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학술지 Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks
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저자
유대성, 이훈, 박석환, 홍승은
발행일
202110
출처
IEEE Wireless Communications Letters, v.10 no.10, pp.2180-2184
ISSN
2162-2337
출판사
IEEE
DOI
https://dx.doi.org/10.1109/LWC.2021.3095500
협약과제
21HH1100, [통합과제] 5G NR 기반 지능형 오픈 스몰셀 기술 개발, 나지현
초록
Cooperative beamforming across access points (APs) and fronthaul quantization strategies are essential for cloud radio access network (C-RAN) systems. The nonconvexity of the C-RAN optimization problems, which is stemmed from per-AP power and fronthaul capacity constraints, requires high computational complexity for executing iterative algorithms. To resolve this issue, we investigate a deep learning approach where the optimization module is replaced with a well-trained deep neural network (DNN). An efficient learning solution is proposed which constructs a DNN to produce a low-dimensional representation of optimal beamforming and quantization strategies. Numerical results validate the advantages of the proposed learning solution.
KSP 제안 키워드
Access point, Cloud radio, Cloud-RAN(C-RAN), Computational complexity, Cooperative Beamforming, Deep neural network(DNN), Efficient learning, Joint Optimization, Learning approach, Learning methods, Low-dimensional representation