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The proliferation of the greedy heterogeneous wireless network demands the ability to handle the sharply increasing resource requirements of power and spectrum. Thus, the study of novel and effective methods to allocate power and spectrum resources in order to reduce energy consumption for heterogeneous devices has become an urgent issue. In this context the article proposes a DJRA in which data fusion, combined with NOMA, SIC and beamforming techniques, are utilized to allocate power and spectrum resources to heterogeneous devices with a reduced energy consumption, where different radio access technologies are integrated into a cellular network for communication between devices. The data correlations between the devices are computed to group the devices so as to reduce data transmission. In addition, NOMA, SIC and beamforming are used in combination to reduce network interference between BSs and the heterogeneous devices. The main contribution of this article is the design of a large-scale heterogeneous wireless network paradigm to improve the percentage of resource utilization also reducing the energy consumption. [ABSTRACT FROM AUTHOR]

Haptic feedback is crucial to immersive experience in virtual and augmented reality applications. The existing promising maglev haptic devices have advantages of no mechanical friction and low inertia. However, their performance is limited by the navigation approach, which mainly results from the challenge that it is difficult to obtain high precision, high frequency, and good stability with lightweight design at the same time. In this study, we reformulate visual-inertial navigation as a regression problem, and adopt deep learning to perform fusion navigation for maglev haptic interaction. A cascade-LSTM-based q-increment learning method is first proposed to progressively learn the increments of target variables. Two cascade LSTM networks are then constructed to estimate the increments of position and orientation, which are pipelined to accomplish visual-inertial fusion navigation. Additionally, we set up a maglev haptic platform as the system testbed. Experimental results show that our cascade-LSTMbased visual-inertial fusion navigation approach can reach 200 Hz while maintaining high-precision navigation (the mean absolute error of the position and orientation is less than 1 mm and 0.02°, respectively) for a maglev haptic interactive deformation application. [ABSTRACT FROM AUTHOR]

The space-terrestrial integrated network (STIN), one of the hottest trends in technology, is transforming our future through interconnecting heterogeneous devices. It has a revolutionary impact on reshaping the industry and changing the world. The extending of new generation network spectrum resources creates tremendous opportunities for obtaining high-level terrestrial network and brings a good prospect for the development of STIN, but STIN still faces a big change of resource shortage caused by the ever increasing heterogeneous devices. In this article, a new STIN architecture supporting virtualization technology is first designed to manage spectrum resource in STIN to satisfy various quality of service (QoS) requirements from heterogeneous devices. Then a dynamic virtualized QoS-driven spectrum allocation algorithm (VQSA) is proposed to improve the QoS from different devices in STIN. VQSA classifies heterogeneous devices into virtual cells according to their QoS correlation and determines the corresponding base stations and satellites for providing services. Spectrum resources in multiple base stations and satellites are rationally assigned in a virtual cell to serve heterogeneous devices with different QoS requirements. Finally, simulation results are provided to show the efficiency of the architecture and the VQSA algorithm in terms of transmission delay and transmission rate. [ABSTRACT FROM AUTHOR]

With the development of intelligent applications (e.g., self-driving, real-time emotion recognition), there are higher requirements for cloud intelligence. However, cloud intelligence depends on the multi-modal data collected by user equipments (UEs). Due to the limited capacity of network bandwidth, offloading all data generated from the UEs to the remote cloud is impractical. Thus, in this article, we consider the challenging issue of achieving a certain level of cloud intelligence while reducing network traffic. In order to solve this problem, we design a traffic control algorithm based on label-less learning on the edge cloud, which is dubbed LLTC. By use of the limited computing and storage resources at the edge cloud, LLTC evaluates the value of data that will be offloaded. Specifically, we first give a statement of the problem and the system architecture. Then we design the LLTC algorithm in detail. Finally, we set up the system testbed. Experimental results show that the proposed LLTC can guarantee the required cloud intelligence while minimizing the amount of data transmission. [ABSTRACT FROM AUTHOR]

The space-terrestrial integrated network (STIN), one of the hottest trends in technology, is transforming our future through interconnecting heterogeneous devices. It has a revolutionary impact on reshaping the industry and changing the world. The extending of new generation network spectrum resources creates tremendous opportunities for obtaining high-level terrestrial network and brings a good prospect for the development of STIN, but STIN still faces a big change of resource shortage caused by the ever increasing heterogeneous devices. In this article, a new STIN architecture supporting virtualization technology is first designed to manage spectrum resource in STIN to satisfy various quality of service (QoS) requirements from heterogeneous devices. Then a dynamic virtualized QoS-driven spectrum allocation algorithm (VQSA) is proposed to improve the QoS from different devices in STIN. VQSA classifies heterogeneous devices into virtual cells according to their QoS correlation and determines the corresponding base stations and satellites for providing services. Spectrum resources in multiple base stations and satellites are rationally assigned in a virtual cell to serve heterogeneous devices with different QoS requirements. Finally, simulation results are provided to show the efficiency of the architecture and the VQSA algorithm in terms of transmission delay and transmission rate. [ABSTRACT FROM AUTHOR]