arXiv subjects

Luping Xiang

Publications and source records attributed to Luping Xiang.

At least 19 recordsLinked to original sources

arXiv2026

Shaping Delay-Doppler Ambiguity in Practical OFDM-ISAC

Orthogonal frequency-division multiplexing (OFDM) is a key waveform for integrated sensing and communication (ISAC). Existing OFDM ambiguity analyses, however, typically assume fully occupied data-only waveforms, whereas practical frames contain direct-current and edge-guard nulls, fixed pilots, and random payload symbols. This mixed resource structure reshapes the self-ambiguity function and induces prominent sidelobes in the sensing region of interest (ROI). We therefore propose ROI-oriented deep block-unitary precoded OFDM (DBU-OFDM), which combines resource-specific trainable unitary transformations with dedicated sensing subcarriers to suppress ROI sidelobes while preserving the prescribed resource support. We develop a constraint-preserving parameterization capable of representing arbitrary unitary matrices and an ROI-aware sensing-support initialization. We prove periodic autocorrelation function (P-ACF) invariance under phase-only optimization and zero-delay Doppler-cut invariance under arbitrary unitary transformations. Numerical results corroborate the P-ACF optimality of cyclic-prefix OFDM (CP-OFDM) and provide numerical support for the conjecture that conventional OFDM is globally optimal in the considered aperiodic autocorrelation function (A-ACF) setting. Unitary pilot optimization improves the ROI peak-to-sidelobe ratio by over 2 dB, while combining it with dedicated sensing subcarriers yields gains ranging from several to tens of dB. Under the considered configuration, the A-ACF mode also provides substantially greater ROI ambiguity-shaping capability than the P-ACF mode.

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arXiv2026

AI-RAN on NPUs: Baseband Processing Without Baseband Chips

AI-RAN aims to unify artificial intelligence and radio access network workloads on a shared compute substrate. While this paradigm has so far been demonstrated primarily on Graphics Processing Units (GPUs), it remains unclear whether Neural Processing Units (NPUs), which are AI accelerators optimized for inference, can also support wireless baseband processing. Here, we provide the first affirmative answer by resolving the fundamental mismatch between baseband workloads and NPU architecture. A computational isomorphism exists: matrix and vector engines NPUs dedicate to inference inherently cover physical-layer operations. Yet NPU architectures are natively shaped for dense-tensor AI inference, not baseband. This architectural mismatch surfaces as opposing optimization objectives: traditional baseband minimizes arithmetic operations, whereas NPU performance demands maximizing engine utilization. We close this gap by reconstructing communication algorithms onto AI compute primitives, prioritizing engine utilization over arithmetic count. We validate this with a complete OFDM transceiver on an Ascend 310B1 edge NPU, demonstrating end-to-end over-the-air transmission via USRP X300 at 3.0 GHz.

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arXiv2026

A Token/KV-Cache Communication Media Selection and Resource Allocation Strategy for Multi-Agent Collaboration

The convergence of large language models (LLMs) with 6G networks is fostering a paradigm of autonomous multi-agent cooperation, which in turn is expected to substantially increase east-west traffic. Although latent-space interaction mechanisms can enable more efficient collaboration than symbolic natural-language (NL) exchanges, prior work often abstracts away the associated communication overhead under practical wireless constraints. In embodied multi-agent settings, heterogeneous interaction media incur disparate inference and transmission costs, thereby inducing an inherent end-to-end (E2E) latency trade-off. To address this, we propose a joint design that integrates communication-media selection with wireless resource allocation. Through analytical characterization and simulation-based evaluation, we show that neither token-based transmission nor key-value (KV) cache-based transmission is uniformly optimal across operating regimes, as performance depends critically on system parameters such as available computational resources and channel conditions. Accordingly, we formulate a joint optimization problem aimed at minimizing the E2E latency of multi-agent collaboration and develop a low-complexity joint media selection and resource allocation (JMSRA) algorithm. Numerical results further confirm that, by adaptively coordinating the interaction media and bandwidth allocation over heterogeneous links, the proposed scheme achieves markedly reduced E2E latency relative to conventional NL-only and KV-cache-only baselines, enabling efficient and robust multi-agent collaboration in future wireless networks.

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arXiv2026

6G Communication Networks Enabling Embodied Agents: Architecture and Prototype

Embodied agents, which couple intelligent decision-making with physical actuation in the real world, impose far more stringent and heterogeneous communication requirements than purely software-based agents. While 6G promises sub-millisecond latency, ultra-high reliability, native intelligence, and integrated sensing, systematic studies on how to exploit these capabilities for embodied agent communication remain limited. This article investigates 6G-enabled communication systems for embodied agents from both conceptual and engineering perspectives. First, we review the concept, embodiment value of embodied agents, and clarify their distinctions from disembodied agents. Then, we analyse the symbiotic relationship between embodied agents and 6G networks. We highlight how key 6G enablers can support the stringent requirements of human-robot interaction. Furthermore, we demonstrate the proactive role of embodied agents in bolstering communication networks through coverage extension, environmental sensing, and physical world understanding. Building on these insights, we propose a hierarchical communication architecture for human-robot remote interaction, comprising a human-intent perception layer, an open radio access network (O-RAN)-based transport layer, an intelligent intermediary layer, and an embodiment layer. To validate its feasibility, we implement an end-to-end prototype that integrates a haptic device, an industrial robotic arm, an intermediary platform, and a 5G O-RAN testbed. Experimental results demonstrate millisecond-level latency and stable closed-loop operation, confirming the practicality of the proposed architecture and providing a reference for future 6G-embodied agent research and industrial deployments.

cs.RO↗

arXiv2026

Low-cost Parallel Transmission for Dense Indoor Data Collection with LoRaWAN: Time Synchronization and Resource Allocation

LoRaWAN is a compelling low-cost solution for large-scale indoor Internet of Things (IoT) data backhaul, owing to its strong penetration capability and low power consumption. However, its default pure ALOHA access mechanism leads to severe channel contention, substantial packet loss, and reduced throughput under dense, concurrent transmissions. To overcome this, we propose a lightweight out-of-band (OOB) synchronization scheme that integrates a time division multiple access (TDMA) mechanism into commercial LoRaWAN Class~A networks. Unlike approaches requiring gateway scheduling, frequent downlink signaling, or custom hardware, our method introduces a single low-cost node providing millisecond-level alignment via a dedicated OOB synchronization channel. End devices seamlessly access this channel by briefly retuning their existing LoRa transceivers. Consequently, the scheme imposes zero downlink overhead during the steady-state reporting phase, requires no hardware modifications to gateways or end devices, and remains fully backward-compatible. This design enables collision-free scheduled channel access within the configured nominal resource capacity, thereby improving throughput and reducing contention. Real-world experiments using an indoor positioning prototype demonstrate that the proposed TDMA-LoRaWAN architecture improves system throughput by over 30\% and reduces the packet loss rate from 25.8\% to 5.02\% in a 20-node indoor deployment. Furthermore, large-scale simulations corroborate these empirical findings, support the scalability analysis under larger network sizes, and indicate improved energy efficiency per successful packet in dense network settings. These combined results demonstrate the effectiveness of the proposed approach for dense indoor IoT data collection and indicate its practical potential under high uplink reporting demands.

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arXiv2026

A Camera-Cooperative ISAC Framework for Multimodal Non-Cooperative UAVs Sensing

The detection of non-cooperative unmanned aerial vehicles (UAVs) presents significant challenges for Integrated Sensing and Communication (ISAC) systems due to the inherent limitations of single-modal perception and the competition for shared communication and sensing resources. To address these challenges, this paper proposes a novel Camera-Cooperative ISAC (CC-ISAC) framework that employs multimodal sensing to enable efficient UAV beam steering and tracking. The proposed framework employs cameras for coarse-grained airspace monitoring and utilizes ISAC for fine-grained, high-precision sensing, forming a complementary perception loop that enhances both sensing accuracy and resource efficiency. Within this framework, two key modules are developed: (1) a Vision-to-Echo Data Alignment (V2EDA) model that aligns visual and echo-domain features through cross-attention mechanisms, and (2) a Multimodal Fusion-Based Estimation (MMFE) model that integrates historical multimodal data with current observations for robust state estimation. Extensive evaluations conducted on the DeepSense 6G dataset demonstrate that the proposed framework achieves an average reduction of 71% in beam steering overhead and 1.69-11.15% in tracking overhead while maintaining high angular estimation accuracy. The CC-ISAC framework effectively mitigates resource contention between sensing and communication, enabling reliable UAV surveillance while freeing substantial system resources for additional communication tasks, thereby representing a practical advancement in ISAC system design.

cs.AI↗

arXiv2026

Curriculum-Guided Heterogeneous Multi-Agent Intelligence for Multi-UAV Cooperative ISAC

Seamlessly unifying communication and sensing, sixth-generation (6G) networks are poised to transform into intelligent platforms with high spectral-energy efficiency and real-time environmental awareness. In the low-altitude economy, unmanned aerial vehicles (UAVs) enable air-ground integrated sensing and communication (ISAC) for applications such as logistics and inspection, yet most studies focus on single-UAV or homogeneous-agent designs. In contrast, this paper proposes a multi-UAV cooperative ISAC system that enables heterogeneous-agent collaboration between multiple UAVs and a ground base station (BS) for joint target sensing, tracking, and communication. The system is formulated as a posterior Cramer-Rao bound (PCRB) minimization problem under communication performance constraints, utilizing joint trajectory-beamforming optimization. To tackle the NP-hard nature of this problem, we design a curriculum-based heterogeneous-agent proximal policy optimization (C-HAPPO) algorithm, where curriculum learning guides progressive policy refinement and Kronecker/QR decomposition mitigates action dimensionality. Simulation results show that the proposed approach achieves more than a 30% improvement in sensing performance, faster convergence, and higher tracking accuracy than existing baselines, demonstrating its scalability and effectiveness for complex multi-UAV ISAC scenarios.

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arXiv2026

SimART: A Unified and Open Real-world Multimodal Simulation Platform for 6G Integrated Sensing and Communication

Research on sixth-generation (6G) integrated sensing and communication (ISAC) increasingly depends on multimodal datasets. These datasets need to jointly characterize wireless propagation, onboard sensing, and platform mobility. Existing tools cover only part of these aspects. Robotics simulators model physics and perception but not site-specific channels, while ray tracing and link level tools lack vehicle dynamics and onboard sensors. Combining them manually leads to workflows that are fragile and hard to reproduce. Rather than introducing another standalone simulator, this article presents SimART. It integrates mature robotics, ray tracing, and wireless evaluation engines into a single reproducible pipeline. The key idea is a robot operating system (ROS) backbone that both synchronizes and organizes all multimodal streams. A shared clock, a common coordinate frame, and timestamped messages keep the streams aligned in time and space, and a single rosbag recording captures the full session into one reproducible file. This design decouples the sensing front end from the wireless back end, so that any ROS-compatible simulator can be plugged in while reusing the same back end across aerial, ground, indoor, and maritime ISAC settings. On top of this backbone, SimART contributes a scene construction pipeline that converts both OpenStreetMap extracts and user-defined layouts into spatially aligned visual and electromagnetic assets, and a channel knowledge map (CKM) generator that aggregates ray tracing and system level outputs into spatial priors for ISAC algorithms. A case study on vision and position aided beam prediction demonstrates the utility of the platform. The code is publicly available at https://github.com/guchuanv-alt/SimART.

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arXiv2026

DBU-OFDM: A Trainable Deep Block-Unitary OFDM Waveform for Integrated Sensing and Communication

Orthogonal frequency-division multiplexing (OFDM) is a dominant waveform in modern wireless systems, yet its high peak-to-average power ratio (PAPR) and limited adaptability hinder efficient support for integrated communication and sensing. This paper proposes deep block-unitary precoded OFDM (DBU-OFDM), a structure-preserving learning framework that enables trainable waveform adaptation while preserving the DFT-based signal structure, pilot/null resource protection, and compatibility with low-complexity frequency-domain equalization. The proposed design restricts learning to a block-unitary transformation over data subcarriers and preserves pilot and null resources for structural compatibility. The transform is parameterized by recursive Householder reflections, ensuring strict unitarity as well as differentiable, numerically stable, and complexity-controllable implementation. Results show that DBU-OFDM achieves PAPR tails close to block-pilot DFT-s-OFDM while retaining comb-type pilots, improves communication reliability in frequency-selective fading via frequency-domain diversity, and enhances range and velocity estimation in direct sensing, especially in dimension-limited settings. Over-the-air USRP experiments and FPGA prototyping further verify its practical feasibility, demonstrating low error vector magnitude (EVM), clear PAPR reduction in real transmission, and hardware throughput up to 200~MS/s with microsecond-level latency. DBU-OFDM therefore offers a practical intermediate solution between conventional model-based OFDM waveforms and unconstrained neural transceivers for next-generation integrated communication and sensing systems.

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arXiv2026

Immersive Volumetric Video Playback: Near-RT Resource Allocation and O-RAN-based Implementation

Immersive volumetric video streaming in extended reality (XR) demands ultra-low motion-to-photon (MTP) latency, which conventional edge-centric architectures struggle to meet due to per-frame computationally intensive rendering tightly coupled with user motion. To address this challenge, we propose an Open Radio Access Network (O-RAN)-integrated playback framework that jointly orchestrates radio, compute, and content resources in near real time (Near-RT) control loop. The system formulates the rendered-pixel ratio as a continuous control variable and jointly optimizes it over the Open Cloud (O-Cloud) compute, gNB transmit power, and bandwidth under a Weber-Fechner quality of experience (QoE) model, explicitly balancing resolution, computation, and latency. A Soft Actor-Critic (SAC) agent with structured action decomposition and QoE-aware reward shaping resolves the resulting high-dimensional control problem. Experiments on a 5G O-RAN testbed and system simulations show that SAC reduces median MTP latency by above $11\%$ and improves both mean QoE and fairness, demonstrating the feasibility of RIC-driven joint radio-compute-content control for scalable, latency-aware immersive streaming.

cs.NI↗

arXiv2026

Extended Target Adaptive Beamforming for ISAC:A Perspective of Predictive Error Ellipse

Utilizing communication signals to extract motion parameters has emerged as a key direction in Vehicle-to- Everything (V2X) networks. Accurately modeling the relationship between communication signals and sensing performance is critical for the advancement of such systems. Unlike prior work that relies primarily on qualitative analysis, this paper derives the Cram\'er-Rao Bound (CRB) for radar parameter estimation in the context of Orthogonal Frequency Division Multiplexing (OFDM) waveforms and Uniform Planar Array (UPA) configurations. Recognizing that vehicles may act as extended targets, we propose two New Radio (NR)-V2X-compatible beamforming schemes tailored to different phases of the communication process. During the initial beam establishment phase, we develop a beamforming approach based on the union of predictive error ellipses, which enhances scatterer localization through temporally assisted beam training. In the beam adjustment phase, we introduce an adaptive narrowest-beam strategy that leverages the positions of scatterers and the communication receiver (CR), enabling effective tracking with reduced complexity. The beam design problem is addressed using the minimum enclosing ellipse algorithm and tailored antenna control methods. Simulation results validate the proposed approach, showing up to a 32.4% improvement in achievable rate with a 32*32 transmit antenna array and a 5.2% gain with an 8*8 array, compared to conventional beam sweeping under identical SNR conditions.

cs.IT↗

arXiv2025

An End-to-End Neural Network Transceiver Design for OFDM System with FPGA-Accelerated Implementation

The evolution toward sixth-generation (6G) wireless networks demands high-performance transceiver architectures capable of handling complex and dynamic environments. Conventional orthogonal frequency-division multiplexing (OFDM) receivers rely on cascaded discrete Fourier transform (DFT) and demodulation blocks, which are prone to inter-stage error propagation and suboptimal global performance. In this work, we propose two neural network (NN) models DFT-Net and Demodulation-Net (Demod-Net) to jointly replace the IDFT/DFT and demodulation modules in an OFDM transceiver. The models are trained end-to-end (E2E) to minimize bit error rate (BER) while preserving operator equivalence for hybrid deployment. A customized DFT-Demodulation Net Accelerator (DDNA) is further developed to efficiently map the proposed networks onto field-programmable gate array (FPGA) platforms. Leveraging fine-grained pipelining and block matrix operations, DDNA achieves high throughput and flexibility under stringent latency constraints. Experimental results show that the DL-based transceiver consistently outperforms the conventional OFDM system across multiple modulation schemes. With only a modest increase in hardware resource usage, it achieves approximately 1.5 dB BER gain and up to 66\% lower execution time.

eess.SY↗

arXiv2025

SemAgent: Semantic-Driven Agentic AI Empowered Trajectory Prediction in Vehicular Networks

Efficient information exchange and reliable contextual reasoning are essential for vehicle-to-everything (V2X) networks. Conventional communication schemes often incur significant transmission overhead and latency, while existing trajectory prediction models generally lack environmental perception and logical inference capabilities. This paper presents a trajectory prediction framework that integrates semantic communication with Agentic AI to enhance predictive performance in vehicular environments. In vehicle-to-infrastructure (V2I) communication, a feature-extraction agent at the Roadside Unit (RSU) derives compact representations from historical vehicle trajectories, followed by semantic reasoning performed by a semantic-analysis agent. The RSU then transmits both feature representations and semantic insights to the target vehicle via semantic communication, enabling the vehicle to predict future trajectories by combining received semantics with its own historical data. In vehicle-to-vehicle (V2V) communication, each vehicle performs local feature extraction and semantic analysis while receiving predicted trajectories from neighboring vehicles, and jointly utilizes this information for its own trajectory prediction. Extensive experiments across diverse communication conditions demonstrate that the proposed method significantly outperforms baseline schemes, achieving up to a 47.5% improvement in prediction accuracy under low signal-to-noise ratio (SNR) conditions.

cs.AI↗

arXiv2025

Cooperative Base Station Assignment and Resource Allocation for 6G ISAC Network

In the upcoming 6G networks, integrated sensing and communications (ISAC) will be able to provide a performance boost in both perception and wireless connectivity. This paper considers a multiple base station (BS) architecture to support the comprehensive services of data transmission and multi-target sensing. In this context, a cooperative BS assignment and resource allocation (CBARA) strategy is proposed in this paper, aiming at jointly optimizing the communication and sensing (C&S) performance. The posterior Cramer-Rao lower bound and the achievable rate with respect to transmit power and bandwidth are derived and utilized as optimization criteria for the CBARA scheme. We develop a heuristic alternating optimization algorithm to obtain an effective sub-optimal solution for the non-convex optimization problem caused by multiple coupled variables. Numerical results show the effectiveness of the proposed solution, which achieves a performance improvement of 117% in communication rate and 40% in sensing accuracy, compared to the classic scheme.

cs.IT↗

arXiv2025

Low-Complexity Null-Space-Based Simultaneous Wireless Information and Power Transfer Scheme

Simultaneous wireless information and power transfer (SWIPT) has attracted sustained interest. We propose a null-space-based transmission scheme for multiuser SWIPT serving both energy users (EUs) and information users (IUs). Under a practical nonlinear energy-harvesting (EH) model and multiple waveform options, we revisit the role of dedicated energy beams (EBs). We show that, in general, dedicated EBs are unnecessary because information beams (IBs) with Gaussian signaling can simultaneously support wireless energy transfer (WET) and wireless information transfer (WIT), unless special energy-centric waveforms (e.g., deterministic sinusoidal waveforms) are employed and provide sufficient gains. Guided by these insights, we formulate an optimization problem for EB design to enable dedicated waveform transmission for WET, and we develop a low-complexity algorithm that reduces computation by ignoring the WET contribution of IBs during optimization. Numerical results corroborate that deterministic sinusoidal waveforms outperform Gaussian signaling when the received RF power lies in the EH high-efficiency region, making dedicated EBs beneficial. The proposed scheme achieves computational complexity reductions of 91.43\% and 98.54\% for the cases $M=8,,K^I=K^E=2$ and $M=16,,K^I=K^E=4$, respectively, with negligible performance loss, thereby validating the efficiency of the low-complexity algorithm.

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arXiv2025

Prototype: A Keyword Spotting-Based Intelligent Audio SoC for IoT

In this demo, we present a compact intelligent audio system-on-chip (SoC) integrated with a keyword spotting accelerator, enabling ultra-low latency, low-power, and low-cost voice interaction in Internet of Things (IoT) devices. Through algorithm-hardware co-design, the system's energy efficiency is maximized. We demonstrate the system's capabilities through a live FPGA-based prototype, showcasing stable performance and real-time voice interaction for edge intelligence applications.

cs.SD↗

arXiv2025

Beamforming-based Achievable Rate Maximization in ISAC System for Multi-UAV Networking

Airborne mobile Integrated Sensing and Communication (ISAC) base stations have garnered significant attention recently, with ISAC technology being a crucial application for 6G networks. Since ISAC can sense potential mobile communication users, this paper studies an effective scheme for a multi-UAV network tailored for emergency communication. In this paper, we develop a temporal-assisted frame structure utilizing integrated omnidirectional and directional beampattern to facilitate efficient and frequent searching, with extended Kalman filtering (EKF) as an aid to beam alignment. Further, we address an optimization problem to maximize the total achievable rate per slot by jointly designing UAV beamforming, load management, and UAV direction planning, all while adhering to the constraints of the predicted beam coverage. Given the problem NP-hard, we introduce three robust mechanisms for its resolution: an enhanced distributed Successive Convex Approximation (SCA)-Iterative Rank Minimization (IRM) algorithm, an coalition game approach, and a Fermat point search method. In particular, the proposed SCA-IRM algorithm decomposes the original complex optimization problem into several sub-problems and assigns them equally to each UAV, so as to realize distributed computing and improve computational efficiency. Our proposed simulations demonstrate the improved system performance in terms of communication rate, fairness, and sensing accuracy, providing design guidelines of UAV-assisted emergency communication networking.

cs.PF↗

arXiv2025

Time-Varying Offset Estimation for Clock-Asynchronous Bistatic ISAC Systems

The bistatic Integrated Sensing and Communication (ISAC) is poised to become a key application for next generation communication networks (e.g., B5G/6G), providing simultaneous sensing and communication services with minimal changes to existing network infrastructure and hardware. However, a significant challenge in bistatic cooperative sensing is clock asynchronism, arising from the use of different clocks at far separated transmitters and receivers. This asynchrony leads to Timing Offsets (TOs) and Carrier Frequency Offsets (CFOs), potentially causing sensing ambiguity. Traditional synchronization methods typically rely on static reference links or GNSS-based timing sources, both of which are often unreliable or unavailable in UAVbased bistatic ISAC scenarios. To overcome these limitations, we propose a Time-Varying Offset Estimation (TVOE) framework tailored for clock-asynchronous bistatic ISAC systems, which leverages the geometrically predictable characteristics of the Line-of-Sight (LoS) path to enable robust, infrastructure-free synchronization. The framework treats the LoS delay and the Doppler shift as dynamic observations and models their evolution as a hidden stochastic process. A state-space formulation is developed to jointly estimate TO and CFO via an Extended Kalman Filter (EKF), enabling real-time tracking of clock offsets across successive frames. Furthermore, the estimated offsets are subsequently applied to correct the timing misalignment of all Non-Line-of-Sight (NLoS) components, thereby enhancing the high-resolution target sensing performance. Extensive simulation results demonstrate that the proposed TVOE method improves the estimation accuracy by 60%.

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