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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.
키워드
beamforming optimization, Cloud radio access networks, constrained fronthaul, deep learning
KSP 제안 키워드
Access point, Beamforming optimization, Cloud radio, Cloud-RAN(C-RAN), Computational complexity, Cooperative Beamforming, Deep neural network(DNN), Efficient learning, Joint Optimization, Learning approach, Learning methods