Yue Gao’s research while affiliated with Fudan University and other places

Quick response to disasters is crucial for saving lives and reducing loss. This requires low-latency uploading of situation information to the remote command center. Since terrestrial infrastructures are often damaged in disaster areas, non-terrestrial networks (NTNs) are preferable to provide network coverage, and mobile edge computing (MEC) could be integrated to improve the latency performance. Nevertheless, the communications and computing in MEC-enabled NTNs are strongly coupled, which complicates the system design. In this paper, an edge information hub (EIH) that incorporates communication, computing and storage capabilities is proposed to synergize communication and computing and enable systematic design. We first address the joint data scheduling and resource orchestration problem to minimize the latency for uploading sensing data. The problem is solved using an optimal resource orchestration algorithm. On that basis, we propose the principles for resource configuration of the EIH considering payload constraints on size, weight and energy supply. Simulation results demonstrate the superiority of our proposed scheme in reducing the overall upload latency, thus enabling quick emergency rescue.

Quick response to disasters is crucial for saving lives and reducing loss. This requires low-latency uploading of situation information to the remote command center. Since terrestrial infrastructures are often damaged in disaster areas, non-terrestrial networks (NTNs) are preferable to provide network coverage, and mobile edge computing (MEC) could be integrated to improve the latency performance. Nevertheless, the communications and computing in MEC-enabled NTNs are strongly coupled, which complicates the system design. In this paper, an edge information hub (EIH) that incorporates communication, computing and storage capabilities is proposed to synergize communication and computing and enable systematic design. We first address the joint data scheduling and resource orchestration problem to minimize the latency for uploading sensing data. The problem is solved using an optimal resource orchestration algorithm. On that basis, we propose the principles for resource configuration of the EIH considering payload constraints on size, weight and energy supply. Simulation results demonstrate the superiority of our proposed scheme in reducing the overall upload latency, thus enabling quick emergency rescue.

The sixth-generation (6G) network is envisioned to shift its focus from the service requirements of human beings to those of Internet-of-Things (IoT) devices. Satellite communications are indispensable in 6G to support IoT devices operating in rural or disaster areas. However, satellite networks face the inherent challenges of low data rate and large latency, which may not support computation-intensive and delay-sensitive IoT applications. Mobile Edge Computing (MEC) is a burgeoning paradigm by extending cloud computing capabilities to the network edge. Using MEC technologies, the resource-limited IoT devices can access abundant computation resources with low latency, which enables the highly demanding applications while meeting strict delay requirements. Therefore, an integration of satellite communications and MEC technologies is necessary to better enable 6G IoT. In this survey, we provide a holistic overview of satellite-MEC integration. We first categorize the related studies based on three minimal structures and summarize current advances. For each minimal structure, we discuss the lessons learned and possible future directions. We also summarize studies considering the combination of minimal structures. Finally, we outline potential research issues to envision a more intelligent, more secure, and greener integrated satellite-MEC network.

The sixth-generation (6G) network is expected to provide both communication and sensing (C&S) services. However, spectrum scarcity poses a major challenge to the harmonious coexistence of C&S systems. Without effective cooperation, the interference resulting from spectrum sharing impairs the performance of both systems. This paper addresses C&S interference within a distributed network. Different from traditional schemes that require pilot-based high-frequency interactions between C&S systems, we introduce a third party named the radio map to provide the large-scale channel state information (CSI). With large-scale CSI, we optimize the transmit power of C&S systems to maximize the signal-to-interference-plus-noise ratio (SINR) for the radar detection, while meeting the ergodic rate requirement of the interfered user. Given the non-convexity of both the objective and constraint, we employ the techniques of auxiliary-function-based scaling and fractional programming for simplification. Subsequently, we propose an iterative algorithm to solve this problem. Simulation results corroborate our idea that the extrinsic information, i.e., positions and surroundings, is effective to decouple C&S interference.