arXiv subjects

Chen Shang

Publications and source records attributed to Chen Shang.

15 recordsLinked to original sources

arXiv2026

Robust Beam Prediction for V2X Networks with Multi-Modal Sensing

Integrated sensing and communication (ISAC) provides a promising foundation for beam prediction in future vehicle-to-everything (V2X) networks. However, existing sensing-assisted beamforming methods still rely heavily on radio-frequency sensing, which may become unreliable in complex vehicular environments. Meanwhile, the growing availability of heterogeneous sensors, such as cameras and LiDAR, offers new opportunities to improve beam prediction through richer environmental perception. Motivated by this, this paper proposes a multi-modal beam prediction framework for V2X networks. Specifically, we develop BeamTransFuser, a hierarchical Transformer-based architecture that progressively fuses camera, LiDAR, radar, and GPS observations for robust beam prediction. In addition, to handle possible missing modalities in practical deployment, we introduce a generative module that reconstructs missing modality features from the available observations. Experimental results on a real-world multi-modal V2X dataset show that the proposed framework consistently outperforms representative baselines, while the generative module further improves robustness under incomplete sensing conditions.

cs.LG↗

arXiv2026

Hitting time mixing for random $k$-cycles

In this paper, we study the random walk on the symmetric group $\mathfrak{S}_n$ generated by the conjugacy class of $k$-cycles, where $2\le k=o(n/(\log n)^4)$. We prove that the walk exhibits hitting-time mixing: at the first time when every card has been touched, the distribution is already close to equilibrium. For odd $k$, the equilibrium measure is the uniform measure on $\mathfrak{A}_n$. For even $k$, the walk first mixes to the parity mixture determined by the hitting time, and in our range this mixture is asymptotically $U_{\mathfrak{S}_n}$. Our argument combines a refined fixed-time approximation for the random $k$-cycle walk near the cutoff window with an auxiliary marking scheme inspired by Jain-Sawhney's work (arXiv:2410.23944) on random transpositions. The main new feature is a parity-compatible coupling which handles both odd and even $k$-cycles in a unified framework. We also prove a hitting-time mixing result in the opposite regime $k\ge n-o(n^{1/2})$, and formulate a conjecture for all $2\le k\le n-1$.

math.PR↗

arXiv2026

Spiking Personalized Federated Learning for Brain-Computer Interface-Enabled Immersive Communication

This work proposes a novel immersive communication framework that leverages brain-computer interface (BCI) to acquire brain signals for inferring user-centric states (e.g., intention and perception-related discomfort), thereby enabling more personalized and robust immersive adaptation under strong individual variability. Specifically, we develop a personalized federated learning (PFL) model to analyze and process the collected brain signals, which not only accommodates neurodiverse brain-signal data but also prevents the leakage of sensitive brain-signal information. To address the energy bottleneck of continual on-device learning and inference on energy-limited immersive terminals (e.g., head-mounted display), we further embed spiking neural networks (SNNs) into the PFL. By exploiting sparse, event-driven spike computation, the SNN-enabled PFL reduces the computation and energy cost of training and inference while maintaining competitive personalization performance. Experiments on real brain-signal dataset demonstrate that our method achieves the best overall identification accuracy while reducing inference energy by 6.46$\times$ compared with conventional artificial neural network-based personalized baselines.

cs.LG↗

arXiv2026

Efficient Cross-View Localization in 6G Space-Air-Ground Integrated Network

Recently, visual localization has become an important supplement to improve localization reliability, and cross-view approaches can greatly enhance coverage and adaptability. Meanwhile, future 6G will enable a globally covered mobile communication system, with a space-air-ground integrated network (SAGIN) serving as key supporting architecture. Inspired by this, we explore an integration of cross-view localization (CVL) with 6G SAGIN, thereby enhancing its performance in latency, energy consumption, and privacy protection. First, we provide a comprehensive review of CVL and SAGIN, highlighting their capabilities, integration opportunities, and potential applications. Benefiting from the fast and extensive image collection and transmission capabilities of the 6G SAGIN architecture, CVL achieves higher localization accuracy and faster processing speed. Then, we propose a split-inference framework for implementing CVL, which fully leverages the distributed communication and computing resources of the 6G SAGIN architecture. Subsequently, we conduct joint optimization of communication, computation, and confidentiality within the proposed split-inference framework, aiming to provide a paradigm and a direction for making CVL efficient. Experimental results validate the effectiveness of the proposed framework and provide solutions to the optimization problem. Finally, we discuss potential research directions for 6G SAGIN-enabled CVL.

cs.NI↗

arXiv2025

Energy-Efficient Learning-Based Beamforming for ISAC-Enabled V2X Networks

This work proposes an energy-efficient, learning-based beamforming scheme for integrated sensing and communication (ISAC)-enabled V2X networks. Specifically, we first model the dynamic and uncertain nature of V2X environments as a Markov Decision Process. This formulation allows the roadside unit to generate beamforming decisions based solely on current sensing information, thereby eliminating the need for frequent pilot transmissions and extensive channel state information acquisition. We then develop a deep reinforcement learning (DRL) algorithm to jointly optimize beamforming and power allocation, ensuring both communication throughput and sensing accuracy in highly dynamic scenario. To address the high energy demands of conventional learning-based schemes, we embed spiking neural networks (SNNs) into the DRL framework. Leveraging their event-driven and sparsely activated architecture, SNNs significantly enhance energy efficiency while maintaining robust performance. Simulation results confirm that the proposed method achieves substantial energy savings and superior communication performance, demonstrating its potential to support green and sustainable connectivity in future V2X systems.

eess.SP↗

arXiv2025

Microscopic Theory of Squeezed Light in Quantum Dot Systems

We present a cavity-QED theory for generating squeezed light from semiconductor quantum dots (QDs) integrated in microcavities. We formulate equations of motion for an inhomogeneously broadened QD ensemble that is incoherently pumped and simultaneously driven by a coherent seed field, solve for steady states, and compute the output-field quadrature variances. The analysis identifies operating conditions that yield amplitude-quadrature squeezing, with photon-number fluctuations reduced below the coherent-state limit and squeezing levels as large as 5 dB attainable with presently accessible QD and cavity parameters using only ~ 1 uW pump power. We further show that quantum correlations originating from four-wave mixing play a dual role: they both shape the gain spectrum and generate squeezing. These correlations constitute the quantum counterpart of the mean-field (semiclassical) mechanisms responsible for self-mode-locking in QD lasers and the ultra-narrow lasing linewidths achieved under self-injection locking.

quant-ph↗

arXiv2025

Increasing single-photon production with cavity-QED

A study was performed to determine the extent cavity enhancement may increase single-photon production while maintaining single-photon purity. It was found that certain combinations of cavity lifetime and light-matter coupling strength can lead to carrier-photon correlations that increase single-photon generation rate while maintaining low 2nd order photon correlation. This study provides guidance for future emitter-cavity design to achieve high purity and ultrafast single photon generation.

quant-ph↗

arXiv2025

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks

Integrating sensing and communication (ISAC) is a promising technology for predictive beamforming in 6G vehicle-to-everything (V2X) networks. However, current ISAC paradigms rely solely on radio-frequency (RF)-based sensing, which limits sensing resolution and beamforming robustness in complex wireless environments. Fortunately, the widespread deployment of diverse non-RF sensors such as cameras and LiDAR, along with the integration of artificial intelligence (AI) and communication systems, offers new opportunities to improve the synergy between sensing and communication. Motivated by this, this work develops a multi-modal sensing-assisted beamforming framework for realistic V2X scenarios. Specifically, we propose BeamTransFuser, a hierarchical Transformer-based multi-modal learning framework that exploits cross-modal correlations among camera, LiDAR, radar, and GPS observations to improve beam prediction accuracy and robustness. To facilitate practical deployment on roadside units, we further develop a module-aware pruning scheme to reduce inference latency while preserving competitive performance. Furthermore, to address potential missing-modality conditions in real-world scenarios, we introduce a generative model that is able to reconstruct missing inputs from available observations, allowing the framework to operate reliably even under incomplete sensing conditions. Extensive experimental results conducted on real-world datasets demonstrate that the proposed scheme consistently outperforms existing baselines across various metrics.

eess.SP↗

arXiv2025

Energy-Efficient and Intelligent ISAC in V2X Networks with Spiking Neural Networks-Driven DRL

Integrated sensing and communication (ISAC) is emerging as a key enabler for vehicle-to-everything (V2X) systems. However, designing efficient beamforming schemes for ISAC signals to achieve accurate sensing and enhance communication performance in the dynamic and uncertain environments of V2X networks presents significant challenges. While artificial intelligence technologies offer promising solutions, the energy-intensive nature of neural networks imposes substantial burdens on communication infrastructures. To address these challenges, this work proposes an energy-efficient and intelligent ISAC system for V2X networks. Specifically, we first leverage a Markov Decision Process framework to model the dynamic and uncertain nature of V2X networks. This framework allows the roadside unit to develop beamforming schemes relying solely on its current sensing information, eliminating the need for numerous pilot signals and extensive CSI acquisition. We then introduce an advanced deep reinforcement learning (DRL) algorithm, enabling the joint optimization of beamforming and power allocation to guarantee both communication rate and sensing accuracy in dynamic and uncertain V2X scenario. To alleviate the energy demands of neural networks, we integrate spiking neural networks (SNNs) into the DRL algorithm. The event-driven, sparse spike-based processing of SNNs significantly improves energy efficiency while maintaining strong performance. Extensive simulation results validate the effectiveness of the proposed scheme with lower energy consumption, superior communication performance, and improved sensing accuracy.

cs.NI↗

arXiv2024

Integrating Brain-Computer Interface and Neuromorphic Computing for Human Digital Twins

The integration of immersive communication into a human-centric ecosystem has intensified the demand for sophisticated Human Digital Twins (HDTs) driven by multifaceted human data. However, the effective construction of HDTs faces significant challenges due to the heterogeneity of data collection devices, the high energy demands associated with processing intricate data, and concerns over the privacy of sensitive information. This work introduces a novel biologically-inspired (bio-inspired) HDT framework that leverages Brain-Computer Interface (BCI) sensor technology to capture brain signals as the data source for constructing HDT. By collecting and analyzing these signals, the framework not only minimizes device heterogeneity and enhances data collection efficiency, but also provides richer and more nuanced physiological and psychological data for constructing personalized HDTs. To this end, we further propose a bio-inspired neuromorphic computing learning model based on the Spiking Neural Network (SNN). This model utilizes discrete neural spikes to emulate the way of human brain processes information, thereby enhancing the system's ability to process data effectively while reducing energy consumption. Additionally, we integrate a Federated Learning (FL) strategy within the model to strengthen data privacy. We then conduct a case study to demonstrate the performance of our proposed twofold bio-inspired scheme. Finally, we present several challenges and promising directions for future research of HDTs driven by bio-inspired technologies.

cs.HC↗

arXiv2024

Turnkey locking of quantum-dot lasers directly grown on Si

Ultra-low-noise laser sources are crucial for a variety of applications, including microwave synthesizers, optical gyroscopes, and the manipulation of quantum systems. Silicon photonics has emerged as a promising solution for high-coherence applications due to its ability to reduce system size, weight, power consumption, and cost (SWaP-C). Semiconductor lasers based on self-injection locking (SIL) have reached fiber laser coherence, but typically require a high-Q external cavity to suppress coherence collapse through frequency-selective feedback. Lasers based on external-cavity locking (ECL) are a low-cost and turnkey operation option, but their coherence is generally inferior to SIL lasers. In this work, we demonstrate quantum-dot (QD) lasers grown directly on Si that achieve SIL laser coherence under turnkey ECL. The high-performance QD laser offers a scalable and low-cost heteroepitaxial integration platform. Moreover, the QD laser's chaos-free nature enables a 16 Hz Lorentzian linewidth under ECL using a low-Q external cavity, and improves the frequency noise by an additional order of magnitude compared to conventional quantum-well lasers.

physics.optics↗

arXiv2023

Observation of Floquet Chern insulators of light

The field of topological photonics studies unique and robust photonic systems that are immune to defects and disorders due to the protection of their underlying topological phases. Mostly implemented in static systems, the studied topological phases are often defined in linear photonic band structures. In this study, we experimentally demonstrate Floquet Chern insulators in periodically driven nonlinear photonic crystals, where the topological phase is controlled by the polarization and the frequency of the driving field. Mediated by strong material nonlinearity, our system enters what we call the 'strong Floquet coupling regime', where the photonic Floquet bands cross and open new energy gaps with non-trivial topology as observed in our transient sum-frequency generation measurements. Our work offers new opportunities to explore the role of classical optical nonlinearity in topological phases and their applications in nonlinear optoelectronics.

physics.optics↗

arXiv2023

Dislocation-induced structural and luminescence degradation in InAs quantum dot emitters on silicon

We probe the extent to which dislocations reduce carrier lifetimes and alter luminescence and growth morphology in InAs quantum dots (QD) grown on silicon. These heterostructures are key ingredients to achieving a highly reliable monolithically integrated light source on silicon necessary for photonic integrated circuits. We find up to 20-30% shorter carrier lifetimes at spatially resolved individual dislocations from both the QD ground and excited states at room temperature using time-resolved cathodoluminescence spectroscopy. These lifetimes are consistent with differences in the intensity measured under steady-state excitation suggesting that trap-assisted recombination limits the minority carrier lifetime, even away from dislocations. Our techniques also reveal the dramatic growth of misfit dislocations in these structures under carrier injection fueled by recombination-enhanced dislocation glide and III-V/Si residual strain. Beyond these direct effects of increased nonradiative recombination, we find the long-range strain field of misfit dislocations deeper in the defect filter layers employed during III-V/Si growth alter the QD growth environment and introduce a crosshatch-like variation in the QD emission color and intensity when the filter layer is positioned close to the QD emitter layer. Sessile threading dislocations generate even more egregious hillock defects that also reduce emission intensities by altering layer thicknesses, as measured by transmission electron microscopy and atom probe tomography. Our work presents a more complete picture of the impacts of dislocations relevant for the development of light sources for scalable silicon photonic integrated circuits.

cond-mat.mes-hall↗

arXiv2022

Electrically pumped quantum-dot lasers grown on 300 mm patterned Si photonic wafers

Monolithic integration of quantum dot (QD) gain materials onto Si photonic platforms via direct epitaxial growth is a promising solution for on-chip light sources. Recent developments have demonstrated superior device reliability in blanket hetero-epitaxy of III-V devices on Si at elevated temperatures. Yet, thick, defect management epi designs prevent vertical light coupling from the gain region to the Si-on-Insulator (SOI) waveguides. Here, we demonstrate the first electrically pumped QD lasers grown on a 300 mm patterned (001) Si wafer with a butt-coupled configuration by molecular beam epitaxy (MBE). Unique growth and fabrication challenges imposed by the template architecture have been resolved, contributing to continuous wave lasing to 60 {\deg}C and a maximum double-side output power of 126.6 mW at 20 {\deg}C with a double-side wall plug efficiency of 8.6%. The potential for robust on-chip laser operation and efficient low-loss light coupling to Si photonic circuits makes this heteroepitaxial integration platform on Si promising for scalable and low-cost mass production.

physics.optics↗

arXiv2020

Defect filtering for thermal expansion induced dislocations in III-V lasers on silicon

Epitaxially integrated III-V semiconductor lasers for silicon photonics have the potential to dramatically transform information networks, but currently, dislocations limit performance and reliability even in defect tolerant InAs quantum dot (QD) based lasers. Despite being below critical thickness, QD layers in these devices contain previously unexplained misfit dislocations, which facilitate non-radiative recombination. We demonstrate here that these misfit dislocations form during post-growth cooldown due to the combined effects of (1) thermal-expansion mismatch between the III-V layers and silicon and (2) precipitate and alloy hardening in the active region. By incorporating an additional sub-critical thickness, indium-alloyed misfit dislocation trapping layer, we leverage these mechanical hardening effects to our advantage, successfully displacing 95% of misfit dislocations from the QD layer in model structures. Unlike conventional dislocation mitigation strategies, the trapping layer reduces neither the number of threading dislocations nor the number of misfit dislocations. It simply shifts the position of misfit dislocations away from the QD layer, reducing the defects' impact on luminescence. In full lasers, adding a misfit dislocation trapping layer both above and below the QD active region displaces misfit dislocations and substantially improves performance: we measure a twofold reduction in lasing threshold currents and a greater than threefold increase in output power. Our results suggest that devices employing both traditional threading dislocation reduction techniques and optimized misfit dislocation trapping layers may finally lead to fully integrated, commercially viable silicon-based photonic integrated circuits.

physics.app-ph↗