Hierarchical RIS-Aided Resource Allocation for FR3 Satellite–Terrestrial Integrated IoT Networks

Abstract

The massive deployment of Internet of Things (IoT) devices in sixth-generation (6G) networks requires efficient resource utilization, seamless connectivity, and extended coverage. Although terrestrial infrastructures can effectively serve most IoT users (IUs), communication reliability may be severely degraded in remote or blocked environments. To address this challenge, this paper proposes a reconfigurable intelligent surface (RIS)-aided satellite-terrestrial integrated networks (STINs) operating in the upper mid-band (FR3) spectrum. The considered network consists of a terrestrial access point (AP), a low Earth orbit (LEO) satellite, multiple unmanned aerial vehicle-mounted RISs (URISs), and terrestrial RISs (TRISs), where the AP serves as the primary access node and the LEO satellite provides complementary connectivity when terrestrial links are unavailable or unable to satisfy the required quality-of-service (QoS) requirements. Within this framework, we formulate a joint transmit power allocation and RIS association problem to maximize the network achievable sum rate under practical power and association constraints. The resulting optimization problem is a mixed-integer nonconvex problem due to the interference-coupled rate expressions and binary association variables. To efficiently solve the problem, we develop a two-phase resource allocation framework that integrates successive convex approximation (SCA)-based power optimization with a many-to-one matching-theory-based IU-RIS association algorithm. Specifically, AP-aided users are associated with TRISs, whereas LEO-aided users are associated with URISs. Simulation results demonstrate that the proposed framework achieves near-optimal performance, closely approaching that of the exhaustive search scheme, while significantly outperforming greedy and random association schemes in terms of network sum rate.