⚡ electrical engineering

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

This paper proposes a dual RIS-assisted 6G framework for reliable IoT communications that addresses severe blockages by decomposing a complex joint beamforming, power allocation, and device association optimization problem into efficient sub-problems, achieving near-optimal sum rate performance with significantly lower complexity than exhaustive search.

Original authors: Muddasir Rahim, Soumaya Cherkaoui

Published 2026-02-03

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Muddasir Rahim, Soumaya Cherkaoui

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the future internet (6G) as a massive, high-speed highway system designed to carry not just cars, but millions of tiny, data-hungry devices (the "Internet of Things"). The authors of this paper are trying to solve a traffic jam problem on this highway.

Here is the breakdown of their solution using simple analogies:

The Problem: The "Golden Band" is Blocked

The researchers want to use a specific stretch of radio spectrum called the Upper Mid-Band (UMB). Think of this as the "Golden Band" of the radio spectrum. It's the perfect middle ground: it has plenty of room for data (like a wide highway) but travels far enough to be useful.

However, there's a catch. In crowded cities or during disasters, tall buildings and debris block the direct line of sight between the cell tower and your device. It's like trying to drive a car through a city where every street is blocked by a wall. The signal can't get through.

The Solution: A Two-Tier "Mirror" System

To fix this, the authors propose a system using Reconfigurable Intelligent Surfaces (RIS). Think of these RISs as smart, magical mirrors that can catch a signal, bounce it off, and steer it exactly where it needs to go, avoiding the obstacles.

But one layer of mirrors isn't enough. If a building blocks the view from the ground, a mirror on the ground won't help. So, they propose a two-tier system:

Ground Mirrors (TRIS): These are the standard mirrors attached to building walls.
Sky Mirrors (HRIS): These are mirrors mounted on High-Altitude Platform Stations (HAPS)—essentially giant, solar-powered drones or balloons floating high in the sky.

If the ground is blocked, the sky mirrors can look over the buildings and bounce the signal down. If the sky is blocked, the ground mirrors can find a path around the corner. It's like having a team of runners passing a baton; if one path is blocked, the next runner in a different location takes over.

The Challenge: Organizing the Chaos

Now, imagine you have 100 devices, 50 ground mirrors, and 1 sky mirror. You need to decide:

Which device talks to which mirror?
How much power should each device use?
How should the mirrors angle themselves?

If you try to calculate the perfect answer for every single combination, it would take a supercomputer longer than the age of the universe (this is called an "Exhaustive Search"). It's too slow and complicated.

The Authors' Strategy: A Smart Teamwork Approach

Instead of trying to solve the whole puzzle at once, the authors broke it down into three smaller, manageable tasks, solving them one by one in a loop:

The "Silence" Trick (Beamforming): They use a technique called Zero-Forcing. Imagine a conductor at an orchestra. Instead of letting every instrument play loudly and create noise, the conductor tells each musician exactly when to play and how loud, so they don't clash. This cancels out the interference between devices.
The "Fuel" Calculator (Power Allocation): They figured out a simple math formula to decide exactly how much "fuel" (power) each device needs. No more, no less. This ensures no one wastes energy.
The "Dating App" Match (Device Association): This is the most creative part. They used a Stable Matching Algorithm. Imagine a dating app where devices and mirrors have "preference lists."
- A device proposes to the mirror that gives it the best speed.
- The mirror looks at all the proposals it got. If it likes a new proposal better than its current match, it swaps. If not, it says "no."
- This happens quickly until everyone is paired up in a way that no one wants to switch. It's fast, fair, and efficient.

The Results: Fast and Nearly Perfect

The authors tested their system in a computer simulation. Here is what they found:

Speed: Their "dating app" method converged (found a solution) in about 45 steps. It was incredibly fast.
Performance: Their system achieved 98% of the performance of the "Exhaustive Search" (the perfect but impossible-to-calculate solution).
Comparison: It crushed the other methods. It was about 15% better than a "Greedy" approach (where devices just pick the first good option they see) and 57% better than a "Random" approach.

The Bottom Line

The paper claims that by using a mix of ground and sky mirrors, and organizing them with a smart, step-by-step matching system, we can create a 6G network that is incredibly reliable. Even if buildings block the signal or disasters hit the ground infrastructure, the sky mirrors can step in to keep the internet running smoothly for millions of devices.

The authors note that in the future, they might add satellites to this mix and handle moving devices (like cars or phones in motion), but for now, this two-tier mirror system is their proven solution for a robust 6G network.

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

Problem Statement
The rapid expansion of Internet of Things (IoT) applications necessitates robust resource allocation in sixth-generation (6G) networks, particularly within the upper mid-band (UMB) spectrum (7–15 GHz), often termed the "Golden Band" for its balance of bandwidth and propagation characteristics. While the UMB offers significant potential, it suffers from severe propagation losses and frequent line-of-sight (LoS) blockages, especially in dense urban environments or during natural disasters where terrestrial infrastructure may be compromised. Existing solutions relying solely on terrestrial reconfigurable intelligent surfaces (TRISs) often create coverage blind spots. Furthermore, the integration of non-terrestrial networks (NTNs), such as High-Altitude Platform Systems (HAPS), introduces complex resource allocation challenges in multi-tier, heterogeneous architectures. The core problem addressed is the maximization of network sum rate in a UMB-based IoT network utilizing a dual-layer RIS structure (TRISs and HAPS-mounted RISs or HRISs) under severe LoS blockage conditions.

Methodology
The authors propose a joint beamforming, power allocation, and IoT device association (JBPDA) framework to maximize the network sum rate. The system model consists of a multi-antenna Access Point (AP) serving single-antenna IoT devices via a two-tier RIS architecture comprising $L$ RISs (a mix of TRISs and HRISs). The direct AP-to-device links are assumed unavailable due to blockages.

The optimization problem is formulated as a Mixed-Integer Nonlinear Program (MINLP), which is NP-hard and difficult to solve directly. To address this, the authors employ a decomposition approach, breaking the problem into three iterative sub-problems:

Beamforming Optimization: The authors utilize Zero-Forcing (ZF) beamforming at the AP to eliminate multi-user interference. The beamforming matrix is derived using the pseudo-inverse of the cascaded channel matrix.
Power Allocation: With the beamforming matrix fixed, the power allocation problem is reformulated as a convex optimization problem. A closed-form expression for optimal power allocation is derived using Karush-Kuhn-Tucker (KKT) conditions.
Device-RIS Association: The association of IoT devices to RISs is modeled as a one-to-one matching problem. The authors propose a device-proposing deferred-acceptance algorithm based on matching theory. In this algorithm, devices propose to RISs based on achievable data rates, and RISs tentatively accept the best proposal, rejecting others until a stable matching is reached.

Key Contributions

Novel Architecture: This work presents the first study analyzing the integration of TRISs and HRISs specifically for UMB communications in 6G IoT networks, addressing the gap in existing literature which has largely focused on mmWave/terahertz bands or terrestrial-only RIS deployments.
Algorithmic Solution: The paper formulates the JBPDA problem and decomposes it into tractable sub-problems. It provides a specific solution involving ZF beamforming, a closed-form power allocation expression, and a stable matching-based association algorithm.
Efficiency: The proposed matching algorithm is proven to terminate in polynomial time with a complexity of $O(K \times L)$ , where $K$ is the number of devices and $L$ is the number of RISs.

Simulation Results
Comprehensive simulations were conducted using MATLAB with parameters including a 15 GHz carrier frequency, 256 AP antennas, and RIS arrays of $100 \times 100$ elements. The proposed JBPDA scheme was benchmarked against Exhaustive Search (ES), Greedy Search (GS), and Random Search (RS).

Convergence: The proposed scheme demonstrates rapid convergence, reaching stability within approximately 45 iterations for 50 IoT devices.
Performance vs. Baselines: The JBPDA scheme consistently outperforms GS and RS. Specifically, it achieves sum rates approximately 15% higher than GS and 57% higher than RS across the tested power range.
Optimality: The proposed scheme approaches the performance of the Exhaustive Search (ES) baseline, achieving results within 2% of the ES optimal solution while maintaining significantly lower computational complexity.
Scalability: As the number of IoT devices increases (up to 200) and the number of AP antennas increases (up to 256), the JBPDA scheme maintains superior performance, demonstrating effective scalability in dense IoT scenarios.

Significance and Claims
The paper claims that the proposed multi-layer RIS-assisted framework effectively mitigates LoS blockages and enhances connectivity reliability for future 6G IoT deployments in the UMB. The significance lies in providing a computationally efficient resource allocation strategy that achieves near-optimal network sum rates without the prohibitive complexity of exhaustive search. The authors assert that this approach validates the effectiveness of integrating terrestrial and non-terrestrial RIS layers.

The paper concludes modestly, noting that while the current work addresses static scenarios, future extensions will investigate integrating Low Earth Orbit (LEO) satellites, hybrid beamforming, and the impact of IoT device mobility, including tracking, Doppler effects, and handover mechanisms.