상세정보
학술지
Deep Learning Methods for Universal MISO Beamforming
- 저자
- 김준범, 이훈, 홍승은, 박석환
- 발행일
- 202011
- 출처
- IEEE Wireless Communications Letters, v.9 no.11, pp.1894-1898
- ISSN
- 2162-2337
- 출판사
- IEEE
- 협약과제
- 20HH3200, 고밀집 네트워크(UDN) 환경에서 고용량, 저비용 달성을 위한 무선전송 기술 개발, 홍승은
- 초록
- This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station. We exploit the sum power budget as side information so that deep neural networks (DNNs) can effectively learn the impact of the power constraint in the beamforming optimization. Consequently, a single training process is sufficient for the proposed universal DL approach, whereas conventional methods need to train multiple DNNs for all possible power budget levels. Numerical results demonstrate the effectiveness of the proposed DL methods over existing schemes.
- KSP 제안 키워드
- Beamforming optimization, Conventional methods, Deep neural network(DNN), Existing schemes, Learning methods, Multi-antenna systems, Numerical results, Power Constraint, Power Limitation, Transmit power, base station(BS)