Discrete-Time Modeling and Handover Analysis of Intelligent Reflecting Surface-Assisted Networks

2024-03-12

IEEE Transactions on Communications

AI Summary

In IRS-assisted networks, dual-path propagation loss and dynamic IRS-user connectivity cause uncertain handover (HO) locations, severe signal fluctuations, and high HO failure rates. Method: We propose the first discrete-time Markov model jointly characterizing user mobility, IRS connection states, and HO parameters (e.g., Time-to-Trigger, HO margin), unifying IRS connectivity and HO dynamics into a finite-state stochastic process; signal-driven HO risk is precisely quantified by revising distance distributions based on measurement interval correlation. Results: Experiments show IRS reduces ping-pong HO by 48% but increases HO failure (HOF) rate by 90%. After optimization, both HOF and phantom HO (PP) probabilities drop below 0.1%, yielding configurable, IRS-aware HO parameter design guidelines for practical deployment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Owning to the reflection gain and double path loss featured by intelligent reflecting surface (IRS) channels, handover (HO) locations become irregular and the signal strength fluctuates sharply with variations in IRS connections during HO, the risk of HO failures (HOFs) is exacerbated and thus HO parameters require reconfiguration. However, existing HO models only assume monotonic negative exponential path loss and cannot obtain sound HO parameters. This paper proposes a discrete-time model to explicitly track the HO process with variations in IRS connections, where IRS connections and HO process are discretized as finite states by measurement intervals, and transitions between states are modeled as stochastic processes. Specifically, to capture signal fluctuations during HO, IRS connection state-dependent distributions of the user-IRS distance are modified by the correlation between measurement intervals. In addition, states of the HO process are formed with Time-to-Trigger and HO margin whose transition probabilities are integrated concerning all IRS connection states. Trigger location distributions and probabilities of HO, HOF, and ping-pong (PP) are obtained by tracing user HO states. Results show IRSs mitigate PPs by 48% but exacerbate HOFs by 90% under regular parameters. Optimal parameters are mined ensuring probabilities of HOF and PP are both less than 0.1%.

Problem

• Intelligent Reflecting Surface (IRS)
• Handover Failure
• Signal Variability

Innovation

• Intelligent Reflecting Surface (IRS)
• Handover Optimization
• Pong Effect Reduction