RIS-Assisted Joint Resource Allocation for 6G FR3 IoT Networks

TL;DR

This paper proposes a RIS-assisted resource allocation framework for 6G FR3 IoT networks, optimizing power and user association to enhance coverage and sum rate in dense environments.

Contribution

It introduces a joint power and user association scheme using SCA and matching theory for RIS-assisted 6G IoT networks, addressing nonconvex optimization challenges.

Findings

Significant sum-rate improvements over greedy and random schemes.

Effective RIS deployment enhances coverage and signal quality.

Proposed multiphase framework efficiently manages interference and resource allocation.

Abstract

In sixth-generation (6G) networks, the deployment of large numbers of Internet of Things (IoT) users (IU) necessitates efficient resource utilization and reliable connectivity, making resource allocation a critical factor. Specifically, the upper mid-band (FR3) spectrum has emerged as a promising candidate for 6G systems due to its favorable balance between bandwidth availability and coverage. However, translating these spectral advantages into performance gains in dense IoT environments requires intelligent management of interference and propagation impairments. In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted IoT network operating in the FR3 band to enhance coverage and improve signal quality. Furthermore, we formulate a joint power allocation and IU-RIS association problem to maximize the achievable sum rate under practical channel conditions and power constraints. The resulting problem is nonconvex and combinatorial due to interference coupling and binary association variables. To address this challenge, we develop a multiphase resource allocation framework that integrates a successive convex approximation (SCA)-based power allocation scheme combined with a matching-theory-based user association algorithm. Simulation results demonstrate that the proposed scheme significantly outperforms conventional greedy and random search schemes in terms of sum-rate enhancement.