This paper looks into the realization of digital twin (DT) technology in unmanned aerial vehicle (UAV) networks. We propose, in particular, a framework for DT-based UAV applications in which distinct jobs in the digital twin interact with UAVs in the physical world via task manager scheduling. Furthermore, we investigate the use of 3D mmWave Radar imaging on UAVs and apply it to the process of radio frequency (RF) characterizing. After that, the method of 3D ray-tracing is employed to accomplish channel modelling of UAVs, which reflects RF domain digital twin match. Finally, we present numerical results to demonstrate that our developed digital twin platform can provide accurate RF presentation of UAV and therefore accomplish smart operating and administration of the actual UAV network.

5G multicast/broadcast multimedia service (MBMS) can efficiently transmit multimedia data for a large number of mobile users simultaneously over broadcast or multicast channels of the mobile network. This paper investigates the ability to provide full-duplex (FD) relay for 5G MBMS using Unmanned Aerial Vehicles (UAVs). To increase the multimedia data transmission rate, the coordinated multiple point (CoMP) technique is used to aggregate data from multiple base stations via UAV relay. Also, to improve transmission reliability, Raptor Q, an advanced fountain code, is used to encode messages in the application layer. Based on these two technologies, a 5G MBMS technical solution based on UAV full-duplex relay is developed. Through the simulation of the system error block rate under various scenarios, it is shown that the proposed scheme can significantly improve the transmission performance of 5G MBMS traffic under different numbers of participating base stations, different UAV flight heights and different flight trajectories.

This research enhances IoT services in 6G non-terrestrial networks (NTNs) by effectively incorporating multicast massive MIMO technology. Based on the Multicast–Broadcast Services (MBS) framework from 3GPP Release 17, we have developed a tailored framework specifically designed for 6G IoT environments. Key features of this framework include a bespoke system architecture, state-of-the-art multicast beamforming, and sophisticated user grouping algorithms. Our study rigorously assesses the framework’s performance, with a particular focus on enhancing spectral efficiency and minimizing latency, both of which are crucial for IoT applications. Our findings reveal that the application of precision-engineered multicast MIMO precoding techniques plays a pivotal role in enhancing network efficiency, underscoring the transformative potential of multicast massive MIMO in 6G IoT service provision. These results strongly suggest that multicast massive MIMO holds significant promise for transforming IoT service delivery in the realm of 6G NTNs.

Due to the characteristics of global coverage, on-demand access, and large capacity, the low earth orbit (LEO) satellite communication (SatCom) has become one promising technology to support the Internet-of-Things (IoT). However, due to the scarcity of satellite spectrum and the high cost of designing satellites, it is difficult to launch a dedicated satellite for IoT communications. To facilitate IoT communications over LEO SatCom, in this paper, we propose the cognitive LEO satellite system, where the IoT users act as the secondary user to access the legacy LEO satellites and cognitively use the spectrum of the legacy LEO users. Due to the flexibility of code division multiple access (CDMA) in multiple access and the wide use of CDMA in LEO SatCom, we apply CDMA to support cognitive satellite IoT communications. For the cognitive LEO satellite system, we are interested in the achievable rate analysis and resource allocation. Specifically, considering the randomness of spreading codes, we use the random matrix theory to analyze the asymptotic signal-to-interference-plus-noise ratios (SINRs) and accordingly obtain the achievable rates for both legacy and IoT systems. The power of the legacy and IoT transmissions at the receiver are jointly allocated to maximize the sum rate of the IoT transmission subject to the legacy satellite system performance requirement and the maximum received power constraints. We prove that the sum rate of the IoT users is quasi-concave over the satellite terminal receive power, based on which the optimal receive powers for these two systems are derived. Finally, the resource allocation scheme proposed in this paper has been verified by extensive simulations.

This paper studies the realization of wireless video transmission by leveraging 5G mixed mode with multimedia broadcast multicast services (MBMS). In particular, it investigates a number of key elements, such as physical layer modeling and precoding strategies, for MBMS implementation with large-scale multi-input multi-output (MIMO). A novel hybrid 5G mixed mode system is proposed to seamlessly integrate unicast and multicast transmissions, wherein system architecture, user grouping strategies, interference mitigation techniques, and optimized multicast beamforming approach are comprehensively elucidated. The performance of our proposed system is assessed through comprehensive simulations and analysis. The results indicate significant improvements in coding and spectral efficiencies while combining MIMO with layer division multiplexing (LDM).

Wi-Fi-based human activity recognition has attracted broad attention for its advantages, which include being device-free, privacy-protected, unaffected by light, etc. Owing to the development of artificial intelligence techniques, existing methods have made great improvements in sensing accuracy. However, the performance of multi-location recognition is still a challenging issue. According to the principle of wireless sensing, wireless signals that characterize activity are also seriously affected by location variations. Existing solutions depend on adequate data samples at different locations, which are labor-intensive. To solve the above concerns, we present an amplitude- and phase-enhanced deep complex network (AP-DCN)-based multi-location human activity recognition method, which can fully utilize the amplitude and phase information simultaneously so as to mine more abundant information from limited data samples. Furthermore, considering the unbalanced sample number at different locations, we propose a perception method based on the deep complex network-transfer learning (DCN-TL) structure, which effectively realizes knowledge sharing among various locations. To fully evaluate the performance of the proposed method, comprehensive experiments have been carried out with a dataset collected in an office environment with 24 locations and five activities. The experimental results illustrate that the approaches can achieve 96.85% and 94.02% recognition accuracy, respectively.