arXiv subjects

Dapeng Wang

Publications and source records attributed to Dapeng Wang.

5 recordsLinked to original sources

arXiv2026

Omni-Photonic Base Station: A Three-Functional-Domain Photonic Architecture for Evolutionary 6G Wireless Infrastructure

6G mobile communications impose immersive communication demands for higher data rates and massive connectivity, while emerging integrated sensing-computing-intelligence scenarios require base stations to concurrently enhance computing capability, guarantee service latency, and realize sensing. We propose the Omni-Photonic Base Station-a progressive evolutionary architecture that introduces photonic technologies into three functional domains, namely baseband processing, fronthaul transmission, and the RF front-end, and obtains system-level gains through cross-domain co-design. By introducing photonics, Omni-PBS harnesses the inherent physical advantages of ultra-broad bandwidth, ultra-low propagation latency, and native parallelism to transcend the aforementioned electronic bottlenecks and fulfill the compound demands of 6G scenarios. The optical baseband computing domain employs photonic accelerators to perform linear computation-intensive tasks, improving the energy efficiency of AI inference by one to two orders of magnitude. The analog optical fronthaul domain replaces digital fronthaul with analog radio-over-fiber , which eliminates the ADCs/DACs and digital intermediate-frequency chips in the remote unit and substantially reduces fronthaul bandwidth requirements. The microwave-photonic RF domain breaks through the bandwidth and frequency limitations of electronic RF front-ends via optical true-time-delay beamforming, programmable photonic filtering, and optical heterodyne frequency conversion. The three domains yield system-level gains beyond single-domain summation through four co-design principles: end-to-end optical-domain continuity, cross-domain co-design, optical computing resource scheduling, and joint optimization of functional splitting.

eess.SP↗

arXiv2026

Task-Oriented Direct-to-Cell Satellite Communications for 6G Closed-Loop Autonomous Operations

Direct-to-cell (D2C) satellite communications have emerged as a crucial alternative to terrestrial communications in the sixth generation (6G) mobile networks due to their wide-area coverage capability. Unlike human-oriented communications, future 6G robot-oriented D2C satellite communications in autonomous operations place greater emphasis on the ultimate task completion than on the intermediate stage of data transmissions. Such a difference renders it crucial to evaluate the performance of each stage in a systematic manner and consider a multistage integrated optimization. Motivated by this, we model the system with a sensing-communication-computing-control (SC3) closed loop and analyze it from an entropy-based perspective, from which a task-oriented system design method is developed. Furthermore, to manage the complexity of the closed-loop network, we decompose it into fine-grained functional structures and investigate the key challenges of collaborative sensing, collaborative computing, and collaborative control. A case study is presented to compare the proposed task-oriented scheme with conventional communication-oriented schemes, showing that the proposed method has better performance in system-level control cost. Finally, several open issues are outlined for future research and practical implementation.

eess.SP↗

arXiv2024

Efficient Estimation of the Convective Cooling Rate of Photovoltaic Arrays with Various Geometric Configurations: a Physics-Informed Machine Learning Approach

Convective heat transfer is crucial for photovoltaic (PV) systems, as the power generation of PV is sensitive to temperature. The configuration of PV arrays have a significant impact on convective heat transfer by influencing turbulent characteristics. Conventional methods of quantifying the configuration effects are either through Computational Fluid Dynamics (CFD) simulations or empirical methods, which face the challenge of either high computational demand or low accuracy, especially when complex array configurations are considered. This work introduces a novel methodology to quantify the impact of geometric configurations of PV arrays on their convective heat transfer rate in wind field. The methodology combines Physics Informed Machine Learning (PIML) and Deep Convolution Neural Network (DCNN) to construct a robust PIML-DCNN model to predict convective heat transfer rates. In addition, an innovative loss function, termed Pocket Loss is proposed to enhance the interpretability of the PIML-DCNN model. The model exhibits promising performance, with a relative error of 1.9\% and overall $R^2$ of 0.99 over all CFD cases in estimating the coefficient of convective heat transfer, when compared with full CFD simulations. Therefore, the proposed model has the potential to efficiently guide the configuration design of PV arrays for power generation enhancement in real-world operations.

physics.flu-dyn↗

arXiv2017

Optical chirality from dark-field illumination of planar plasmonic nanostructures

Dark-field illumination is shown to make planar chiral nanoparticle arrangements exhibit circular dichroism in extinction analogous to true chiral scatterers. Circular dichrosim is experimentally observed at the maximum scattering of single oligomers consisting rotationally symmetric arrangements of gold nanorods, with strong agreement to numerical simulation. A dipole model is developed to show that this effect is caused by a difference in the geometric projection of a nanorod onto the handed orientation of electric fields created by a circularly polarized dark-field that is normally incident on a glass substrate. Owing to this geometric origin, the wavelength of the peak chiral response is also experimentally shown to shift depending on the separation between nanoparticles. All presented oligomers have physical dimensions less than the operating wavelength, and the applicable extension to closely packed planar arrays of oligomers is demonstrated to amplify the magnitude of circular dichroism. The realization of strong chirality in these oligomers demonstrates a new path to engineer optical chirality from planar devices using dark-field illumination.

physics.optics↗

arXiv2013

Methods for scoring the collective effect of SNPs: Minor alleles of common SNPs quantitatively affect traits/diseases and are under both positive and negative selection

Most common SNPs are popularly assumed to be neutral. We here developed novel methods to examine in animal models and humans whether extreme amount of minor alleles (MAs) carried by an individual may represent extreme trait values and common diseases. We analyzed panels of genetic reference populations and identified the MAs in each panel and the MA content (MAC) that each strain carried. We also analyzed 21 published GWAS datasets of human diseases and identified the MAC of each case or control. MAC was nearly linearly linked to quantitative variations in numerous traits in model organisms, including life span, tumor susceptibility, learning and memory, sensitivity to alcohol and anti-psychotic drugs, and two correlated traits poor reproductive fitness and strong immunity. Similarly, in Europeans or European Americans, enrichment of MAs of fast but not slow evolutionary rate was linked to autoimmune and numerous other diseases, including type 2 diabetes, Parkinson's disease, psychiatric disorders, alcohol and cocaine addictions, cancer, and less life span. Therefore, both high and low MAC correlated with extreme values in many traits, indicating stabilizing selection on most MAs. The methods here are broadly applicable and may help solve the missing heritability problem in complex traits and diseases.

q-bio.GN↗