Bo Hu’s research while affiliated with Beijing University of Posts and Telecommunications and other places

High-altitude platform (HAP) drones and satellites collaborate to form a network that provides edge computing services to terrestrial internet of things (IoT) devices, which is considered a promising method. In this network, IoT devices’ tasks can be split into multiple parts and processed by servers at non-terrestrial nodes in different locations, thereby reducing task processing delays. However, splitting tasks and allocating communication and computing resources are important challenges. In this paper, we investigate the task offloading and resource allocation problem in multi-HAP drones and multi-satellite collaborative networks. In particular, we formulate a task splitting and communication and computing resource optimization problem to minimize the total delay of all IoT devices’ tasks. To solve this problem, we first transform and decompose the original problem into two subproblems. We design a task splitting optimization algorithm based on deep reinforcement learning, which can achieve online task offloading decision-making. This algorithm structurally designs the actor network to ensure that output actions are always valid. Furthermore, we utilize convex optimization methods to optimize the resource allocation subproblem. The simulation results show that our algorithm can effectively converge and significantly reduce the total task processing delay when compared with other baseline algorithms.

Lower Earth Orbit (LEO) satellite becomes an important part of complementing terrestrial communication due to its lower orbital altitude and smaller propagation delay than Geostationary satellite. However, the LEO satellite communication system cannot meet the requirements of users when the satellite-terrestrial link is blocked by obstacles. To solve this problem, we introduce Intelligent reflect surface (IRS) for improving the achievable rate of terrestrial users in LEO satellite communication. We investigated joint IRS scheduling, user scheduling, power and bandwidth allocation (JIRPB) optimization algorithm for improving LEO satellite system throughput. The optimization problem of joint user scheduling and resource allocation is formulated as a non-convex optimization problem. To cope with this problem, the nonconvex optimization problem is divided into resource allocation optimization sub-problem and scheduling optimization sub-problem firstly. Second, we optimize the resource allocation sub-problem via alternating direction multiplier method (ADMM) and scheduling sub-problem via Lagrangian dual method repeatedly. Third, we prove that the proposed resource allocation algorithm based ADMM approaches sublinear convergence theoretically. Finally, we demonstrate that the proposed JIRPB optimization algorithm improves the LEO satellite communication system throughput.

The space–air–ground collaborative network can provide computing service for ground users in remote areas by deploying edge servers on satellites and high-altitude platform (HAP) drones. However, with the growing number of ground devices required to be severed, it becomes imperative to address the issue of spectrum demand for the HAP drone to meet the access of a large number of users. In addition, the long propagation distance between devices and the HAP drone, and between the HAP drone and LEO satellites, will lead to high data transmission energy consumption. Motivated by these factors, we introduce a space–air–ground collaborative network that employs the non-orthogonal multiple access (NOMA) technique, enabling all ground devices to access the HAP drone. Therefore, all devices can share the same communication spectrum. Furthermore, the HAP drone can process part of the ground devices’ tasks locally, and offload the rest to satellites within the visible range for processing. Based on this system, we formulate a weighted energy consumption minimization problem considering power control, computing frequency allocation, and task-offloading decision. The problem is solved by the proposed low-complexity iterative algorithm. Specifically, the original problem is decomposed into interconnected coupled subproblems using the block coordinate descent (BCD) method. The first subproblem is to optimize power control and computing frequency allocation, which is solved by a convex algorithm after a series of transformations. The second subproblem is to make an optimal task-offloading strategy, and we solve it using the concave–convex procedure (CCP)-based algorithm after penalty-based transformation on binary variables. Simulation results verify the convergence and performance of the proposed iterative algorithm compared with the two benchmark algorithms.

The widespread employ of Internet of Things (IoT) devices relies on the massive deployment of sensor nodes and data collection timely. Benefit from development of LEO satellite technology, the LEO Satellite is considered an effective way for achieving wider coverage to terrestrial IoT devices in remote area. However, it is challenging to obtain perfect channel state information (CSI) in LEO satellite system because estimation error and longer round trip time in practice. To overcome this problem, we propose Max-Min Fairness (MMF) robust beamforming Deterministic uncertainty model of Imperfect CSI Convex-concave optimization Algorithm (D-ICCA) and Stochastic uncertainty model of Imperfect CSI Convex-concave optimization Algorithm (S-ICCA) in LEO satellite communication system, respectively. MMF optimization problems are formulated under the constraints of the maximum per-antennas power constraint in the LEO satellite communication system. In deterministic uncertainty model of imperfect CSI, we obtain the lower bound of CSI by the Cauchy-Schwarz inequality firstly. Then, we transform the formulated MMF optimization problem to a series of standard convex problem and solve convex optimization sub-problems by adopting the Alternating Direction Multiplier Method (ADMM) to obtain sub-optimal robust beamforming vectors. In stochastic uncertainty model of imperfect CSI, we model the MMF optimization problem with stochastic phase error and solve the MMF optimization problem via ADMM with closed-from solution to obtain sub-optimal robust beamforming vectors. Finally, simulation results demonstrate that the proposed D-ICCA and S-ICCA beamforming algorithms can achieve better performance than ConADMM and FFA-SCA without robust design.