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

Muddasir Rahim, Irfan Azam, Soumaya Cherkaoui

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.

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

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.

Paper Structure

This paper contains 13 sections, 20 equations, 4 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. Related Works
  3. Contributions
  4. System Model
  5. Network Model
  6. Channel Model
  7. Signal Model
  8. Optimization Problem Formulation
  9. Proposed Resource Allocation Framework
  10. SCA-based Power Optimization
  11. Matching-based IU-RIS Association
  12. Simulation Results and Discussion
  13. Conclusions

Figures (4)

  • Figure 1: System model of RIS-assisted IoT network.
  • Figure 2: Proposed two-phase resource allocation algorithm: Phase 1 performs SCA-based downlink power optimization, while Phase 2 executes matching-based one-to-one IU–RIS association.
  • Figure 3: Achievable network sum rate as a function of the AP power budget.
  • Figure 4: Achievable network sum rate as a function of the number of RIS elements.