Out-of-Band Modality Synergy Based Multi-User Beam Prediction and Proactive BS Selection with Zero Pilot Overhead

TL;DR

This paper tackles the overhead-laden problem of multi-user beam prediction and proactive BS selection in dense multi-BS mmWave networks. It proposes an out-of-band modality synergy (OMS) that combines vision and location data to identify and track users without pilots, and a BEM-GBPN network that predicts per-user beam gains and optimal beams via a binary-encoded environmental map and a Mixture-of-Experts framework. A proactive BS selection and beam switching strategy leverages predictions from all BSs to assign users to the BS with the highest predicted gain, enabling seamless handoffs with zero pilot overhead. Extensive simulations in urban vehicular scenarios demonstrate near-optimal transmission rates (approximately 91% of the optimum) and significantly reduced coordination overhead compared with existing methods, highlighting the practical impact for scalable, high-mobility ISAC-enabled networks.

Abstract

Multi-user millimeter-wave communication relies on narrow beams and dense cell deployments to ensure reliable connectivity. However, tracking optimal beams for multiple mobile users across multiple base stations (BSs) results in significant signaling overhead. Recent works have explored the capability of out-of-band (OOB) modalities in obtaining spatial characteristics of wireless channels and reducing pilot overhead in single-BS single-user/multi-user systems. However, applying OOB modalities for multi-BS selection towards dense cell deployments leads to high coordination overhead, i.e, excessive computing overhead and high latency in data exchange. How to leverage OOB modalities to eliminate pilot overhead and achieve efficient multi-BS coordination in multi-BS systems remains largely unexplored. In this paper, we propose a novel OOB modality synergy (OMS) based mobility management scheme to realize multi-user beam prediction and proactive BS selection by synergizing two OOB modalities, i.e., vision and location. Specifically, mobile users are initially identified via spatial alignment of visual sensing and location feedback, and then tracked according to the temporal correlation in image sequence. Subsequently, a binary encoding map based gain and beam prediction network (BEM-GBPN) is designed to predict beamforming gains and optimal beams for mobile users at each BS, such that a central unit can control the BSs to perform user handoff and beam switching. Simulation results indicate that the proposed OMS-based mobility management scheme enhances beam prediction and BS selection accuracy and enables users to achieve 91% transmission rates of the optimal with zero pilot overhead and significantly improve multi-BS coordination efficiency compared to existing methods.

Figures (18)

• Figure 1: The framework diagram of the proposed out-of-band modality synergy based mobility management scheme.
• Figure 2: The diagram of the OMS-based user identification and tracking algorithm. First, the BSM data fed back from users is utilized to identify user objects in the image. Subsequently, the visual MOT algorithm tracks these user objects in real-time. Aperiodic BSM feedback from users is used to correct the tracking results to ensure robust user identification and tracking.
• Figure 3: The illustration of the camera imaging principle and the relations between different coordinate systems.
• Figure 4: The illustration of the two-step matching based user identification process. A case of distance mismatch is shown on the top-right side, in which the user object is obstructed by the non-user object.
• Figure 5: The diagram of the binary encoding map based gain and beam prediction network (BEM-GBPN) at the BS.