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.

Ultra-reliable low-latency communication is the key technology for smart factories and autonomous vehicles. However, traditional beam training approaches in millimeter-wave communications generally cause significant latency and communication overhead, especially in the case of multi-user communications. To tackle this problem, we propose a novel Vision-aided Multi-user Beam Tracking (VA-MUBT) framework for mmWave massive MIMO system, which leverages deep learning based visual object detection and multiple objects tracking algorithm to enable fast beam tracking of multi-user. In addition, a prototype is constructed to evaluate the proposed VA-MUBT framework and the experimental results based on this prototype show that the accuracy of 3-time beam search can reach near 90% with only 8% overhead of the exhaustive beam search method. Hence, the proposed VA-MUBT demonstrates the superiority in achieving fast multi-user beam tracking and significantly reducing the communication overhead.

In partial duplicate image retrieval systems, min-Hash algorithms are widely used because of its high efficiency and robustness. In most of min-Hash algorithms, min-Hash functions are considered independent and grouped into tuples called sketches, the discriminative power of sketches are limited. By modeling correlations of min-Hash functions, we propose a novel sketch construction method called Nonpara-metric Clustering min-Hash (NCmH). In NCmH, the randomly generated min-Hash functions are clustered before grouping them into sketches, while spatial information is fully used in this process. The constructed sketches preserve abundant spatial information between visual words, thus NCmH achieves higher retrieval accuracy compared to the standard min-Hash. Furthermore, our method can be combined with other min-Hash algorithms such as GVP mH [1], PmH [2] and TmH [3] to further improve accuracy. In experiments, we show that our method outperforms the standard min-Hash and improves the state-of-the-art min-Hash algorithm on Oxford 5K dataset and University of Kentucky dataset.