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Signaling Rate and Performance of RIS Reconfiguration and Handover Management in Next Generation Mobile Networks

Mounir Bensalem, Admela Jukan

TL;DR

The paper tackles signaling-rate challenges for RIS reconfigurations (RR) and handover (HO) in next-generation mmWave/THz networks. It develops a stochastic-geometry framework to derive closed-form RR and HO rates, incorporating static obstacles, self-blockage, RIS density $\lambda_{RIS}$, eNB density $\lambda_{eNB}$, and UE mobility; it also derives the signaling rate $\mathbb{E}[\gamma]$ including RR/HO overhead. Key contributions include the first analytical model linking RR and HO rates to signaling performance, and a proposed RIS management architecture (RIS-M) plus an extended signaling protocol for RR/HO in LTE/NR-like systems, with dimensioning guidance for RIS-M and SGW servers. The results show how obstacle densities, mobility, and RIS density shape RR/HO dynamics and signaling load, enabling informed network-planning decisions for RIS-enabled infrastructure.

Abstract

We consider the problem of signaling rate and performance for an efficient control and management of RIS reconfigurations and handover in next generation mobile networks. To this end, we first analytically determine the rates of RIS reconfigurations and handover using a stochastic geometry network model. We derive closed-form expressions of these rates while taking into account static obstacles (both known and unknown), self-blockage, RIS location density, and variations in the angle and direction of user mobility. Based on the rates derived, we analyze the signaling rates of a sample novel signaling protocol, which we propose as an extension of an handover signaling protocol standard in mobile networks. The results quantify the impact of known and unknown obstacles on the RIS and handover reconfiguration rate as function of device density and mobility. We use the proposed analysis to evaluate the signaling overhead due to RIS reconfigurations, as well as to dimension the related RIS control plane server capacity in the network management system. To the best of our knowledge, this is the first analytical model to derive the closed form expressions of RIS reconfiguration rates, along with handover rates, and relate its statistical properties to the signaling rate and performance in next generation mobile networks.

Signaling Rate and Performance of RIS Reconfiguration and Handover Management in Next Generation Mobile Networks

TL;DR

The paper tackles signaling-rate challenges for RIS reconfigurations (RR) and handover (HO) in next-generation mmWave/THz networks. It develops a stochastic-geometry framework to derive closed-form RR and HO rates, incorporating static obstacles, self-blockage, RIS density , eNB density , and UE mobility; it also derives the signaling rate including RR/HO overhead. Key contributions include the first analytical model linking RR and HO rates to signaling performance, and a proposed RIS management architecture (RIS-M) plus an extended signaling protocol for RR/HO in LTE/NR-like systems, with dimensioning guidance for RIS-M and SGW servers. The results show how obstacle densities, mobility, and RIS density shape RR/HO dynamics and signaling load, enabling informed network-planning decisions for RIS-enabled infrastructure.

Abstract

We consider the problem of signaling rate and performance for an efficient control and management of RIS reconfigurations and handover in next generation mobile networks. To this end, we first analytically determine the rates of RIS reconfigurations and handover using a stochastic geometry network model. We derive closed-form expressions of these rates while taking into account static obstacles (both known and unknown), self-blockage, RIS location density, and variations in the angle and direction of user mobility. Based on the rates derived, we analyze the signaling rates of a sample novel signaling protocol, which we propose as an extension of an handover signaling protocol standard in mobile networks. The results quantify the impact of known and unknown obstacles on the RIS and handover reconfiguration rate as function of device density and mobility. We use the proposed analysis to evaluate the signaling overhead due to RIS reconfigurations, as well as to dimension the related RIS control plane server capacity in the network management system. To the best of our knowledge, this is the first analytical model to derive the closed form expressions of RIS reconfiguration rates, along with handover rates, and relate its statistical properties to the signaling rate and performance in next generation mobile networks.
Paper Structure (29 sections, 2 theorems, 51 equations, 13 figures, 5 tables)

This paper contains 29 sections, 2 theorems, 51 equations, 13 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Reference Architecture
  4. RIS Reconfiguration vs. Handover Management
  5. Modeling statistical properties
  6. Management and signaling
  7. Analysis
  8. Reference Scenario and Assumptions
  9. Topology
  10. Obstacle model
  11. Self-blockage model
  12. Mobility
  13. RIS reconfigurations
  14. Handovers (HO)
  15. Signaling
  16. ...and 14 more sections

Key Result

Theorem 1

The HO rate can be expressed as the sum over all SGW of the product of the session arrival rate $\lambda_{k}$ for the k$^{th}$ SGW and the probability of HO when a UE moves with a speed $d_{U}$ and a angle of movement $\xi_{eNB}$, given as follows: where $R_{eNB}$ can be computed as shown in Appendix appedix:proof1 (see Eq. (eq:R_from_r)) in relation to the speed of the UE $d_{U}$, the angle of m

Figures (13)

  • Figure 1: RIS reconfiguration vs. handover (HO) management.
  • Figure 2: Enhancing the LTE mobility architecture with RIS management entity. MME: Mobility Management Entity, SGW: Serving Gateway, PGW: Packet Data Network Gateway, RIS-M: RIS Manager
  • Figure 3: Conceptual proposal for signaling.
  • Figure 4: Reference scenario.
  • Figure 5: Scenario with mobile UEs, RISs, obstacles and self-blockage.
  • ...and 8 more figures

Theorems & Definitions (5)

  • Theorem 1
  • proof
  • Proposition 1
  • proof
  • proof