With the advance of unmanned aerial vehicles (UAVs) and low earth orbit (LEO) satellites, the integration of space, air and ground networks has become a potential solution to the beyond fifth generation (B5G) Internet of remote things (IoRT) networks. However, due to the network heterogeneity and the high mobility of UAVs and LEOs, how to design an efficient UAV-LEO integrated data collection scheme without infrastructure support is very challenging. In this paper, we investigate the resource allocation problem for a two-hop uplink UAV-LEO integrated data collection for the B5G IoRT networks, where numerous UAVs gather data from IoT devices and transmit the IoT data to LEO satellites. In order to maximize the data gathering efficiency in the IoT-UAV data gathering process, we study the bandwidth allocation of IoT devices and the 3-dimensional (3D) trajectory design of UAVs. In the UAV-LEO data transmission process, we jointly optimize the transmit powers of UAVs and the selections of LEO satellites for the total uploaded data amount and the energy consumption of UAVs. Considering the relay role and the cache capacity limitations of UAVs, we merge the optimizations of IoT-UAV data gathering and UAV-LEO data transmission into an integrated optimization problem, which is solved with the aid of the successive convex approximation (SCA) and the block coordinate descent (BCD) techniques. Simulation results demonstrate that the proposed scheme achieves better performance than the benchmark algorithms in terms of both energy consumption and total upload data amount.

With the rapid development of low earth orbit (LEO) satellites, the ultra-dense LEO satellite-terrestrial integrated networking has become a promising paradigm to provide wide coverage, high capacity and flexible services for the sixth generation (6G) mobile communication networks. However, the natural heterogeneity, high dynamics, and large scale of ultradense LEO satellite networks pose new challenges to network control and management. To this end, in this article, we propose a medium earth orbit (MEO), low earth orbit (LEO) and satellite Earth stations (SESs) integrated multi-layered management architecture for the ultra-dense LEO satellite network, where global controllers and local controllers are introduced to reduce the network management complexity. With the aid of this hybrid multi-layered management framework, we can efficiently implement the network status control, mobility management, resource management, and service management in the satelliteterrestrial integrated network. To obtain proper global and local controllers with high management efficiency in the hybrid multilayered network management architecture, we further propose a novel and efficient grouping and clustering method for the ultradense LEO satellite network, where each group manager MEO satellite and cluster head (CH) LEO satellites are respectively considered as the global and local controllers of each LEO satellite group. Numerical simulations are carried out to evaluate the superiority and effectiveness of the proposed hybrid multilayered ultra-dense LEO networking management architecture.

With a massive number of Internet-of-Things (IoT) devices connecting with the Internet via 5G or beyond 5G (B5G) wireless networks, how to support massive access for coexisting cellular users and IoT devices with quality-of-service (QoS) guarantees over limited radio spectrum is one of the main challenges. In this paper, we investigate the multi-operator dynamic spectrum sharing problem to support the coexistence of rate guaranteed cellular users and massive IoT devices. For the spectrum sharing among mobile network operators (MNOs), we introduce a wireless spectrum provider (WSP) to make spectrum trading with MNOs through the Stackelberg pricing game. This framework is inspired by the active radio access network (RAN) sharing architecture of 3GPP, which is regarded as a promising solution for MNOs to improve the resource utilization and reduce deployment and operation cost. For the coexistence of cellular users and IoT devices under each MNO, we propose the coexisting access rules to ensure their QoS and the priority of cellular users. In particular, we prove the uniqueness of the Stackelberg equilibrium (SE) solution, which can maximize the payoffs of MNOs and WSP simultaneously. Moreover, we propose an iterative algorithm for the Stackelberg pricing game, which is proved to achieve the unique SE solution. Extensive numerical simulations demonstrate that, the payoffs of WSP and MNOs are maximized and the SE solution can be reached. Meanwhile, the proposed multi-operator dynamic spectrum sharing algorithm can support more than almost 40% IoT devices compared with the existing no-sharing method, and the gap is less than about 10% compared with the exhaustive method.

With the significant advance of vehicle-to-everything (V2X) techniques, unsignalized intersection coordination has been widely recognized to facilitate the development of automated vehicles (AVs) for the intelligent transportation system. However, how to guarantee driving safety while improving the unsignalized intersection management efficiency is a challenging issue. In this paper, we investigate the collision-free and efficient V2X-enabled AV scheduling problem at unsignalized intersections. First, by dividing the intersection zone into different collision sections (CSs), we formulate the intersection collision-free model into an absolute value programming (AVP) problem, which is proved to be NP-hard. We consider both the non-platoon and platoon traffic scenarios, and unlike previous algorithms, which require to control all the AVs at each scheduling step with computational intractability, our scheduling algorithm can assign a feasible time for each arriving AV with low complexity. Further, we propose an alternately iterative descent method (AIDM) to solve the AVP problem by assigning the optimal entering time for each arriving AV. Through extensive simulations with various traffic data generated by SUMO, we demonstrate that our proposed AIDM algorithm can significantly enhance the scheduling performance in terms of passing delay and scheduling throughput. Even though the AIDM algorithm achieves the same level of transportation performances with the state-of-the-art algorithm, it advances dramatically in computational complexity and communication overhead, which is easier to be implemented in practice.

While the commercial deployment and promotion of 5G is ongoing, mobile communication networks are still facing three fundamental challenges, i.e., spectrum resource scarcity, especially for low-frequency spectrum, exacerbated by fragmented spectrum allocation, user-centric network service provision when facing billions of personalized user demands in the era of Internet of everything (IoE), and proliferating operation costs mainly due to huge energy consumption of network infrastructure. To address these issues, it is imperative to consider and develop disruptive technologies in the next generation mobile communication networks, namely 6G. In this paper, by studying brain neurons and the neurotransmission, we propose the fully-decoupled radio access network (FD-RAN). In the FD-RAN, base stations (BSs) are physically decoupled into control BSs and data BSs, and the data BSs are further physically split into uplink BSs and downlink BSs. We first review the fundamentals of neurotransmission and then propose the 6G design principles inspired by the neurotransmission. Based on the principles, we propose the FD-RAN architecture, elastic resource cooperation in FD-RAN, and improved transport service layer design. The proposed fully decoupled and flexible architecture can profoundly facilitate resource cooperation to enhance the spectrum utilization, reduce the network energy consumption and improve the quality of user experience. Future research topics in this direction are envisioned and discussed.

Vehicular ad hoc network (VANET) plays a promising role to support diverse intelligent transportation system (ITS) applications in 5G networks, namely 5G-VANET. There are three types of communication modes in 5G-VANET: cellular mode, reuse mode and dedicated mode, i.e., vehicle users (VUEs) communicates with each other using the cellular network spectrum directly, in an underlay sharing way, and utilizing the allocated dedicated spectrum, respectively. However, how to dynamically share the multi-mode spectrum to optimize the network performance (i.e., network throughput) in 5G-VANET is a challenging task due to the high dynamic VANET environment and network resource heterogeneity. In this paper, we propose a dynamic Stackelberg pricing game enabled multi-mode spectrum sharing solution in 5G-VANET. In specific, we develop an access price strategy for different spectrum sharing modes considering the cellular BS's revenue and whole network throughput, while VUEs can select communication modes in a distributed way and dynamically change the selections through an evolutionary game. Through testing different traffic scenarios generated by SUMO, we demonstrate the effectiveness of the proposed algorithm. Specifically, the proposed algorithm can improve the total transmission rate of VANET by at least 20% compared with the random selection method.