Owing to the inherent central information processing and resource management ability,the cloud radio access network(C-RAN)is a promising network structure for an intelligent and simplified sixth-generation(6G)wireless...Owing to the inherent central information processing and resource management ability,the cloud radio access network(C-RAN)is a promising network structure for an intelligent and simplified sixth-generation(6G)wireless network.Nevertheless,to further enhance the capacity and coverage,more radio remote heads(RRHs)as well as high-fidelity and low-latency fronthaul links are required,which may lead to high implementation cost.To address this issue,we propose to exploit the intelligent reflecting surface(IRS)as an alternative way to enhance the C-RAN,which is a low-cost and energy-efficient option.Specifically,we consider the uplink transmission where multi-antenna users communicate with the baseband unit(BBU)pool through multi-antenna RRHs and multiple IRSs are deployed between the users and RRHs.RRHs can conduct either point-to-point(P2P)compression or Wyner-Ziv coding to compress the received signals,which are then forwarded to the BBU pool through fronthaul links.We investigate the joint design and optimization of user transmit beamformers,IRS passive beamformers,and fronthaul compression noise covariance matrices to maximize the uplink sum rate subject to fronthaul capacity constraints under P2P compression and Wyner-Ziv coding.By exploiting the Arimoto-Blahut algorithm and semi-definite relaxation(SDR),we propose a successive convex approximation approach to solve non-convex problems,and two iterative algorithms corresponding to P2P compression and Wyner-Ziv coding are provided.Numerical results verify the performance gain brought about by deploying IRS in C-RAN and the superiority of the proposed joint design.展开更多
压缩感知又称为压缩采样技术,是一种新的信号感知或采样方法,已经在各个科学研究领域中崭露头角,尤其在空间目标方位估计(Direction Of Arrival,DOA)领域中引起研究学者的广泛关注。文中首先阐述压缩感知技术应用于目标方位估计的基本原...压缩感知又称为压缩采样技术,是一种新的信号感知或采样方法,已经在各个科学研究领域中崭露头角,尤其在空间目标方位估计(Direction Of Arrival,DOA)领域中引起研究学者的广泛关注。文中首先阐述压缩感知技术应用于目标方位估计的基本原理,并采用高效稳定的凸优化问题解算工具箱CVX进行目标方位的有效求解,在此基础之上,将压缩波束形成技术与虚拟阵列孔径理论相结合,提出一种基于虚拟阵列的压缩波束形成技术,以期获得更高的空间目标方位分辨能力。数值仿真结果表明了文中方法的有效性,尤其在低信噪比、相干源和小快拍数条件下,该方法能够分辨相近的空间目标,较传统方法具有更高的空间分辨能力。展开更多
基金Project supported by the Zhejiang Provincial Natural Science Foundation of China(Nos.LY21F010008 and LD21F010001)the National Natural Science Foundation of China(No.62171412)the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University,China(No.2020D10)。
文摘Owing to the inherent central information processing and resource management ability,the cloud radio access network(C-RAN)is a promising network structure for an intelligent and simplified sixth-generation(6G)wireless network.Nevertheless,to further enhance the capacity and coverage,more radio remote heads(RRHs)as well as high-fidelity and low-latency fronthaul links are required,which may lead to high implementation cost.To address this issue,we propose to exploit the intelligent reflecting surface(IRS)as an alternative way to enhance the C-RAN,which is a low-cost and energy-efficient option.Specifically,we consider the uplink transmission where multi-antenna users communicate with the baseband unit(BBU)pool through multi-antenna RRHs and multiple IRSs are deployed between the users and RRHs.RRHs can conduct either point-to-point(P2P)compression or Wyner-Ziv coding to compress the received signals,which are then forwarded to the BBU pool through fronthaul links.We investigate the joint design and optimization of user transmit beamformers,IRS passive beamformers,and fronthaul compression noise covariance matrices to maximize the uplink sum rate subject to fronthaul capacity constraints under P2P compression and Wyner-Ziv coding.By exploiting the Arimoto-Blahut algorithm and semi-definite relaxation(SDR),we propose a successive convex approximation approach to solve non-convex problems,and two iterative algorithms corresponding to P2P compression and Wyner-Ziv coding are provided.Numerical results verify the performance gain brought about by deploying IRS in C-RAN and the superiority of the proposed joint design.
文摘压缩感知又称为压缩采样技术,是一种新的信号感知或采样方法,已经在各个科学研究领域中崭露头角,尤其在空间目标方位估计(Direction Of Arrival,DOA)领域中引起研究学者的广泛关注。文中首先阐述压缩感知技术应用于目标方位估计的基本原理,并采用高效稳定的凸优化问题解算工具箱CVX进行目标方位的有效求解,在此基础之上,将压缩波束形成技术与虚拟阵列孔径理论相结合,提出一种基于虚拟阵列的压缩波束形成技术,以期获得更高的空间目标方位分辨能力。数值仿真结果表明了文中方法的有效性,尤其在低信噪比、相干源和小快拍数条件下,该方法能够分辨相近的空间目标,较传统方法具有更高的空间分辨能力。
