Beijing University of Posts and Telecommunications

About: Beijing University of Posts and Telecommunications is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: MIMO & Quality of service. The organization has 39576 authors who have published 41525 publications receiving 403759 citations. The organization is also known as: BUPT.

Resource Allocation Optimization for Device-to-Device Communication Underlaying Cellular Networks

Abstract: Device-to-Device (D2D) communication will become an important technology in future networks with the increase of the requirements of local communication services. The interference between cellular communication and D2D communication can be coordinated by proper power control and resource allocation. In this paper, we analyze the resource allocation methods for D2D communication underlaying cellular networks. A novel resource allocation method that D2D can reuse the resources of more than one cellular user is proposed. After that, we discuss the selection of the optimal resource allocation method from the proposed method and the conventional methods. Finally, the performance of different methods is evaluated through numerical simulation. The simulation results show that the proposed method is the optimal resource allocation method when the D2D pair locates at the most part of the cell area in both uplink and downlink. The proposed method can improve the sum throughput of cellular communication and D2D communication significantly.

Resource Allocation for Multi-UAV Aided IoT NOMA Uplink Transmission Systems

Abstract: Unmanned aerial vehicle (UAV) communication is a promising technology for Internet of Things (IoT) systems. In this paper, we combine UAV communication and nonorthogonal multiple access (NOMA) for constructing high capacity IoT uplink transmission systems, where UAVs are used as aerial base stations for collecting data from IoT nodes while NOMA is invoked for uplink transmission. We aim to maximize the system capacity by jointly optimize the subchannel assignment, the uplink transmit power of IoT nodes, and the flying heights of UAVs. We commence by proposing an efficient subchannel assignment algorithm relying on the classic ${K}$ -means clustering method and matching theory. Then, we determine both the distributed uplink transmit power of IoT nodes and flying heights of UAVs based on successive optimization approach. An alternative optimization algorithm is also proposed for finding the near-optimal solutions. Finally, the numerical results demonstrate the superiority of our proposed scheme.

Blockchain-Based Software-Defined Industrial Internet of Things: A Dueling Deep ${Q}$ -Learning Approach

Abstract: With the developments of communication technologies and smart manufacturing, Industrial Internet of Things (IIoT) has emerged. Software-defined networking (SDN), a promising paradigm shift, has provided a viable way to manage IIoT dynamically, called software-defined IIoT (SDIIoT). In SDIIoT, lots of data and flows are generated by industrial devices, where a physically distributed but logically centralized control plane is necessary. However, one of the most intractable problems is how to reach consensus among multiple controllers under complex industrial environments. In this paper, we propose a blockchain (BC)-based consensus protocol in SDIIoT, along with detailed consensus steps and theoretical analysis, where BC works as a trusted third party to collect and synchronize network-wide views between different SDN controllers. Specially, it is a permissioned BC. In order to improve the throughput of this BC-based SDIIoT, we jointly consider the trust features of BC nodes and controllers, as well as the computational capability of the BC system. Accordingly, we formulate view change, access selection, and computational resources allocation as a joint optimization problem. We describe this problem as a Markov decision process by defining state space, action space, and reward function. Due to the fact that it is difficult to solve this joint problem by traditional methods, we propose a novel dueling deep ${Q}$ -learning approach. Simulation results are presented to show the effectiveness of our proposed scheme.

Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs

Abstract: Cellular-connected UAVs will inevitably be integrated into future cellular networks as new aerial mobile users. Providing cellular connectivity to UAVs will enable a myriad of applications ranging from online video streaming to medical delivery. However, to enable reliable wireless connectivity for the UAVs as well as secure operation, various challenges need to be addressed such as interference management, mobility management and handover, cyber-physical attacks, and authentication. In this article, the goal is to expose the wireless and security challenges that arise in the context of UAV-based delivery systems, UAV-based real-time multimedia streaming, and UAV-enabled intelligent transportation systems. To address such challenges, ANN-based solution schemes are introduced. The introduced approaches enable UAVs to adaptively exploit wireless system resources while guaranteeing secure operation in real time. Preliminary simulation results show the benefits of the introduced solutions for each of the aforementioned cellular-connected UAV application use cases.