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Dual-Tier IRS-Assisted Mid-Band 6G Mobile Networks: Robust Beamforming and User Association

Muddasir Rahim, Soumaya Cherkaoui

TL;DR

This paper tackles robust downlink resource allocation for 6G FR3 (7–15 GHz) networks by proposing a dual-tier IRS architecture that combines terrestrial IRS (TIRS) and aerial IRS (AIRS) mounted on low-altitude platforms to mitigate frequent LoS blockages. It formulates a joint beamforming and user association (JBUA) MINLP and solves it via decomposition into a zero-forcing beamforming subproblem and a stable-matching-based association subproblem, enabling low-complexity, near-optimal performance. Results show the JBUA scheme approaches exhaustive-search performance (within roughly 2%) and outperforms greedy and random baselines by significant margins, while scaling effectively with large IRS sizes and network dimensions. The framework provides a practical blueprint for deploying scalable, reliable 6G FR3 networks capable of supporting massive IoT in cluttered environments, with potential extensions to LEO satellites and mobility-aware hybrid beamforming.

Abstract

The rapid growth of Internet of Things (IoT) applications necessitates robust resource allocation in future sixth-generation (6G) networks, particularly at the upper mid-band (7-15 GHz, FR3). This paper presents a novel intelligent reconfigurable surface (IRS)-assisted framework combining terrestrial IRS (TIRS) and aerial IRS (AIRS) mounted on low-altitude platform stations, to ensure reliable connectivity under severe line-of-sight (LoS) blockages. Distinguishing itself from prior work restricted to terrestrial IRS and mmWave and THz bands, this work targets the FR3 spectrum, the so-called Golden Band for 6G. The joint beamforming and user association (JBUA) problem is formulated as a mixed-integer nonlinear program (MINLP), solved through problem decomposition, zero-forcing beamforming, and a stable matching algorithm. Comprehensive simulations show our method approaches exhaustive search performance with significantly lower complexity, outperforming existing greedy and random baselines. These results provide a scalable blueprint for real-world 6G deployments, supporting massive IoT connectivity in challenging environments.

Dual-Tier IRS-Assisted Mid-Band 6G Mobile Networks: Robust Beamforming and User Association

TL;DR

This paper tackles robust downlink resource allocation for 6G FR3 (7–15 GHz) networks by proposing a dual-tier IRS architecture that combines terrestrial IRS (TIRS) and aerial IRS (AIRS) mounted on low-altitude platforms to mitigate frequent LoS blockages. It formulates a joint beamforming and user association (JBUA) MINLP and solves it via decomposition into a zero-forcing beamforming subproblem and a stable-matching-based association subproblem, enabling low-complexity, near-optimal performance. Results show the JBUA scheme approaches exhaustive-search performance (within roughly 2%) and outperforms greedy and random baselines by significant margins, while scaling effectively with large IRS sizes and network dimensions. The framework provides a practical blueprint for deploying scalable, reliable 6G FR3 networks capable of supporting massive IoT in cluttered environments, with potential extensions to LEO satellites and mobility-aware hybrid beamforming.

Abstract

The rapid growth of Internet of Things (IoT) applications necessitates robust resource allocation in future sixth-generation (6G) networks, particularly at the upper mid-band (7-15 GHz, FR3). This paper presents a novel intelligent reconfigurable surface (IRS)-assisted framework combining terrestrial IRS (TIRS) and aerial IRS (AIRS) mounted on low-altitude platform stations, to ensure reliable connectivity under severe line-of-sight (LoS) blockages. Distinguishing itself from prior work restricted to terrestrial IRS and mmWave and THz bands, this work targets the FR3 spectrum, the so-called Golden Band for 6G. The joint beamforming and user association (JBUA) problem is formulated as a mixed-integer nonlinear program (MINLP), solved through problem decomposition, zero-forcing beamforming, and a stable matching algorithm. Comprehensive simulations show our method approaches exhaustive search performance with significantly lower complexity, outperforming existing greedy and random baselines. These results provide a scalable blueprint for real-world 6G deployments, supporting massive IoT connectivity in challenging environments.
Paper Structure (11 sections, 2 theorems, 20 equations, 4 figures, 1 table, 1 algorithm)

This paper contains 11 sections, 2 theorems, 20 equations, 4 figures, 1 table, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. Related Works
  3. Contributions
  4. System Model
  5. Channel Modeling and Data Rate Analysis
  6. Optimization Problem Formulation
  7. Proposed Resource Allocation Solutions
  8. Zero Forcing Beamforming
  9. User-IRS Association
  10. Simulation Results and Discussion
  11. Conclusions

Key Result

Theorem 1

The optimization problem eq_opt_prob is a MINLP and a non-deterministic polynomial-time hard (NP-hard) problem.

Figures (4)

  • Figure 1: System model of IRS-assisted with LAPS and terrestrial layers.
  • Figure 2: Sum rate versus AP power budget.
  • Figure 3: Sum rate versus the number of IRS elements.
  • Figure 4: Sum rate versus network area.

Theorems & Definitions (2)

  • Theorem 1
  • Theorem 2