Extreme Value Theory-enhanced Radio Maps for Handovers in Ultra-reliable Communications

2025-09-26

AI Summary

In ultra-reliable communication (URC), inaccurate physical-layer handover (HO) timing and target-cell selection often trigger ping-pong effects and service interruptions. To address this, we propose an extreme value theory (EVT)-enhanced statistical radio map modeling framework. By characterizing the tail distribution of channel gain, our approach accurately predicts signal degradation at critical boundaries; integrating real-time physical-layer measurements, it constructs a dynamic radio map to guide HO triggering and target-cell selection. Furthermore, we design a joint HO decision-making and resource allocation mechanism coordinated with spatial resource management. Experimental results demonstrate that the proposed method significantly suppresses ping-pong handovers, improves HO success rate and service availability, and achieves superior reliability, lower latency, and enhanced energy efficiency in URC scenarios.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Reasoning under Uncertainty: Stochastic Optimization
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: User model development and evaluation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Efficient handover (HO) strategies are essential for maintaining the stringent performance requirements of ultra-reliable communication (URC) systems. This work introduces a novel HO framework designed from a physical-layer perspective, where the decision-making process focuses on determining the optimal time and location for performing HOs. Leveraging extreme value theory (EVT) and statistical radio maps, the proposed method predicts signal behaviour and enables efficient resource allocation. The framework ensures seamless HOs and improved system performance by facilitating effective resource transitions and coordination across spatial locations while incorporating mechanisms to mitigate the ping-pong effect. Comparative evaluations demonstrate that this strategy provides superior service availability and energy efficiency than traditional HO mechanisms, highlighting its effectiveness in URC environments.

Problem

• Optimizing handover timing and location decisions
• Predicting signal behavior using extreme value theory
• Improving service availability and energy efficiency

Innovation

• EVT-enhanced radio maps predict signal behavior
• Physical-layer framework optimizes handover timing
• Statistical coordination mitigates ping-pong effect