High frequency sky wave communication suffers from poor performance including poor link quality and low link success rate. To enhance performance, diversity technology is proposed in the high frequency communication n...High frequency sky wave communication suffers from poor performance including poor link quality and low link success rate. To enhance performance, diversity technology is proposed in the high frequency communication network(HFCN) in this paper.First, we present the benefits and the challenges by introducing diversity technology into the existing HFCN. Secondly, to exploit the benefits fully and overcome the challenges, we propose a system structure suitable for deploying diversity technology in HFCN in large scale,based on the cloud radio access network and software defined network. Moreover, we present a general structure for the real-time updating frequency management system that plays a more important role especially when resource consuming(e.g., frequency) diversity technology is deployed. Thirdly, we investigate the key techniques enabling diversity technology deployment. Finally, we point out the future research directions to help the HFCN with diversity work more efficiently and intelligently.展开更多
In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In t...In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In that case, the Quality of Experience(Qo E) has received much attention and has become a key performance measurement for the application and service. In order to meet the users' expectations, the management of the resource is crucial in wireless network, especially the Qo E based resource allocation. One of the effective way for resource allocation management is accurate application identification. In this paper, we propose a novel deep learning based method for application identification. We first analyse the requirement of managing Qo E for wireless communication, and review the limitation of the traditional identification methods. After that, a deep learning based method is proposed for automatically extracting the features and identifying the type of application. The proposed method is evaluated by using the practical wireless traffic data, and the experiments verify the effectiveness of our method.展开更多
基金supported by the National Science Foundation of China under Grants No. 61801492 and No. 61601490a national major specific project governed by the national development and reform commission of China
文摘High frequency sky wave communication suffers from poor performance including poor link quality and low link success rate. To enhance performance, diversity technology is proposed in the high frequency communication network(HFCN) in this paper.First, we present the benefits and the challenges by introducing diversity technology into the existing HFCN. Secondly, to exploit the benefits fully and overcome the challenges, we propose a system structure suitable for deploying diversity technology in HFCN in large scale,based on the cloud radio access network and software defined network. Moreover, we present a general structure for the real-time updating frequency management system that plays a more important role especially when resource consuming(e.g., frequency) diversity technology is deployed. Thirdly, we investigate the key techniques enabling diversity technology deployment. Finally, we point out the future research directions to help the HFCN with diversity work more efficiently and intelligently.
基金supported by NSAF under Grant(No.U1530117)National Natural Science Foundation of China(No.61471022 and No.61201156)
文摘In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In that case, the Quality of Experience(Qo E) has received much attention and has become a key performance measurement for the application and service. In order to meet the users' expectations, the management of the resource is crucial in wireless network, especially the Qo E based resource allocation. One of the effective way for resource allocation management is accurate application identification. In this paper, we propose a novel deep learning based method for application identification. We first analyse the requirement of managing Qo E for wireless communication, and review the limitation of the traditional identification methods. After that, a deep learning based method is proposed for automatically extracting the features and identifying the type of application. The proposed method is evaluated by using the practical wireless traffic data, and the experiments verify the effectiveness of our method.
