💻 computer science

RIS-Assisted Proactive Handover for Reliable mmWave Wireless Networks

This paper proposes a novel RIS-assisted proactive handover framework for mmWave networks that utilizes particle swarm optimization to determine the optimal number of RIS elements offline, thereby balancing signal quality, energy efficiency, and handover timing constraints to mitigate line-of-sight blockages.

Original authors: Alaa Adnan, Mohammad Al-Quraan, Ahmed Zoha, M. Majid Butt, Sami Muhaidat, Muhammad Ali Imran, Marco Di Renzo, Lina Mohjazi

Published 2026-06-26

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CC BY 4.0

Original authors: Alaa Adnan, Mohammad Al-Quraan, Ahmed Zoha, M. Majid Butt, Sami Muhaidat, Muhammad Ali Imran, Marco Di Renzo, Lina Mohjazi

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

The Big Problem: The "Highway" Gets Blocked

Imagine your phone's internet connection (specifically the super-fast 5G/6G kind called mmWave) is like a high-speed car driving down a straight, empty highway. This highway is great because it's fast, but it has a major flaw: it only works if the road is perfectly straight. If a giant truck (a building, a bus, or a person) pulls in front of the car, the signal stops instantly. The car crashes, and the connection is lost.

In the past, when this happened, the system would try to find a different highway (a different cell tower) to reroute the car. But what if there are no other highways nearby? The car is stuck.

The Solution: A "Magic Mirror" (RIS)

This paper proposes a clever fix: instead of finding a new highway, we build a Magic Mirror (called a Reconfigurable Intelligent Surface, or RIS) on the side of a building.

When the "truck" blocks the direct road, this Magic Mirror catches the signal, bounces it off the side of the building, and steers it around the obstacle to the user. It creates a "Virtual Highway" that goes around the blockage.

The New Challenge: The Mirror is Too Heavy to Move Fast

Here is the catch: This Magic Mirror isn't just a piece of glass. It's made of thousands of tiny, tiny switches (called elements). To make the mirror work, a computer has to tell every single switch exactly how to tilt the signal.

The Problem: If you have 1,000 switches, it takes a long time to tell them all what to do.
The Risk: In a "Proactive Handover" (which means switching to the mirror before the connection breaks), you have a very short window of time. If the mirror takes too long to set up, the connection will break before the mirror is ready.
The Energy Cost: Also, flipping all those switches uses a lot of battery power. If the mirror is on a building with limited power, it might run out of juice.

The Paper's Innovation: The "Just-Right" Mirror

The authors realized that you don't always need the entire mirror to work. Sometimes, a smaller piece of the mirror is enough to get the job done.

They created a smart computer program (using a method called Particle Swarm Optimization, which is like a flock of birds searching for the best food) to figure out the minimum number of switches needed to keep the signal strong enough.

Think of it like packing a suitcase for a trip:

Old way: Pack the whole closet (use all 1,000 switches). It's heavy, takes forever to pack, and uses a lot of energy.
New way: The computer calculates exactly what you need. "You only need 880 switches to stay connected."
The Result: By using fewer switches, the mirror sets up faster (giving you time to switch before the signal drops) and uses less energy.

How It Works in Real Life (The "Vision" Part)

The system uses cameras (like the ones on your phone or a security camera) to "see" the world.

The Eyes: The cameras see a bus coming down the street that will block the signal.
The Brain: The system predicts, "In 2 seconds, that bus will block the road."
The Action: Instead of waiting for the crash, the system instantly tells the Magic Mirror to wake up.
The Optimization: The system says, "We only need to activate the center 880 switches of the mirror to bounce the signal around the bus."
The Handover: Because the mirror is smaller and faster to set up, the switch happens smoothly before the user even notices the signal was about to drop.

The Key Takeaways

The Goal: Keep the internet fast and reliable even when obstacles block the direct path.
The Method: Use a "Magic Mirror" (RIS) to bounce the signal around blocks.
The Breakthrough: Don't use the whole mirror. Use a computer to find the smallest possible piece of the mirror that still works.
The Benefit:
- Speed: The mirror sets up faster, so the handover happens in time.
- Energy: It uses less power, making it greener and easier to install on buildings.
- Reliability: It prevents the internet from cutting out when a bus or building blocks the signal.

The paper proves that by reducing the number of active mirror switches by about 12%, they saved 10% of the energy without making the signal any worse. They also showed that this "Virtual Highway" is much stronger (by 15–30 dB) than trying to use a blocked direct line.

Technical Summary: RIS-Assisted Proactive Handover for Reliable mmWave Wireless Networks

Problem Statement
Millimeter-wave (mmWave) networks offer high transmission rates but suffer from severe signal degradation due to line-of-sight (LoS) blockages. Vision-aided wireless communications (VAWC) enable proactive handovers (PHO) to mitigate these blockages by predicting obstructions. However, PHO becomes challenging when no nearby base station (BS) is available for handover. In such scenarios, Reconfigurable Intelligent Surfaces (RIS) can restore connectivity by steering signals around obstacles. A critical bottleneck in this approach is the RIS configuration time. Large-scale RIS arrays, often required to achieve sufficient signal strength in mmWave bands, introduce significant latency in configuration and high energy dissipation. This latency can violate the strict timing constraints of PHO, particularly in ultra-reliable low-latency communication (URLLC) scenarios. Furthermore, existing literature often assumes the availability of alternative BSs or overlooks the impact of RIS configuration time on handover preparation delays.

Methodology
The paper proposes a novel RIS-assisted PHO framework that integrates VAWC with an optimized RIS configuration strategy. The core methodology involves a two-stage optimization process executed offline to construct a Channel Knowledge Map (CKM), ensuring that computational complexity does not impact real-time responsiveness.

System and Channel Modeling:
- The system models a BS equipped with Uniform Planar Arrays (UPA) and cameras, communicating with a single-antenna user.
- The RIS is modeled with a specific allocation strategy: square subarrays expanding outward from the center to maintain a fixed RIS center and aperture efficiency, requiring an odd number of elements per side.
- The channel model accounts for a cascaded link where the BS-to-RIS link operates in the far-field, while the RIS-to-user link may operate in the near-field, requiring spherical wavefront considerations.
- RIS configuration time ( $T_c$ ) is explicitly modeled as a function of the number of RIS elements ( $N$ ), the number of diodes per element ( $B$ ), and the shift register clock rate ($Clk$), expressed as $T_c = NB/Clk$ .
Optimization Formulation:
- The primary objective is to minimize the number of allocated RIS elements ( $N$ ) to reduce configuration time and energy dissipation while maintaining a target Signal-to-Noise Ratio (SNR).
- Stage 1 (Far-Field): A closed-form objective function is derived to minimize $N$ under ideal far-field assumptions and continuous phase shifts. This determines lower bounds for system parameters (transmitter antennas $M_{active}$ , subcarriers $K$ , and beam vectors).
- Stage 2 (Near-Field): Using the Stage 1 results as lower bounds, a second optimization problem maximizes the near-field SNR by jointly optimizing the RIS phase shifts (quantized), transmit power, and beamforming vectors.
- Algorithm: Particle Swarm Optimization (PSO) is employed to solve these non-convex, mixed-variable problems. The algorithm iteratively searches for the optimal combination of $N$ , $M_{active}$ , $K$ , and beam vectors that satisfy SNR constraints with minimal resource usage.
PHO Framework Integration:
- The proposed framework integrates the calculated RIS configuration time ( $T_c$ ) into the standard 3GPP handover preparation phase.
- The VAWC framework detects blockages and user mobility, triggering the PHO process. The updated execution time ( $T^{RIS}_{exec}$ ) includes $T_c$ , allowing the system to calculate a precise waiting time ( $T^{RIS}_W$ ) to trigger the handover before the LoS link is completely blocked.

Key Contributions

Integration of RIS and VAWC: The work integrates RIS-assisted connectivity into a VAWC framework to enable blockage prediction and precise user localization, facilitating beam steering toward blocked regions without requiring a secondary BS.
Optimization of RIS Elements: A novel optimization algorithm is developed to jointly minimize the number of allocated RIS elements and configuration time while maintaining link quality. This involves optimizing transmitter array specifications, beam selection, and subcarrier allocation.
Explicit Timing Consideration: The paper proposes a PHO framework that explicitly incorporates RIS configuration time into the handover preparation phase, addressing a gap in existing literature where this factor was often overlooked or estimated.
Practical Allocation Strategy: The paper introduces a centered subarray allocation method for RIS elements to preserve aperture efficiency and alignment, contrasting with corner-starting allocation methods that shift the effective array center.

Results
Simulation results, based on a scenario where a bus obstructs the LoS link between a BS and a user, demonstrate the efficacy of the proposed approach:

Energy and Complexity Reduction: Reducing the number of RIS elements by 12% resulted in a 10% decrease in dissipated energy without compromising the SNR.
Link Quality Improvement: The RIS-assisted link achieved a 15–30 dB improvement in signal strength in blocked regions compared to the blocked LoS scenario.
Timing Accuracy: The framework successfully maintained accurate PHO timing by accounting for the configuration delay, ensuring seamless connectivity.
Performance: The system maintained the required SNR thresholds while utilizing fewer resources than previous studies suggested were necessary (e.g., reducing the need for the previously estimated 1000+ elements in specific configurations).

Significance and Claims
The authors claim that this work provides a foundational baseline for environment-aware RIS-assisted PHO. By explicitly addressing the trade-off between link quality and RIS configuration time, the paper argues that it enables the practical deployment of RIS-assisted links in time-critical 6G applications. The significance lies in demonstrating that RIS-assisted handovers are feasible even when alternative BSs are unavailable, provided that the number of active RIS elements is optimized to meet strict latency and energy constraints. The work positions itself as a step toward green, sustainable 6G networks by reducing the power consumption and complexity associated with large-scale RIS deployments.