In this paper, we investigate task offloading and computing in an urban mobile edge computing (MEC) system assisted by an aerial intelligent reflecting surface (AIRS). To enhance information freshness while saving energy, we formulate a joint optimization problem to minimize the weighted sum of average age of information (AoI) and total energy consumption by jointly optimizing task offloading decisions, resource allocation, and AIRS configuration including its deployment position, phase shifts, and panel size, subject to offloading quality and computing deadline constraints. To tackle the resulting mixed-integer and nonconvex problem, we develop a hierarchical optimization framework based on the objective priority and variable coupling relations. Under this framework, the problem is solved in two stages using quadratic penalty, numerical analysis, and convex optimization techniques. Specifically, an AoI-aware task offloading policy is first designed to maximize information freshness; subsequently, given the obtained offloading policy, an AIRS configuration and resource allocation scheme is proposed to minimize energy consumption. Simulation results demonstrate that the proposed approach significantly outperforms benchmark schemes in reducing both AoI and energy consumption, while exhibiting superior convergence performance.

The deployment of space–air–ground integrated networks (SAGIN) is critical for addressing signal coverage challenges in high-speed railway (HSR) tunnel scenarios. However, when aerial access networks and ground base stations (BSs) simultaneously serve trains, co-frequency interference can severely degrade system performance, preventing the satisfaction of Quality of Service (QoS) requirements for flows. To address this issue, this article formulates an optimization problem aimed at maximizing the number of scheduled flows through transmission optimization for HSR tunnel communications in SAGIN. Subsequently, a link selection algorithm is proposed to identify valid transmitter-receiver associations by filtering potential link combinations based on the signal-to-interference-plus-noise ratio (SINR) threshold, ensuring that only feasible associations are selected to meet each flow’s QoS requirement. To improve the number of successfully scheduled flows, a graph theory-based transmission optimization (GTTO) algorithm is developed. This approach effectively avoids the simultaneous transmission of highly interfering links by introducing an interference factor and reorders the scheduling sequence of flows to prioritize those associated with links that have higher SINR. The proposed method is evaluated through simulations, demonstrating its ability to substantially improve the number of scheduled flows and system throughput under varying conditions, such as different train speeds, QoS requirements, airship altitudes, tunnel lengths, and the number of mobile relays (MRs). The results also demonstrate the superiority of the proposed method over conventional approaches, achieving reliable communication and enhanced performance across HSR tunnel scenarios.

In the space-air-ground integrated network (SAGIN), high-speed railway (HSR) communication is expected to be enhanced by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS). However, when both aerial platforms and ground base stations (BSs) provide services to HSR user equipments (UEs), severe signal interference can arise, leading to the system failing to meet the quality of service (QoS) requirements of the flows. In this paper, we introduce the optimization problem of transmission scheduling for STAR-RIS assisted train-to-ground communications in SAGIN. To address this issue, a phase optimization algorithm of passive STAR-RIS is proposed to improve the channel quality of HSR UEs. Furthermore, a coalition game algorithm is proposed to associate HSR UEs with the optimal links that minimize inter-flow interference. Finally, a QoS-aware flow scheduling algorithm is proposed to optimize the order of the flows for the selected links. Simulation results confirm that the proposed scheduling scheme effectively increases the number of completed flows and total transmitted bits for STAR-RIS assisted train-to-ground communications in SAGIN, outperforming traditional methods.