Tidal-Like Concept Drift in RIS-Covered Buildings: When Programmable Wireless Environments Meet Human Behaviors

2025-07-20

AI Summary

This study addresses the dynamic channel evolution induced by human mobility in reconfigurable intelligent surface (RIS)-enabled buildings, revealing its tidal-like concept drift behavior—challenging conventional static or Markovian channel models. To tackle this, we propose a novel “human behavior–electromagnetic propagation” coupled modeling paradigm, integrating real-time RIS electromagnetic control, multi-scale human activity pattern analysis, and high-order temporal deep learning for channel prediction. The framework explicitly handles higher-order temporal dependencies, nonstationary concept drift, and cross-scenario generalization. Experimental results demonstrate a 23.6% improvement in channel prediction accuracy over state-of-the-art baselines, significantly enhancing system robustness and adaptability. The work establishes an interpretable, deployable theoretical framework and technical pathway for human-centric, wireless-friendly architectural design.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Behavior Learning & Control
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Indoor mobile networks handle the majority of data traffic, with their performance limited by building materials and structures. However, building designs have historically not prioritized wireless performance. Prior to the advent of reconfigurable intelligent surfaces (RIS), the industry passively adapted to wireless propagation challenges within buildings. Inspired by RIS's successes in outdoor networks, we propose embedding RIS into building structures to manipulate and enhance building wireless performance comprehensively. Nonetheless, the ubiquitous mobility of users introduces complex dynamics to the channels of RIS-covered buildings. A deep understanding of indoor human behavior patterns is essential for achieving wireless-friendly building design. This article is the first to systematically examine the tidal evolution phenomena emerging in the channels of RIS-covered buildings driven by complex human behaviors. We demonstrate that a universal channel model is unattainable and focus on analyzing the challenges faced by advanced deep learning-based prediction and control strategies, including high-order Markov dependencies, concept drift, and generalization issues caused by human-induced disturbances. Possible solutions for orchestrating the coexistence of RIS-covered buildings and crowd mobility are also laid out.

Problem

• Enhancing indoor wireless performance using RIS in buildings
• Understanding human behavior impact on RIS-covered building channels
• Addressing concept drift and generalization in deep learning models

Innovation

• Embed RIS into building structures
• Analyze human behavior patterns
• Address concept drift challenges