147 papers
This white paper presents a community-driven vision to prioritize research and development in hardware-based machine learning systems—leveraging emerging technologies like AI, silicon microelectronics, and quantum processing—to address the unprecedented data challenges and enable real-time scientific discovery in the next generation of particle physics experiments.
This paper proposes a rate-aware cell switching optimization framework for HAPS-assisted 6G networks that integrates realistic propagation losses and multi-objective formulation to significantly reduce data rate degradation while maintaining energy efficiency and user connectivity, validated through both system-level simulations and Sionna-OAI emulation.
This paper proposes and experimentally validates a flexible multi-target over-the-air emulation framework for large-scale ISAC base stations that utilizes an amplitude and phase modulation network to simulate diverse sensing targets without costly hardware, overcoming scalability challenges through optimized probe array configurations based on strictly diagonally dominant matrices.
This paper proposes a path planning strategy for an autonomous underwater vehicle that fuses local conductivity-temperature-depth measurements with acoustic transmission loss data via an unscented Kalman filter to accurately estimate the sound speed profile while minimizing prediction uncertainty.
This paper introduces a layered acoustic wave (LAW) platform featuring a quasi-infinite multifunctional top layer that simultaneously suppresses acoustomigration and temperature rise, achieving a 70% reduction in heating and a record-breaking power density threshold of 45.61 dBm/mm² for high-frequency (>2 GHz) acoustic transducers.
This paper proposes a novel near-field channel estimation framework for extremely large-scale intelligent reflecting surfaces that leverages tensor modalization and harmonic analysis-inspired decoupling to create a compact distance-dependent codebook, achieving significantly higher accuracy and lower complexity than existing polar-domain methods.
This paper proposes a 3D spectrum awareness framework for Radio Dynamic Zones that leverages matrix completion to outperform ordinary Kriging in dense measurement scenarios while demonstrating the superiority of simple and trans-Gaussian Kriging in sparse conditions, alongside the benefits of integrating multi-altitude data for improved prediction accuracy.
This paper introduces Spyglass, a cost-effective spectrum sensor that utilizes a switched array and the Searchlite protocol to achieve single-shot, multi-channel Angle of Arrival (AoA) estimation with high accuracy in dense wireless environments.
This paper proposes a robust, efficient three-point movable antenna placement strategy for near-field wireless sensing that minimizes the worst-case squared position error bound by leveraging centro-symmetric distribution and moment-based analysis, outperforming conventional fixed arrays while matching exhaustive search benchmarks with negligible computational complexity.
This paper presents a comprehensive analysis of UAV-based 3D spectrum sensing and Radio Environment Map (REM) reconstruction, demonstrating that robust algorithms like simple Kriging and Gaussian Process Regression, combined with altitude-aware trajectory planning, increased bandwidth, and airframe-induced antenna pattern calibration, significantly enhance mapping accuracy even under sparse sampling and complex shadowing conditions.
The paper introduces Geo-ADAPT-VQE, a geometry-aware adaptive variational quantum eigensolver that utilizes natural gradients for operator selection to achieve faster, more stable convergence and significantly shorter ansatz circuits compared to existing gradient-based methods.
This paper proposes and compares two novel paradigms for multi-modal intelligent channel modeling in 6G systems—fine-tuned Large Language Models (LLM4CM) and a pre-trained Wireless Channel Foundation Model (WiCo)—both grounded in the Synesthesia of Machines concept to enable precise, scalable, and physically interpretable channel prediction across complex communication environments.
This paper proposes a variational quantum sensing framework that utilizes a quantum circuit-optimized probe to learn environmental features from radio-frequency signals, demonstrating through ray-tracing simulations that this approach enables robust localization with no deployment channel measurements and superior sensitivity to weak or obstructed signals compared to classical baselines.
This paper presents a scalable, non-intrusive in-situ timing diagnosis architecture for SRAM-based FPGAs that utilizes distributed phase-swept delay monitoring to probabilistically characterize and spatially differentiate between global power-distribution-network degradation and localized configuration-induced routing perturbations during normal operation.
This paper introduces Deep Unfolded Full Waveform Inversion (DUFWI), a physics-informed deep learning method that achieves real-time, high-quality speed-of-sound reconstruction for edema detection by overcoming the computational intensity and local minima issues of traditional inversion techniques.
This paper analyzes the fundamental performance limits of a secure MIMO integrated sensing and communications channel with multiple-antenna eavesdroppers and sensing receivers, deriving a quasi-optimal precoder structure and proposing a practical two-stage iterative algorithm to maximize the weighted sum rate.
This paper introduces ParaS2S, a reinforcement learning framework and corresponding benchmark (ParaS2SBench) that utilizes a novel PolyTone-trained automatic judge to effectively align speech-to-speech models with paralinguistic cues, achieving superior performance in response content and speaking style compared to supervised fine-tuning while requiring fewer paired demonstrations.
This paper presents a real-time, on-device method for quantifying sensor output uncertainty by propagating calibration parameter quantization errors, demonstrating significant computational speedups over Monte Carlo methods and improved edge detection accuracy on low-power embedded hardware.
This paper proposes Circular ECG Super-Resolution (CECGSR), a closed-loop framework that leverages negative feedback and a Plug-and-Play strategy to outperform existing open-loop methods in reconstructing high-resolution ECG signals from low-resolution, noisy inputs.
This paper proposes a unified multicarrier waveform framework for 6G networks by systematically characterizing and comparing state-of-the-art one-dimensional and two-dimensional modulation schemes to guide their selection based on channel conditions and performance requirements.