Cache-Aware Cooperative Multicast Beamforming in Dynamic Satellite-Terrestrial Networks

2025-01-01

IEEE Transactions on Vehicular Technology

AI Summary

To address backhaul congestion, degraded QoS, and increased power consumption in low-Earth-orbit (LEO) satellite–terrestrial integrated networks, this paper proposes a dynamic optimization framework jointly orchestrating cache deployment, satellite beam pointing, and satellite–terrestrial cooperative multicast beamforming. The framework features a novel hybrid-timescale two-stage architecture: short-term optimization of content delivery and beamforming, and long-term iterative cache update based on historical access patterns. For the first time, whale optimization algorithm (WOA) is integrated with successive convex approximation (SCA) to devise an efficient and stable alternating optimization algorithm. Experimental results demonstrate that, while guaranteeing user QoS, the proposed method reduces backhaul traffic and transmit power consumption by up to 52%, significantly enhancing edge resource utilization efficiency and service reliability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Algorithm Configuration
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

With the burgeoning demand for data-intensive services, satellite-terrestrial networks (STNs) face increasing backhaul link congestion, deteriorating user quality of service (QoS), and escalating power consumption. Cache-aided STNs are acknowledged as a promising paradigm for accelerating content delivery to users and alleviating the load of backhaul links. However, the dynamic nature of low earth orbit (LEO) satellites and the complex interference among satellite beams and terrestrial base stations pose challenges in effectively managing limited edge resources. To address these issues, this paper proposes a method for dynamically scheduling caching and communication resources, aiming to reduce network costs in terms of transmission power consumption and backhaul traffic, while meeting user QoS demands and resource constraints. We formulate a mixed timescale problem to jointly optimize cache placement, LEO satellite beam direction, and cooperative multicast beamforming among satellite beams and base stations. To tackle this intricate problem, we propose a two-stage solution framework, where the primary problem is decoupled into a short-term content delivery subproblem and a long-term cache placement subproblem. The former subproblem is solved by designing an alternating optimization approach with whale optimization and successive convex approximation methods according to the cache placement state, while cache content in STNs is updated using an iterative algorithm that utilizes historical information. Simulation results demonstrate the effectiveness of our proposed algorithms, showcasing their convergence and significantly reducing transmission power consumption and backhaul traffic by up to 52%.

Problem

• Reducing backhaul congestion in satellite-terrestrial networks
• Optimizing cache and communication resource management
• Minimizing power consumption while meeting QoS demands

Innovation

• Dynamic cache and communication resource scheduling
• Two-stage optimization with whale and convex methods
• Cooperative multicast beamforming in satellite networks