arXiv subjects

Tingjun Chen

Publications and source records attributed to Tingjun Chen.

At least 19 recordsLinked to original sources

arXiv2026

Trapped Ion Quantum Networking and Telecommunications Coexisting on One Fiber

Research into long-distance quantum memory-based networking to date has exclusively used dark fibers. This avoids the detector background from telecommunications (telecom) traffic, but as a result excludes many fibers deployed in the field. If memory-photon entanglement and telecom signals coexist on one fiber, the entire classical fiber infrastructure becomes available for quantum links. We present the first experimental demonstration of such coexistence. Ion-photon entanglement using 1092 nm photons emitted by a Strontium-88 ion is distributed over a deployed 2.8 km fiber loop which carries Ethernet and 5G traffic. All classical control signals required to coordinate the quantum transmitter and receiver systems co-propagate on the same fiber. These include fiber sensing for polarization stabilization. Our results demonstrate that memory-based quantum networks can be realized on active classical network infrastructure.

quant-ph↗

arXiv2026

Feature Anchors for Time-Series Sensor-Based Human Activity Recognition

Wearable Human Activity Recognition (HAR) still lacks a representation that is both explicit and adaptable. Handcrafted time-series features (TSFs) capture meaningful motion statistics and remain competitive on standard benchmarks, but they are usually used as fixed preprocessing outputs. Deep models learn adaptable representations directly from raw signals, but those representations are typically latent and difficult to inspect. We address this gap by treating handcrafted TSFs as feature anchors: explicit intermediate representations that remain inside the model and are adjusted by neural context instead of being discarded. We propose the Temporal Conditioning Network for Feature Anchors (TCNet), which extracts handcrafted anchors, encodes complementary time-domain and frequency-domain context from raw IMU windows, and predicts context-conditioned scale, bias, and gating parameters to modulate anchor groups directly in feature space. This design keeps anchor semantics visible while allowing the representation to adapt to the classification objective. Across five HAR benchmarks, TCNet achieves 70.2% mF1 on USC-HAD, 85.1% mF1 on Daphnet, 93.9% mF1 on MHealth, and 94.5% mF1 on PAMAP2. Relative to rTsfNet, it improves by 4.5 points on USC-HAD, 14.6 points on Daphnet, and 6.5 points on MHealth. Ablations show that the gains come primarily from anchor guidance rather than simple branch fusion, and feature-space analyses indicate that several discriminative TSF families are not reliably accessible in standard latent representations. These results suggest that, for HAR, handcrafted TSFs are most useful when they remain explicit and adaptable within the model. The code is available at: https://github.com/ni-x-lab/TCNet-har

cs.HC↗

arXiv2026

MARS: Efficient, Adaptive Co-Scheduling for Heterogeneous Agentic Systems

Large language models (LLMs) are increasingly deployed as the execution core of autonomous agents rather than as standalone text generators. Agentic workloads induce a temporal shift from single-turn inference to multi-turn LLM-tool loops, and a spatial shift from chat-scale, GPU-only execution to repository-scale, GPU-CPU co-located execution. Consequently, coordinating heterogeneous resource demands of agentic execution has emerged as a critical system challenge. We design and implement MARS, an efficient and adaptive co-scheduling system that globally coordinates heterogeneous agentic workloads under coupled GPU-CPU resource pressure. By establishing holistic visibility across GPU inference and CPU tool execution via a unified information stream, an external control plane in MARS decouples admission from execution to prevent heterogeneous resource oversubscription. An internal agent-centric scheduler further minimizes the end-to-end critical path by prioritizing latency-sensitive continuations and adaptively retaining KV cache state only when warm resumption yields a latency benefit. Our evaluations show that MARS reduces end-to-end latency by up to 5.94x while maintaining nearly maximal system throughput. We further integrate MARS as the serving backend for the OpenHands coding agent framework, demonstrating its real-world effectiveness by accelerating end-to-end task completion time by up to 1.87x. Our source code is publicly available at https://github.com/Afterglow231/MARS_preview .

cs.OS↗

arXiv2026

Real-Time and Scalable Zak-OTFS Receiver Processing on GPUs

Orthogonal time frequency space (OTFS) modulation offers superior robustness to high-mobility channels compared to conventional orthogonal frequency-division multiplexing (OFDM) waveforms. However, its explicit delay-Doppler (DD) domain representation incurs substantial signal processing complexity, especially with increased DD domain grid sizes. To address this challenge, we present a scalable, real-time Zak-OTFS receiver architecture on GPUs through hardware--algorithm co-design that exploits DD-domain channel sparsity. Our design leverages compact matrix operations for key processing stages, a branchless iterative equalizer, and a structured sparse channel matrix of the DD domain channel matrix to significantly reduce computational and memory overhead. These optimizations enable low-latency processing that consistently meets the 99.9-th percentile real-time processing deadline. The proposed system achieves up to 906.52 Mbps throughput with a DD grid size of (16384,32) using 16QAM modulation over 245.76 MHz bandwidth. Extensive evaluations under a Vehicular-A channel model demonstrate strong scalability and robust performance across CPU (Intel Xeon) and multiple GPU platforms (NVIDIA Jetson Orin, RTX 6000 Ada, A100, and H200), highlighting the effectiveness of compute-aware Zak-OTFS receiver design for next-generation (NextG) high-mobility communication systems.

eess.SP↗

arXiv2026

Skilled AI Agents for Embedded and IoT Systems Development

Large language models (LLMs) and agentic systems have shown promise for automated software development, but applying them to hardware-in-the-loop (HIL) embedded and Internet-of-Things (IoT) systems remains challenging due to the tight coupling between software logic and physical hardware behavior. Code that compiles successfully may still fail when deployed on real devices because of timing constraints, peripheral initialization requirements, or hardware-specific behaviors. To address this challenge, we introduce a skills-based agentic framework for HIL embedded development together with IoT-SkillsBench, a benchmark designed to systematically evaluate AI agents in real embedded programming environments. IoT-SkillsBench spans three representative embedded platforms, 23 peripherals, and 42 tasks across three difficulty levels, where each task is evaluated under three agent configurations (no-skills, LLM-generated skills, and human-expert skills) and validated through real hardware execution. Across 378 hardware validated experiments, we show that concise human-expert skills with structured expert knowledge enable near-perfect success rates across platforms.

cs.SE↗

arXiv2026

RISE: Real-time Image Processing for Spectral Energy Detection and Localization

Energy detection is widely used for spectrum sensing, but accurately localizing the time and frequency occupation of signals in real-time for efficient spectrum sharing remains challenging. To address this challenge, we present RISE, a software-based spectrum sensing system designed for real-time signal detection and localization. RISE treats time-frequency spectrum plots as images and applies adaptive thresholding, morphological operations, and connected component labeling with a multi-threaded architecture. We evaluate RISE using both synthetic data and controlled over-the-air (OTA) experiments across diverse signal types. Results show that RISE satisfies real-time latency constraints while achieving a probability of detection of 80.42% at an intersection-over-union (IoU) threshold of 0.4. RISE sustains a raw I/Q input rate of 3.2 Gbps for 100 MHz bandwidth sensing with time and frequency resolutions of 10.24 us and 97.6 kHz, respectively. Compared to Searchlight, a representative energy-based method, RISE achieves 20.51x lower latency and 22.31% higher IoU. Compared to machine learning baselines, RISE improves IoU by 56.02% over DeepRadar while meeting the real-time deadline, which a GPU-accelerated U-Net exceeds by 213.38x.

cs.NI↗

arXiv2026

Agentic AI for Scalable and Robust Optical Systems Control

We present AgentOptics, an agentic AI framework for high-fidelity, autonomous optical system control built on the Model Context Protocol (MCP). AgentOptics interprets natural language tasks and executes protocol-compliant actions on heterogeneous optical devices through a structured tool abstraction layer. We implement 64 standardized MCP tools across 8 representative optical devices and construct a 410-task benchmark to evaluate request understanding, role-aware responses, multi-step coordination, robustness to linguistic variation, and error handling. We assess two deployment configurations--commercial online LLMs and locally hosted open-source LLMs--and compare them with LLM-based code generation baselines. AgentOptics achieves 87.7%--99.0% average task success rates, significantly outperforming code-generation approaches, which reach up to 50% success. We further demonstrate broader applicability through five case studies extending beyond device-level control to system orchestration, monitoring, and closed-loop optimization. These include DWDM link provisioning and coordinated monitoring of coherent 400 GbE and analog radio-over-fiber (ARoF) channels; autonomous characterization and bias optimization of a wideband ARoF link carrying 5G fronthaul traffic; multi-span channel provisioning with launch power optimization; closed-loop fiber polarization stabilization; and distributed acoustic sensing (DAS)-based fiber monitoring with LLM-assisted event detection. These results establish AgentOptics as a scalable, robust paradigm for autonomous control and orchestration of heterogeneous optical systems.

eess.SY↗

arXiv2025

DecodeX: Exploring and Benchmarking of LDPC Decoding across CPU, GPU, and ASIC Platforms

Emerging virtualized radio access networks (vRANs) demand flexible and efficient baseband processing across heterogeneous compute substrates. In this paper, we present DecodeX, a unified benchmarking framework for evaluating low-density parity-check (LDPC) decoding acceleration across different hardware platforms. DecodeX integrates a comprehensive suite of LDPC decoder implementations, including kernels, APIs, and test vectors for CPUs (FlexRAN), GPUs (Aerial and Sionna-RK), and ASIC (ACC100), and can be readily extended to additional architectures and configurations. Using DecodeX, we systematically characterize how different platforms orchestrate computation-from threading and memory management to data movement and accelerator offload-and quantify the resulting decoding latency under varying Physical layer parameters. Our observations reveal distinct trade-offs in parallel efficiency and offload overhead, showing that accelerator gains strongly depend on data-movement and workload granularity. Building on these insights, we discuss how cross-platform benchmarking can inform adaptive scheduling and co-design for future heterogeneous vRANs, enabling scalable and energy-efficient baseband processing for NextG wireless systems.

cs.NI↗

arXiv2025

Optical Link Tomography: First Field Trial and 4D Extension

Optical link tomography (OLT) is a rapidly evolving field that allows the multi-span, end-to-end visualization of optical power along fiber links in multiple dimensions from network endpoints, solely by processing signals received at coherent receivers. This paper has two objectives: (1) to report the first field trial of OLT, using a commercial transponder under standard DWDM transmission, and (2) to extend its capability to visualize across 4D (distance, time, frequency, and polarization), allowing for locating and measuring multiple QoT degradation causes, including time-varying power anomalies, spectral anomalies, and excessive polarization dependent loss. We also address a critical aspect of OLT, i.e., its need for high fiber launch power, by improving power profile signal-to-noise ratio through averaging across all available dimensions. Consequently, multiple loss anomalies in a field-deployed link are observed even at launch power lower than the system-optimal level. The applications and use cases of OLT from network commissioning to provisioning and operation for current and near-term network scenarios are also discussed.

eess.SP↗

arXiv2025

IoT-MCP: Bridging LLMs and IoT Systems Through Model Context Protocol

The integration of Large Language Models (LLMs) with Internet-of-Things (IoT) systems faces significant challenges in hardware heterogeneity and control complexity. The Model Context Protocol (MCP) emerges as a critical enabler, providing standardized communication between LLMs and physical devices. We propose IoT-MCP, a novel framework that implements MCP through edge-deployed servers to bridge LLMs and IoT ecosystems. To support rigorous evaluation, we introduce IoT-MCP Bench, the first benchmark containing 114 Basic Tasks (e.g., ``What is the current temperature?'') and 1,140 Complex Tasks (e.g., ``I feel so hot, do you have any ideas?'') for IoT-enabled LLMs. Experimental validation across 22 sensor types and 6 microcontroller units demonstrates IoT-MCP's 100% task success rate to generate tool calls that fully meet expectations and obtain completely accurate results, 205ms average response time, and 74KB peak memory footprint. This work delivers both an open-source integration framework (https://github.com/Duke-CEI-Center/IoT-MCP-Servers) and a standardized evaluation methodology for LLM-IoT systems.

cs.DC↗

arXiv2025

Chameleon: Integrated Sensing and Communication with Sub-Symbol Beam Switching in mmWave Networks

Next-generation cellular networks are envisioned to integrate sensing capabilities with communication, particularly in the millimeter-wave (mmWave) spectrum, where beamforming using large-scale antenna arrays enables directional signal transmissions for improved spatial multiplexing. In current 5G networks, however, beamforming is typically designed either for communication or sensing (e.g., beam training during link establishment). In this paper, we present Chameleon, a novel framework that augments and rapidly switches beamformers during each demodulation reference signal (DMRS) symbol to achieve integrated sensing and communication (ISAC) in 5G mmWave networks. Each beamformer introduces an additional sensing beam toward target angles while maintaining the communication beams toward multiple users. We implement Chameleon on a 28 GHz software-defined radio testbed supporting over-the-air 5G physical downlink shared channel (PDSCH) transmissions. Extensive experiments in open environments show that Chameleon achieves multi-user communication with a sum data rate of up to 0.80 Gbps across two users. Simultaneously, Chameleon employs a beamformer switching interval of only 0.24 {\mu}s, therefore producing a 31x31-point 2D imaging within just 0.875 ms. Leveraging machine learning, Chameleon further enables object localization with median errors of 0.14 m (distance) and 0.24{\deg} (angle), and material classification with 99.0% accuracy.

cs.NI↗

arXiv2025

BatStation: Toward In-Situ Radar Sensing on 5G Base Stations with Zero-Shot Template Generation

The coexistence between incumbent radar signals and commercial 5G signals necessitates a versatile and ubiquitous radar sensing for efficient and adaptive spectrum sharing. In this context, leveraging the densely deployed 5G base stations (BS) for radar sensing is particularly promising, offering both wide coverage and immediate feedback to 5G scheduling. However, the targeting radar signals are superimposed with concurrent 5G uplink transmissions received by the BS, and practical deployment also demands a lightweight, portable radar sensing model. This paper presents BatStation, a lightweight, in-situ radar sensing framework seamlessly integrated into 5G BSs. BatStation leverages uplink resource grids to extract radar signals through three key components: (i) radar signal separation to cancel concurrent 5G transmissions and reveal the radar signals, (ii) resource grid reshaping to align time-frequency resolution with radar pulse characteristics, and (iii) zero-shot template correlation based on a portable model trained purely on synthetic data that supports detection, classification, and localization of radar pulses without fine-tuning using experimental data. We implement BatStation on a software-defined radio (SDR) testbed and evaluate its performance with real 5G traffic in the CBRS band. Results show robust performance across diverse radar types, achieving detection probabilities of 97.02% (PUCCH) and 79.23% (PUSCH), classification accuracy up to 97.00%, and median localization errors of 2.68-6.20 MHz (frequency) and 24.6-32.4 microseconds (time). Notably, BatStation achieves this performance with a runtime latency of only 0.11/0.94 ms on GPU/CPU, meeting the real-time requirement of 5G networks.

cs.NI↗

arXiv2025

Nexus: Efficient and Scalable Multi-Cell mmWave Baseband Processing with Heterogeneous Compute

The rapid adoption of 5G New Radio (NR), particularly in the millimeter-wave (mmWave) spectrum, imposes stringent demands on the flexibility, scalability, and efficiency of baseband processing. While virtualized Radio Access Networks (vRANs) enable dynamic spectrum sharing across cells, compute resource allocation for baseband processing, especially in multi-cell deployments with heterogeneous workloads, remains underexplored. In this paper, we present NEXUS, the first system to realize real-time, virtualized multi-cell mmWave baseband processing on a single server with heterogeneous compute resources. NEXUS integrates software-based digital signal processing pipelines with hardware-accelerated LDPC decoding, and introduces a novel framework for sharing Intel's ACC100 eASIC across multiple CPU cores via virtual functions (VFs). For single-cell operation, NEXUS employs a random forest (RAF)-based model that predicts the most energy-efficient resource allocation for the given cell configuration with microsecond-level inference latency and high accuracy. For multi-cell scenarios, NEXUS introduces a power-aware scheduler that incorporates a lightweight contention model to adjust resource allocation strategies under concurrent execution. Through extensive evaluation across various Frequency Range 2 (FR2) cell configurations, we show that NEXUS supports up to 16 concurrent cells under full load, achieving 5.37Gbps aggregate throughput, while reducing the multi-cell scheduling search space by orders of magnitude. These results demonstrate that virtualized, resource-aware baseband processing is both practical and efficient for next-generation vRAN systems.

cs.NI↗

arXiv2025

Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrum

Accurate modeling of the gain spectrum in Erbium-Doped Fiber Amplifiers (EDFAs) is essential for optimizing optical network performance, particularly as networks evolve toward multi-vendor solutions. In this work, we propose a generalized few-shot transfer learning architecture based on a Semi-Supervised Self-Normalizing Neural Network (SS-NN) that leverages internal EDFA features - such as VOA input or output power and attenuation, to improve gain spectrum prediction. Our SS-NN model employs a two-phase training strategy comprising unsupervised pre-training with noise-augmented measurements and supervised fine-tuning with a custom weighted MSE loss. Furthermore, we extend the framework with transfer learning (TL) techniques that enable both homogeneous (same-feature space) and heterogeneous (different-feature sets) model adaptation across booster, preamplifier, and ILA EDFAs. To address feature mismatches in heterogeneous TL, we incorporate a covariance matching loss to align second-order feature statistics between source and target domains. Extensive experiments conducted across 26 EDFAs in the COSMOS and Open Ireland testbeds demonstrate that the proposed approach significantly reduces the number of measurements requirements on the system while achieving lower mean absolute errors and improved error distributions compared to benchmark methods.

cs.NI↗

arXiv2025

RaGNNarok: A Light-Weight Graph Neural Network for Enhancing Radar Point Clouds on Unmanned Ground Vehicles

Low-cost indoor mobile robots have gained popularity with the increasing adoption of automation in homes and commercial spaces. However, existing lidar and camera-based solutions have limitations such as poor performance in visually obscured environments, high computational overhead for data processing, and high costs for lidars. In contrast, mmWave radar sensors offer a cost-effective and lightweight alternative, providing accurate ranging regardless of visibility. However, existing radar-based localization suffers from sparse point cloud generation, noise, and false detections. Thus, in this work, we introduce RaGNNarok, a real-time, lightweight, and generalizable graph neural network (GNN)-based framework to enhance radar point clouds, even in complex and dynamic environments. With an inference time of just 7.3 ms on the low-cost Raspberry Pi 5, RaGNNarok runs efficiently even on such resource-constrained devices, requiring no additional computational resources. We evaluate its performance across key tasks, including localization, SLAM, and autonomous navigation, in three different environments. Our results demonstrate strong reliability and generalizability, making RaGNNarok a robust solution for low-cost indoor mobile robots.

cs.RO↗

arXiv2025

Machine Intelligence on Wireless Edge Networks

Machine intelligence on edge devices enables low-latency processing and improved privacy, but is often limited by the energy and delay of moving and converting data. Current systems frequently avoid local model storage by sending queries to a server, incurring uplink cost, network latency, and privacy risk. We present the opposite approach: broadcasting model weights to clients that perform inference locally using in-physics computation inside the radio receive chain. A base station transmits weights as radio frequency (RF) waveforms; the client encodes activations onto the waveform and computes the result using existing mixer and filter stages, RF components already present in billions of edge devices such as cellphones, eliminating repeated signal conversions and extra hardware. Analysis shows that thermal noise and nonlinearity create an optimal energy window for accurate analog inner products. Hardware-tailored training through a differentiable RF chain preserves accuracy within this regime. Circuit-informed simulations, consistent with a companion experiment, demonstrate reduced memory and conversion overhead while maintaining high accuracy in realistic wireless edge scenarios.

cs.ET↗

arXiv2025

Phantora: Maximizing Code Reuse in Simulation-based Machine Learning System Performance Estimation

Modern machine learning (ML) training workloads place substantial demands on both computational and communication resources. Consequently, accurate performance estimation has become increasingly critical for guiding system design decisions, such as the selection of parallelization strategies, cluster configurations, and hardware provisioning. Existing simulation-based performance estimation requires reimplementing the ML framework in a simulator, which demands significant manual effort and is hard to maintain as ML frameworks evolve rapidly. This paper introduces Phantora, a hybrid GPU cluster simulator designed for performance estimation of ML training workloads. Phantora executes unmodified ML frameworks as is within a distributed, containerized environment. Each container emulates the behavior of a GPU server in a large-scale cluster, while Phantora intercepts and simulates GPU- and communication-related operations to provide high-fidelity performance estimation. We call this approach hybrid simulation of ML systems, in contrast to traditional methods that simulate static workloads. The primary advantage of hybrid simulation is that it allows direct reuse of ML framework source code in simulation, avoiding the need for reimplementation. Our evaluation shows that Phantora provides accuracy comparable to static workload simulation while supporting three state-of-the-art LLM training frameworks out-of-the-box. In addition, Phantora operates on a single GPU, eliminating the need for the resource-intensive trace collection and workload extraction steps required by traditional trace-based simulators. Phantora is open-sourced at https://github.com/QDelta/Phantora.

cs.DC↗

arXiv2025

Disaggregated Deep Learning via In-Physics Computing at Radio Frequency

Modern edge devices, such as cameras, drones, and Internet-of-Things nodes, rely on deep learning to enable a wide range of intelligent applications, including object recognition, environment perception, and autonomous navigation. However, deploying deep learning models directly on the often resource-constrained edge devices demands significant memory footprints and computational power for real-time inference using traditional digital computing architectures. In this paper, we present WISE, a novel computing architecture for wireless edge networks designed to overcome energy constraints in deep learning inference. WISE achieves this goal through two key innovations: disaggregated model access via wireless broadcasting and in-physics computation of general complex-valued matrix-vector multiplications directly at radio frequency. Using a software-defined radio platform with wirelessly broadcast model weights over the air, we demonstrate that WISE achieves 95.7% image classification accuracy with ultra-low operation power of 6.0 fJ/MAC per client, corresponding to a computation efficiency of 165.8 TOPS/W. This approach enables energy-efficient deep learning inference on wirelessly connected edge devices, achieving more than two orders of magnitude improvement in efficiency compared to traditional digital computing.

cs.ET↗