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

Haoyan Wei, Hongtao Zhang

TL;DR

This work tackles handover in IRS-assisted networks where IRS reflection gains and double path loss cause irregular HO triggers. It introduces a discrete-time, finite-state framework that discretizes IRS connections and the HO process at measurement intervals, enabling explicit tracking of signal fluctuations along user trajectories. Key contributions include revised distance and angle PDFs conditioned on measurement correlation, integration of IRS gains into HO-state transitions, and analytic expressions for HO, HOF, and PP trigger locations along with optimal HO parameter settings. Numerical results show IRS can reduce ping-pong events but may increase handover failures under regular parameters, and they identify HO parameter regions (TTT and HO margin) that keep both $\\mathbb{P}_{hof}$ and $\\mathbb{P}_{pp}$ below $0.1\%$, highlighting practical guidance for IRS-enabled HO design.

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%.

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

TL;DR

This work tackles handover in IRS-assisted networks where IRS reflection gains and double path loss cause irregular HO triggers. It introduces a discrete-time, finite-state framework that discretizes IRS connections and the HO process at measurement intervals, enabling explicit tracking of signal fluctuations along user trajectories. Key contributions include revised distance and angle PDFs conditioned on measurement correlation, integration of IRS gains into HO-state transitions, and analytic expressions for HO, HOF, and PP trigger locations along with optimal HO parameter settings. Numerical results show IRS can reduce ping-pong events but may increase handover failures under regular parameters, and they identify HO parameter regions (TTT and HO margin) that keep both and below , highlighting practical guidance for IRS-enabled HO design.

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%.
Paper Structure (22 sections, 17 theorems, 45 equations, 13 figures, 1 table)

This paper contains 22 sections, 17 theorems, 45 equations, 13 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Works
  3. Contribution
  4. System Model
  5. Network Model
  6. IRS Connection Events
  7. Handover Process and Events
  8. User Mobility
  9. Discrete-Time Models for IRS Connections and Handover
  10. IRS Connections
  11. Distance and Angle Distribution of the IRS
  12. Handover Analysis
  13. Handover Failure Analysis
  14. Ping-Pong Analysis
  15. Numerical Results
  16. ...and 7 more sections

Key Result

Lemma 1

The state transition matrix of the IRS connection is given by where ${p_{m,n}^{{{\mathcal{I}}^k}}\left( i \right)}, k \in \left\{ {o, t} \right\}, m, n \in \left\{ {1,2,3,4} \right\}$ is state transition probabilities of IRS connection sates. ${p_{m,n}^{{{\mathcal{I}}^o}}\left( i \right)}$ and ${p_{m,n}^{{{\mathcal{I}}^t}}\left( i \right)}$ are given by where $S_i^o$ ($S_i^t$) is the area of the

Figures (13)

  • Figure 1: Handover process in the IRS-assisted networks: (a) The IRS-assisted network structure; (b) The received signal strength changes with time.
  • Figure 2: User mobility mode and location relations between users and network elements.
  • Figure 3: Discrete-time models for (a) IRS connections, (b) handover, (c) handover failure, and (d) ping-pong. The symbols of each state are inside the circles and the state transition probabilities are beside the lines.
  • Figure 4: Probability distributions of HO trigger locations and HO execution locations, where $D=10\rm{m}$: (a) Probability distribution of HO trigger locations; (b) Probability distribution of HO execution locations ($T_t=120\rm{ms}$); (c) Probability distribution of HO execution locations ($T_t=480\rm{ms}$).
  • Figure 5: Probability distributions of HO trigger locations and HO execution locations, where $D=50\rm{m}$: (a) Probability distribution of HO trigger locations; (b) Probability distribution of HO execution locations ($T_t=120\rm{ms}$); (c) Probability distribution of HO execution locations ($T_t=480\rm{ms}$).
  • ...and 8 more figures

Theorems & Definitions (19)

  • Remark 1
  • Remark 2
  • Lemma 1
  • Lemma 2
  • Lemma 3
  • Lemma 4
  • Lemma 5
  • Lemma 6
  • Lemma 7
  • Theorem 1
  • ...and 9 more