상세정보
학술지
Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks
- 저자
- 유대성, 이훈, 박석환, 홍승은
- 발행일
- 202110
- 출처
- IEEE Wireless Communications Letters, v.10 no.10, pp.2180-2184
- ISSN
- 2162-2337
- 출판사
- IEEE
- 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