User association method improves resource use in satellite networks
Multi-Granularity Communication-Computing Resource Mapping for User Association in MEO-Assisted LEO Satellite Networks
Networking and Internet Architecture
Summary
Satellite networks with many satellites need to decide which satellite should serve each user, considering communication quality and computing resources like CPUs and memory. The authors study a system where medium-Earth satellites help collect status data, and a control center assigns users to low-Earth satellites with computing capability. They create a method that turns user requests into resource needs for specific satellites and use algorithms to optimally assign users. Their experiments with realistic data show better resource use and fewer service interruptions compared to simpler methods.
What this means in practice
- •For satellite network operators: Assign users efficiently to satellites in LEO networks to improve service reliability and resource utilization using the proposed multi-granularity mapping and algorithms.
- •For network resource managers: Optimize allocation of communication and computing resources under telemetry delay and load variability for satellites equipped with onboard execution capability.
Authors
Yi Jing, Chunxiao Jiang, Jiawei Wang, Jiachen Sun, Yafeng Zhan
Abstract
Low-Earth-orbit (LEO) satellite networks are evolving into spaceborne communication-computing infrastructures. Their user association must jointly account for the radio link and the CPU, GPU, NPU, memory, cache, storage, energy, queue, and thermal states that determine executable service capacity. This paper studies a MEO-assisted LEO architecture in which medium-Earth-orbit (MEO) regional state aggregators collect and relay time-stamped telemetry, the network operation control center (NOCC) maintains the global logical resource view and makes the final association decisions, and LEO satellites provide access and onboard execution. We develop a multigranularity representation and service-primitive-driven mapping that converts a service-level request into a target-LEO-dependent quantized resource vector. The integer formulation, exact mixed-integer linear programming (MILP) reference, and a capacity-aware primal-dual algorithm share the same mapped objective. Experiments use real-world service bandwidth traces, a 1584-satellite LEO shell, multiple traffic seeds, controlled load and mapping error, delayed telemetry, and temporal sessions. Relative to static mapping, the proposed method improves realized utility by 10.64% and lowers end-to-end outage by 5.70 percentage points. Target quantization reduces signaling by 19.84% with negligible utility loss. Additional load, delay, temporal, runtime, and exact-MILP audits delimit the operating region and remaining mapping uncertainty.