RIS-Assisted Proactive Handover for Reliable mmWave Wireless Networks

2026-06-25 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

A novel RIS-assisted proactive handover (PHO) approach addresses line-of-sight (LoS) blockages in millimeter-wave (mmWave) networks, particularly when no nearby base station is available. This method optimizes the number of allocated reconfigurable intelligent surface (RIS) elements to balance signal processing complexity and link quality, ensuring timely handovers and energy efficiency. The system formulates an optimization problem using particle swarm optimization (PSO) for offline configuration, mitigating latency constraints associated with large RIS arrays. Experimental results demonstrate that reducing RIS elements by 12% leads to a 10% decrease in dissipated energy without compromising the signal-to-noise ratio (SNR). Furthermore, the RIS-assisted link achieves a significant 15-30 dB improvement in blocked regions while maintaining accurate PHO timing.

Key takeaway

For research scientists developing reliable mmWave networks, consider integrating RIS-assisted proactive handover systems. This approach allows you to maintain robust connectivity in blockage-prone areas by optimizing RIS element allocation, achieving 15-30 dB signal improvement. You can reduce energy dissipation by 10% with a 12% element reduction, ensuring efficient and timely handovers through offline PSO optimization.

Key insights

RIS-assisted proactive handover optimizes element allocation for reliable, energy-efficient mmWave connectivity in blocked scenarios.

Principles

• Balance complexity and link quality for timely PHO.
• Offline optimization bypasses RIS configuration latency.
• Reduced RIS elements can save energy without SNR loss.

Method

An optimization problem based on particle swarm optimization (PSO) is formulated to determine the optimal end-to-end RIS link setup, running offline to bypass latency constraints.

In practice

• Deploy RIS for mmWave blockage mitigation.
• Optimize RIS element count for energy savings.
• Utilize PSO for offline RIS configuration.

Topics

• Millimeter-wave Networks
• Reconfigurable Intelligent Surfaces
• Proactive Handover
• Particle Swarm Optimization
• LoS Blockage Mitigation
• Wireless Connectivity

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.