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Robust Fast 3D Beam Alignment for UAV-Assisted mmWave and Terahertz Communications.

Loubna Gafari¹, Wissal Attaoui², Essaid Sabir³

¹NEST Research Group, LRI Laboratory, École Nationale Supérieure d'Électricité et de Mécanique (ENSEM), Hassan II University of Casablanca, Casablanca 20000, Morocco.

Sensors (Basel, Switzerland)

|June 12, 2026

Summary

This study introduces a risk-aware framework for Unmanned Aerial Vehicle (UAV)-assisted millimeter-wave (mmWave) and terahertz (THz) networks. The Lévy Self-Renewable Flow Direction Algorithm (LSRFDA) ensures fast, reliable initial access with reduced latency and energy use.

Keywords:

HRLLC UAV communications URLLC beam alignment initial access latency variance mmWave risk-aware optimization stochastic geometry terahertz

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Published on: March 6, 2019

Area of Science:

Wireless Communication
Next-Generation Networks (5G/6G)
Signal Processing

Background:

Unmanned Aerial Vehicle (UAV)-assisted millimeter-wave (mmWave) and terahertz (THz) communications are key for ultra-reliable, low-latency next-generation wireless networks.
Initial access and beam alignment in 3D environments with highly directional beams present significant challenges for UAV-assisted mmWave/THz systems.

Purpose of the Study:

To develop a risk-aware beam alignment framework for UAV-assisted mmWave/THz systems.
To minimize expected cell-search latency and its variance while meeting detection-failure and link-quality constraints.
To enable fast and dependable initial access in 5G mmWave and 6G THz networks.

Main Methods:

Investigated a risk-aware beam alignment framework where user equipment scans a 3D spherical region for UAV base stations.
Employed the Lévy Self-Renewable Flow Direction Algorithm (LSRFDA) to solve the non-convex optimization problem, combining Lévy-flight exploration with self-renewal.
Utilized a unified propagation model incorporating free-space spreading loss and frequency-dependent molecular absorption for mmWave and THz regimes.

Main Results:

LSRFDA demonstrated lower latency and latency variation compared to Particle Swarm Optimization, Random Search, Reinforcement Learning, and PPO-Lagrangian methods.
The proposed approach achieved more reliable detection and lower energy consumption across various UAV densities and coverage radii.
Validated the effectiveness of risk-aware geometric optimization for initial access in UAV-assisted mmWave/THz networks.

Conclusions:

The LSRFDA-based risk-aware beam alignment framework significantly enhances initial access performance in UAV-assisted mmWave/THz systems.
The method provides a robust solution for minimizing latency and its variance while ensuring reliable communication.
This research contributes to the advancement of fast and dependable initial access for future 5G and 6G wireless networks.