Toward Blockage-Resilient 6G-V2X Connectivity: Semi-Distributed Bandit with Dynamic Arm Set for mmWave HetNets

2026-08-05

AI Summary

This work addresses the challenge of channel dynamics in 6G vehicular millimeter-wave heterogeneous networks, where rapid time-variation and dynamic blockage render conventional multi-armed bandit algorithms ineffective. To tackle this, the authors propose two novel algorithms—distributed BAND and semi-distributed S-BAND—that exploit spatial channel correlation through trajectory-aligned knowledge regions. These methods integrate blockage-aware change-point detection, dynamic base station set management, and knowledge transfer mechanisms to enable efficient user association without requiring centralized channel state information or offline training. Evaluated in realistic urban scenarios, both algorithms significantly outperform centralized baselines: BAND and S-BAND reduce cumulative regret by 34.9% and 59.4%, respectively, while maintaining robust performance across blockage rates ranging from 10% to 50%.

Abstract

The vision for 6G vehicle-to-everything (V2X) communications demands reliable, adaptive connectivity for fully autonomous driving across complex dynamic environments. Millimeter-wave (mmWave) user association (UA) in heterogeneous vehicular networks presents a particularly demanding instance of this problem, where dynamic blockages and rapid channel variations continuously undermine the stationary reward assumptions of traditional multi-armed bandit (MAB) frameworks. This paper proposes a fully distributed blockage-aware non-stationary dynamic bandit algorithm (BAND) and its semi-distributed extension S-BAND for cooperative learning across vehicles. Blockage prediction is incorporated into the change-detection (CD) mechanism to suppress false alarms, while a dynamic base station (BS) set management scheme balances exploration and exploitation across large-scale BS deployments without requiring centralized channel state information (CSI) acquisition or offline training. In S-BAND, vehicles accumulate BS reward estimates as local knowledge and periodically upload them to the macro base station (MBS), which aggregates them into cluster-based central knowledge. A trajectory-aligned knowledge (TAK) region is proposed to capture the spatial correlation of mmWave channel characteristics. A knowledge inheritance fidelity (KIF) metric is introduced to quantify knowledge transfer quality. Simulation results on a realistic urban topology show that BAND and S-BAND achieve 34.9% and 59.4% regret reduction relative to a centralized MAB baseline, with performance gains sustained across blockage rates ranging from 10% to 50%. The proposed TAK region consistently outperforms the traditional K-means clustering scheme under both fidelity criteria.

Problem

• 6G-V2X
• mmWave HetNets
• dynamic blockage
• non-stationary user association
• reliable connectivity

Innovation

• blockage-aware bandit
• dynamic arm set
• trajectory-aligned knowledge
• knowledge inheritance fidelity
• semi-distributed learning