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Reliable IoT Communications in 6G Non-Terrestrial Networks with Dual RIS

Muddasir Rahim, Soumaya Cherkaoui

TL;DR

This work addresses reliable IoT communications in 6G upper mid-band non-terrestrial networks by introducing a two-tier RIS architecture that combines terrestrial RISs (TRIS) and HRISs mounted on HAPS to overcome LoS blockages. The authors formulate a joint beamforming, power allocation, and IoT device association problem (JBPDA) as a mixed-integer nonlinear program and solve it via decomposition into subproblems: zero-forcing beamforming, closed-form power allocation, and a stable-matching device–RIS association. The proposed JBPDA achieves sum-rate performance within about 2% of an exhaustive search while markedly outperforming greedy and random baselines and converging rapidly (e.g., ~45 iterations for moderate network sizes). This demonstrates the viability of multi-layer RISs for robust, scalable 6G IoT connectivity and provides a foundation for extensions to LEO segments and mobility-aware operation.

Abstract

The increasing demand for Internet of Things (IoT) applications has accelerated the need for robust resource allocation in sixth-generation (6G) networks. In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted upper mid-band communication framework. To ensure robust connectivity under severe line-of-sight (LoS) blockages, we use a two-tier RIS structure comprising terrestrial RISs (TRISs) and high-altitude platform station (HAPS)-mounted RISs (HRISs). To maximize network sum rate, we formulate a joint beamforming, power allocation, and IoT device association (JBPDA) problem as a mixed-integer nonlinear program (MINLP). The formulated MINLP problem is challenging to solve directly; therefore, we tackle it via a decomposition approach. The zero-forcing (ZF) technique is used to optimize the beamforming matrix, a closed-form expression for power allocation is derived, and a stable matching-based algorithm is proposed for device-RIS association based on achievable data rates. Comprehensive simulations demonstrate that the proposed scheme approaches the performance of exhaustive search (ES) while exhibiting substantially lower complexity, and it consistently outperforms greedy search (GS) and random search (RS) baselines. Moreover, the proposed scheme converges much faster than the ES scheme.

Reliable IoT Communications in 6G Non-Terrestrial Networks with Dual RIS

TL;DR

This work addresses reliable IoT communications in 6G upper mid-band non-terrestrial networks by introducing a two-tier RIS architecture that combines terrestrial RISs (TRIS) and HRISs mounted on HAPS to overcome LoS blockages. The authors formulate a joint beamforming, power allocation, and IoT device association problem (JBPDA) as a mixed-integer nonlinear program and solve it via decomposition into subproblems: zero-forcing beamforming, closed-form power allocation, and a stable-matching device–RIS association. The proposed JBPDA achieves sum-rate performance within about 2% of an exhaustive search while markedly outperforming greedy and random baselines and converging rapidly (e.g., ~45 iterations for moderate network sizes). This demonstrates the viability of multi-layer RISs for robust, scalable 6G IoT connectivity and provides a foundation for extensions to LEO segments and mobility-aware operation.

Abstract

The increasing demand for Internet of Things (IoT) applications has accelerated the need for robust resource allocation in sixth-generation (6G) networks. In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted upper mid-band communication framework. To ensure robust connectivity under severe line-of-sight (LoS) blockages, we use a two-tier RIS structure comprising terrestrial RISs (TRISs) and high-altitude platform station (HAPS)-mounted RISs (HRISs). To maximize network sum rate, we formulate a joint beamforming, power allocation, and IoT device association (JBPDA) problem as a mixed-integer nonlinear program (MINLP). The formulated MINLP problem is challenging to solve directly; therefore, we tackle it via a decomposition approach. The zero-forcing (ZF) technique is used to optimize the beamforming matrix, a closed-form expression for power allocation is derived, and a stable matching-based algorithm is proposed for device-RIS association based on achievable data rates. Comprehensive simulations demonstrate that the proposed scheme approaches the performance of exhaustive search (ES) while exhibiting substantially lower complexity, and it consistently outperforms greedy search (GS) and random search (RS) baselines. Moreover, the proposed scheme converges much faster than the ES scheme.
Paper Structure (12 sections, 2 theorems, 25 equations, 5 figures, 1 table, 1 algorithm)

This paper contains 12 sections, 2 theorems, 25 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

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

Key Result

Theorem 1

The closed-form equation for the power allocation problem $\boldsymbol{\mathrm{P2}}$ in P2.1 can be written as where $[x]^+ = \max \{x,0\}$, and $\mu$, and $\varrho_k$ represent the Lagrangian multipliers. The multiplier $\mu$ is chosen to ensure that $\sum_{k \in K} p_{k}\leq P_{\mathtt{AP}}$ and $\varrho_k$ ensures the non-negative condition $p_{k}\geq 0$.

Figures (5)

  • Figure 1: System model of RIS-assisted with terrestrial and HAPS layers
  • Figure 2: Convergence behavior of the proposed scheme in terms of the sum rate versus the number of iterations for $K=50$ and $M=100\times100$.
  • Figure 3: Sum rate versus AP power budget for $K=7$ and $M=100\times100$.
  • Figure 4: Sum rate versus number of IoT devices for $M=100\times 100$ and $N=256$.
  • Figure 5: Sum rate versus number of AP antennas for $K=100$ and $M=100\times 100$.

Theorems & Definitions (2)

  • Theorem 1
  • Theorem 2