaches and conventional approaches. This is useful for critical infrastructure systems such as base stations that require a high degree of robustness. © 2021 IEEE.
Açıklama
Institute on Networking Systems of AI (INSAI)
2021 International Conference on Networking Systems of AI, INSAI 2021 -- 19 November 2021 through 20 November 2021 -- Shanghai -- 178906
2021 International Conference on Networking Systems of AI, INSAI 2021 -- 19 November 2021 through 20 November 2021 -- Shanghai -- 178906
Anahtar Kelimeler
Channel assignment, Condition number, Deep reinforcement learning, Hybrid noma systems
Kaynak
Proceedings - 2021 International Conference on Networking Systems of AI, INSAI 2021
WoS Q Değeri
Scopus Q Değeri
N/A
