Joint Beam Hopping and Caching Optimization in Integrated Terrestrial-Satellite Backhaul Networks

Integrated Terrestrial-Satellite Backhaul Networks (ITSBN) offer wide coverage and cost effectiveness, especially in remote or underserved areas, where backhaul links are facilitated through a multi-beam mechanism. In this context, a Beam Hopping Plan (BHP) can enhance resource efficiency by dynamically activating beams based on traffic demand, improving coverage flexibility. However, despite its several advantages, ITSBN may encounter congestion challenges during periods of high traffic demand. Content caching may be a solution to alleviate the network load, enabling content delivery at the network edge and reducing backhaul traffic. In this work, we consider ITSBN with caching at local base stations (BSs) and aim to maximize the system throughput. To achieve this, we propose a double-layer iterative algorithm. The outer layer employs our proposed <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon$</tex>-Beam Hopping Plan and Content Selection algorithm (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon$</tex>-BHPCS), leveraging the matching game approach. This method jointly designs the BHP and selects content carried by each beam, balancing accuracy and execution time through the parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon \in[0, 1]$</tex>. For inner layer, we propose the Alternating Direction Method of Multipliers Based Power Allocation and Caching (ADMM-PAC) scheme, optimizing power across beams and managing caching at BSs in a distributed fashion, using BSs' resources and local information for parallel optimization processes without requiring global information as in a centralized method. Simulations show that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon$</tex>-BHPCS achieves performance similar to the optimal exhaustive search, while the distributed ADMM-PAC aligns closely with standard centralized interior-point optimization algorithm.