A conditional denoising diffusion model generates radio maps from sparse RSS fragments or transmitter coordinates, outperforming cGAN and pix2pix in synthetic indoor and outdoor scenarios.
Contextual Combinatorial Beam Management via Online Probing for Multiple Access mmWave Wireless Networks
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
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.
citation-role summary
citation-polarity summary
fields
cs.NI 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Denoising Diffusion Probabilistic Model for Radio Map Estimation in Generative Wireless Networks
A conditional denoising diffusion model generates radio maps from sparse RSS fragments or transmitter coordinates, outperforming cGAN and pix2pix in synthetic indoor and outdoor scenarios.