In term of the features of 3G system, a novel AMR link adaptation strategy for 3G system is proposed. The impacts of AMR codec modes and power control on traffic quality of service are taken into account in the strate...In term of the features of 3G system, a novel AMR link adaptation strategy for 3G system is proposed. The impacts of AMR codec modes and power control on traffic quality of service are taken into account in the strategy at the same time. By synthetically comparing the signal-to-interference ratio value with the switching threshold and comparing the transmission power with its threshold, radio resource management can always keep each link on its proper codec mode with the corresponding optimal power level to achieve both robust speech quality and link capacity enhancement. Based on the WCDMA FDD uplink link-level simulation platform, AMR link adaptation platform is constructed. Simulation results show that the algorithm can track the fast change of channel conditions and select the most robust codec mode, thus the synthetic speech quality of AMR is better than that of signal mode during a wide range of channel conditions. The result will provide a reference strategy for AMR link adaptation of 3G system.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
文摘In term of the features of 3G system, a novel AMR link adaptation strategy for 3G system is proposed. The impacts of AMR codec modes and power control on traffic quality of service are taken into account in the strategy at the same time. By synthetically comparing the signal-to-interference ratio value with the switching threshold and comparing the transmission power with its threshold, radio resource management can always keep each link on its proper codec mode with the corresponding optimal power level to achieve both robust speech quality and link capacity enhancement. Based on the WCDMA FDD uplink link-level simulation platform, AMR link adaptation platform is constructed. Simulation results show that the algorithm can track the fast change of channel conditions and select the most robust codec mode, thus the synthetic speech quality of AMR is better than that of signal mode during a wide range of channel conditions. The result will provide a reference strategy for AMR link adaptation of 3G system.
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.
