Contextual Combinatorial Beam Management via Online Probing for Multiple Access mmWave Wireless Networks
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Abstract (arXiv)
Due to the exponential increase in wireless devices and a diversification of network services, unprecedented challenges, such as managing heterogeneous data traffic and massive access demands, have arisen in next-generation wireless networks. To address these challenges, there is a pressing need for the evolution of multiple access schemes with advanced transceivers. Millimeter-wave (mmWave) communication emerges as a promising solution by offering substantial bandwidth and accommodating massive connectivities. Nevertheless, the inherent signaling directionality and susceptibility to blockages pose significant challenges for deploying multiple transceivers with narrow antenna beams. Consequently, beam management becomes imperative for practical network implementations to identify and track the optimal transceiver beam pairs, ensuring maximum received power and maintaining high-quality access service. In this context, we propose a Contextual Combinatorial Beam Management (CCBM) framework tailored for mmWave wireless networks. By leveraging advanced online probing techniques and integrating predicted contextual information, such as dynamic link qualities in spatial-temporal domain, CCBM aims to jointly optimize transceiver pairing and beam selection while balancing the network load. This approach not only facilitates multiple access effectively but also enhances bandwidth utilization and reduces computational overheads for real-time applications. Theoretical analysis establishes the asymptotically optimality of the proposed approach, complemented by extensive evaluation results showcasing the superiority of our framework over other state-of-the-art schemes in multiple dimensions.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Nov 2024.
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Citing papers per year: 2024: 1; 2025: 0; 2026: 3.
4 papers.
- 2024Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learningcited by 2
- 2026CSI-JEPA: Towards Foundation Representations for Ubiquitous Sensing with Minimal Supervisioncited by 2
- 2026Multi-User mmWave Beam and Rate Adaptation via Combinatorial Satisficing Banditscited by 0
- 2026Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximationcited by 0
- 2026Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation15 Jul 2026
- 2026CSI-JEPA: Towards Foundation Representations for Ubiquitous Sensing with Minimal Supervision13 May 2026
- 2026Multi-User mmWave Beam and Rate Adaptation via Combinatorial Satisficing Bandits16 Apr 2026
- 2024Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning23 Dec 2024