The interplay between artificial intelligence(AI) and fog radio access networks(F-RANs) is investigated in this work from two perspectives: how F-RANs enable hierarchical AI to be deployed in wireless networks and how...The interplay between artificial intelligence(AI) and fog radio access networks(F-RANs) is investigated in this work from two perspectives: how F-RANs enable hierarchical AI to be deployed in wireless networks and how AI makes F-RANs smarter to better serve mobile devices. Due to the heterogeneity of processing capability, the cloud, fog, and device layers in F-RANs provide hierarchical intelligence via centralized, distributed, and federated learning. In addition, cross-layer learning is also introduced to further reduce the demand for the memory size of the mobile devices. On the other hand, AI provides F-RANs with technologies and methods to deal with massive data and make smarter decisions. Specifically, machine learning tools such as deep neural networks are introduced for data processing, while reinforcement learning(RL) algorithms are adopted for network optimization and decisions. Then, two examples of AI-based applications in F-RANs, i.e., health monitoring and intelligent transportation systems, are presented, followed by a case study of an RL-based caching application in the presence of spatio-temporal unknown content popularity to showcase the potential of applying AI to F-RANs.展开更多
This paper studies the effect of phase noise and fronthaul compression on a downlink cloud radio access network(C-RAN), where several remote radio heads(RRHs) are coordinated to communicate with users by a baseband un...This paper studies the effect of phase noise and fronthaul compression on a downlink cloud radio access network(C-RAN), where several remote radio heads(RRHs) are coordinated to communicate with users by a baseband unit(BBU) on the cloud server. In the system, the baseband signals are precoded at BBU, and then compressed before being transmitted to RRHs through capacity-limited fronthaul links which results in the compressive quantization noise. We assume the regularized zero-forcing precoding is performed with an imperfect channel state information and a compression strategy is applied at BBU. The effect of phase noise arising from nonideal local oscillators both at RRHs and users is considered. We propose an approximate expression for the downlink ergodic sum-rate of considered C-RAN utilizing large dimensional random matrix theory in the large-system regime. From simulation results, the accuracy of the approximate expression is validated, and the effect of phase noise and fronthaul compression can be analyzed theoretically based on the approximate expression.展开更多
基金supported in part by the National Natural Science Foundation of China under Grants U1805262,61871446,and 61671251。
文摘The interplay between artificial intelligence(AI) and fog radio access networks(F-RANs) is investigated in this work from two perspectives: how F-RANs enable hierarchical AI to be deployed in wireless networks and how AI makes F-RANs smarter to better serve mobile devices. Due to the heterogeneity of processing capability, the cloud, fog, and device layers in F-RANs provide hierarchical intelligence via centralized, distributed, and federated learning. In addition, cross-layer learning is also introduced to further reduce the demand for the memory size of the mobile devices. On the other hand, AI provides F-RANs with technologies and methods to deal with massive data and make smarter decisions. Specifically, machine learning tools such as deep neural networks are introduced for data processing, while reinforcement learning(RL) algorithms are adopted for network optimization and decisions. Then, two examples of AI-based applications in F-RANs, i.e., health monitoring and intelligent transportation systems, are presented, followed by a case study of an RL-based caching application in the presence of spatio-temporal unknown content popularity to showcase the potential of applying AI to F-RANs.
基金supported in part by the Natural Science Foundation of China (NSFC) under Grant U1805262, 61871446, and 61671251supported by NSFC under Grant 61625106 and Grant 61531011
文摘This paper studies the effect of phase noise and fronthaul compression on a downlink cloud radio access network(C-RAN), where several remote radio heads(RRHs) are coordinated to communicate with users by a baseband unit(BBU) on the cloud server. In the system, the baseband signals are precoded at BBU, and then compressed before being transmitted to RRHs through capacity-limited fronthaul links which results in the compressive quantization noise. We assume the regularized zero-forcing precoding is performed with an imperfect channel state information and a compression strategy is applied at BBU. The effect of phase noise arising from nonideal local oscillators both at RRHs and users is considered. We propose an approximate expression for the downlink ergodic sum-rate of considered C-RAN utilizing large dimensional random matrix theory in the large-system regime. From simulation results, the accuracy of the approximate expression is validated, and the effect of phase noise and fronthaul compression can be analyzed theoretically based on the approximate expression.
