arXiv subjects

Yong Zeng

Publications and source records attributed to Yong Zeng.

At least 19 recordsLinked to original sources

arXiv2026

A Material-Aware Channel Model for Efficient CKM Generation via Environment Reconstruction

Channel knowledge map (CKM) is a promising technology for environment-aware wireless communication, sensing, and localization in 6G networks. Accurate CKM generation requires precise reconstruction of the environment, including 3D geometries and scatterer materials, typically from multi-modal sensory observations such as LiDAR point clouds and sparse channel measurements. While the former is relatively easy to acquire, materials remain difficult to obtain directly from sparse channel measurements due to the lack of an explicit channel model linking them. To fill this gap, this paper proposes a material-aware channel model that explicitly characterizes the influence of scatterer materials on the wireless channel. Based on this model, an iterative gradient descent based material reconstruction algorithm is proposed. Full wave simulation results validate the developed model and the proposed algorithm, demonstrating their potentials for efficient CKM generation via environment reconstruction.

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arXiv2026

Universal CKM for Environment-Aware Wireless Networks: Enabling Cross-Device and Cross-Task Channel Knowledge Transfer

Channel knowledge map (CKM) is a promising technology for environment-aware sixth-generation (6G) wireless networks. However, most existing CKMs are tightly coupled with wireless devices and downstream tasks, which limit their scalability and reusability in wireless networks. To address these limitations, this article proposes the concept of universal CKM (uCKM) as a foundational wireless environment prior, which aims to enable cross-device and cross-task channel knowledge transfer for environment-aware wireless networks. We first revisit the representative CKMs and discuss their limitations. Then, the uCKM-enabled new paradigm for environment-aware wireless networks is introduced, and its benefits are highlighted from the perspectives of uCKM construction and utilization phases, for which we propose the visions of ``All for uCKM'' and ``uCKM for All'', i.e., the data acquired by all devices and tasks should contribute to the construction of uCKM, and vice versa. Subsequently, we discuss the main challenges of uCKM and propose potential solutions. Last, we provide simulation results to demonstrate the feasibility and performance gains brought by uCKM and outline future research directions.

cs.IT↗

arXiv2026

SafeStats: Efficient 2PC Protocols for Data Statistic-Related Functions

Statistical analysis on sensitive datasets like medical records and financial transactions is essential for decision-making, but raises significant privacy concerns. While existing secure Two-Party Computation (2PC) makes extensive efforts in designing the common secure primitives (e.g., addition and multiplication) or machine learning-related functions, few pay attention to the statistical functions. In this paper, we propose SafeStats, a secure toolkit tailored for 2PC secure statistical analysis. Specifically, to develop SafeStats, we first refer to Microsoft Excel's statistical library and summarize that most statistical operations can be achieved with three core functions:1) frequency counting, 2) sorting, and 3) non-linear math functions. Then, for each core statistical function, SafeStats presents an efficient 2PC implementation. For secure frequency counting, SafeStats adopts a secure shift-based strategy to avoid invoking expensive 2PC equality test protocols. For secure sort, SafeStats involves a secure segment-indicator protocol to achieve secure counting-based sort, which enables fast element sorting over specific statistical scenarios without the need for secure comparison. For non-linear math functions, we enhance the current reduce-then-approximate paradigm by introducing a bisection-based range reduction protocol. Finally, we implement SafeStats and test it on 14 common statistical analysis cases. As an example, for the chi-square test, SafeStats achieves a 1.5 $\times$ runtime speedup and a 4.2 $\times$ reduction in communication compared to directly using the current general-purpose 2PC library to realize it.

cs.CR↗

arXiv2026

Where to Perform Channel Measurements for CKM Construction: A Random Field Theory Analysis

Channel knowledge map (CKM) is regarded as a promising technology for future sixth-generation (6G) networks, facilitating environmental-aware wireless communication, sensing, and localization. Research works on CKM construction can be classified as model-based methods and data-based approaches. Specifically, data-based CKM construction exploits the fundamental principle of spatial correlation to complete CKM based on limited measurement data, leading to the question of "where to perform channel measurements". In this paper, we study the spatial measurement strategy for efficient data-based CKM construction, and consider a specific type of CKM named channel gain map (CGM). The general objective is to select a subset of locations for channel measurements, so as to minimize the average mean-squared-error (AMSE) of the global CGM construction. In order to reduce the infinite measurement locations to a finite set, we discretize the underlying physical space into a finite number of cubic grid points, and formulate a combinatorial optimization problem to select measurement locations from them. In order to solve the proposed problem, we employ two representative algorithms, namely the greedy algorithm and the simulated annealing (SA), and discuss their respective advantages. To overcome the accuracy-complexity trade-off of traditional uniform discretization, we develop an adaptive discretization strategy from the viewpoint of Gaussian random field theory to minimize the information loss from the original continuous field to its approximated discrete representation in the mean-squared sense. Compared to uniform discretization, the proposed adaptive discretization strategy achieves a significant performance gain in terms of AMSE-reduction, establishing the theoretical framework of spatial measurement and providing practical guidance for implementation.

cs.IT↗

arXiv2026

Low-Altitude UAV-Assisted Bistatic ISAC: Closed-form 3D CRLB and Coverage Analysis

This paper investigates the fundamental performance limits of three-dimensional (3D) localization in unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) systems. Specifically, a base station (BS) estimates the 3D position of a sensing target with the aid of a UAV acting as a flexible aerial anchor node. We derive a closed-form expression for the 3D Cramer-Rao lower bound (CRLB), which explicitly quantifies the achievable localization accuracy as a function of both the UAV's location and the target's position. The CRLB is shown to decompose naturally into three distinct components, arising from signal propagation delay, angular measurements, and their coupling effect, respectively. To validate the analytical results, we consider a representative orthogonal frequency-division multiplexing (OFDM)-based ISAC system and demonstrate that the derived CRLB closely predicts the performance of maximum-likelihood estimation across diverse geometric configurations and UAV mobility patterns. Furthermore, we introduce the notion of CRLB-constrained sensing coverage to characterize the spatial region within which a prescribed localization accuracy can be guaranteed. Through local boundary approximations and coverage-size evaluations, we reveal how UAV displacement, altitude, and the CRLB threshold jointly shape the extent and geometry of the reliable sensing region.

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arXiv2026

Active Learning for Channel Knowledge Map Construction via Bayesian Inference Diffusion Models

Channel knowledge maps (CKMs) are regarded as key enablers of environment-aware communications in future wireless networks, as they provide location-specific channel information by establishing an explicit connection between wireless devices and the physical propagation environment. As a representative CKM, the channel gain map (CGM) characterizes the spatial distributions of large-scale fading to support wireless environment awareness and network optimization. Existing CGM construction methods generally lack a well-defined sampling-point acquisition strategy, which may result in a limited number of sampling points being allocated to spatially redundant or highly predictable regions, thereby degrading CGM reconstruction performance in complex propagation environments. In this paper, we propose an active-learning-based diffusion framework for efficient CGM construction. By combining Bayesian inference with the diffusion model, the proposed method estimates epistemic uncertainty without retraining the model. Two uncertainty quantification algorithms are further developed along the reverse diffusion process to generate element-wise epistemic uncertainty maps. Furthermore, an uncertainty-aware sampling strategy is designed to determine new observation locations by jointly considering epistemic uncertainty and spatial distribution uniformity. Experimental results on both static and dynamic CGM datasets demonstrate that the proposed method achieves better reconstruction performance than baseline methods. These results indicate that the proposed method can effectively improve the utilization efficiency of limited sampling points and enhance the accuracy of CGM construction in complex wireless propagation environments.

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arXiv2026

Ray Antenna Array Enhanced Low-Altitude ISAC: Performance Analysis and Beamforming Design

The low-altitude economy (LAE) heavily relies on aerial vehicles, yet these platforms remain vulnerable to environmental and security risks, necessitating robust airspace monitoring. Integrated sensing and communication (ISAC) as one of the key technologies of 6G provides potential solutions for safe LAE. However, conventional antenna arrays face limitations in cost, scalability, and coverage, especially directly above the base station, due to hardware complexity and degraded angular resolution. By exploiting the recently proposed ray antenna array (RAA), this paper considers a RAA-enhanced low-altitude ISAC system. RAA architecture employs multiple ray-arranged arrays directly connected without phase shifters, significantly reducing hardware costs while supporting flexible beamforming via dynamic ray selection. Moreover, RAA can provide uniform angular resolution and eliminates coverage holes, making it particularly suitable for low-altitude ISAC. In this paper, we formulate an optimization problem for joint ray selection and beamforming to enhance sensing coverage under communication constraints. An efficient alternating optimization algorithm is proposed to solve this problem. Analytical and simulation results demonstrate that RAA achieves higher sensing signal-to-noise ratio compared to traditional arrays, offering a cost-effective and high-performance solution for achieving low-altitude ISAC.

cs.IT↗

arXiv2026

Under What Conditions Can a Machine Be Called Genuinely Creative?

Recent AI systems can generate texts, software architectures, hypotheses, designs, and scientific workflows that appear creative. This paper asks under what conditions a machine can be called genuinely creative, and how human agency can be preserved within shared cognitive and creative environments. It develops a requirement framework derived from Designics, the science of meaning-bearing intentional change. The paper argues that genuine machine creativity should not be defined by output novelty, current performance, or transient architecture alone. Instead, creativity is understood as the structural transformation of incomplete situations through recursive intervention dynamics. On this view, it depends on ten requirements: environment representation, scoped perception, conflict identification, intervention capability, consequence observation, knowledge and environment update, rescoping, local-to-global unfolding, value-based scoping, and human-AI co-living. These are organized through the three laws of Designics: perception, conflict, and capability. The paper illustrates the computational tractability of these requirements through selected cyber-physical and cyber-biological studies, including recursive element extraction, autonomous mesh generation, and neurophysiological and workload analysis. It then treats open-ended systems, automated discovery frameworks, self-modifying agents, foundation models, and agentic workflows as pressure cases: they demonstrate powerful generative means but do not by themselves establish genuine machine creativity. Finally, the paper argues that proactive AI ethics is internal to genuine machine creativity rather than an after-the-fact filter. Value-based scoping and human-AI co-living must shape how creative machines perceive environments, identify conflicts, select interventions, observe consequences, update knowledge, and rescope future action.

cs.AI↗

arXiv2026

Capacity Characterization and Formation Optimization for Multi-User MIMO Communications with UAV Swarm

For a multi-user multiple-input multiple-output (MU-MIMO) wireless communication system, imagining that the locations of the users are now fully controllable, what is the maximum sum-capacity, and what are the corresponding optimal user locations? While these questions are irrelevant in conventional human-centric communications with random user mobility, they become critically important for emerging applications involving ground or aerial robots. This paper addresses these fundamental questions in the context of MU-MIMO communications with an unmanned aerial vehicle (UAV) swarm acting as the users. To this end, we first derive closed-form expressions for the sum-capacity of MU-MIMO UAV swarm communications. Our results reveal that, compared to conventional MU-MIMO systems, the additional degrees of freedom provided by the coordinated mobility of the UAV swarm yields substantial capacity enhancement. Specifically, when the base station (BS) is equipped with an $M$-element uniform linear array (ULA), the full spatial multiplexing gain and beamforming gain, both equal to $M$, can be achieved simultaneously. For a BS with a uniform planar array (UPA), we show that asymptotically $\frac{\pi M}{4}$ users can simultaneously enjoy the full beamforming gain $M$. Furthermore, we propose a novel framework to optimize UAV swarm formation for maximizing the sum-capacity achieved by successive interference cancellation (SIC) and maximizing the sum-rate via treating interference as noise (TIN), taking into account practical considerations such as collision avoidance and swarm cohesion constraints. By exploiting the manifold structure of the array response vectors with respect to UAV directions, we develop an efficient algorithm to solve the resulting non-convex formation optimization problems. Extensive simulation results demonstrate that the proposed algorithms achieve near-optimal performance.

cs.IT↗

arXiv2026

Towards Intelligent Low-Altitude Wireless Network Deployment: Differentiable Channel Knowledge Map Construction and Trajectory Design

Channel knowledge map (CKM) has emerged as a promising technique to leverage prior propagation knowledge in low-altitude wireless networks (LAWNs), yet state-of-the-art grid-based CKM construction methods struggle to support efficient LAWN deployment due to their lack of differentiability with respect to continuous locations of unmanned aerial vehicles (UAVs). To overcome this limitation, we propose a differentiable CKM-triggered trajectory optimization framework for LAWNs. Firstly, we propose a location-oriented CKM construction method that directly maps continuous spatial coordinates to channel gain. In particular, a shared convolutional neural network (CNN) is employed to encode high-level environmental features from conditional inputs. These features are then sampled based on location information to form a fused regressor-conditional multilayer perceptron (c-MLP) or conditional Kolmogorov-Arnold network (cKAN)-for channel gain prediction. We further propose a joint power, bandwidth, and trajectory optimization (JPBTO) method for multi-UAV systems, with the constructed differentiable CKM employed to evaluate the communication performance. The formulated non-convex problem is solved via alternating optimization and successive convex approximation. Numerical results show that the proposed framework enables location-aware differentiability of the CKM, while achieving higher accuracy than the methods without environmental features. Furthermore, the proposed CKM-JPBTO achieves a significantly higher minimum throughput than the conventional statistical channel model-based JPBTO.

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arXiv2026

CRLB and Parameter Estimation for OFDM-ISAC with Non-Uniform Sparse Resource Allocation

Integrated sensing and communication (ISAC) holds great promise in expanding the applications of wireless communication networks. However, in current communication-centric systems, the time-frequency resources available for sensing may be limited, and also usually non-uniformly and sparsely distributed across the time-frequency domain. Such a non-uniformity destroys the "thumbtack-shaped" ambiguity function of the orthogonal frequency division multiplexing (OFDM) waveform, leading to degraded sensing performance. To this end, this paper explores the parameter estimation algorithm for OFDM-ISAC systems with non-uniform sparse resource allocation. Specifically, for the single target case, we derive the closed-form Cramer-Rao lower bound (CRLB) for parameter estimation as a function of resource indices. Furthermore, we show that simply filling unused resource locations with zeros and applying the classic periodogram estimation is equivalent to maximum likelihood (ML) estimation, which is asymptotically optimal. For the multi-target case, we generate a virtual resource using the autocorrelation function of the original signal, which exhibits a significantly larger virtual bandwidth compared to the original signal, at the cost of higher peak-to-sidelobe ratio (PSLR). Simulation results demonstrate that the proposed approach outperforms the conventional periodogram method for non-uniform sparse resource allocation.

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arXiv2026

Channel Knowledge Map-Enabled NLoS ISAC Localization

Accurate localization in non-line-of-sight (NLoS) environments remains challenging even with both angle-of-arrival (AoA) and time-of-arrival (ToA) measurements. In complex urban scenarios, the absence of line-of-sight (LoS) paths and the lack of environment prior knowledge make geometric based localization methods inapplicable, while prior-based approach such as fingerprinting is sensitive to environmental perturbations. This paper proposes a novel environment-aware localization framework enabled by the emerging concept called channel knowledge map (CKM). In the offline stage, AoA-ToA path signatures are learned by the CKM, with each path mapped to one candidate scatterer, thereby forming geometric priors within the environment. In the online stage, observed paths are matched to the CKM to extract high-confidence scatterers. Nonlinear least squares (NLS) method is then applied to jointly estimate the user and dominant scatterer locations. Even with imperfect CSI matching, geometric feasibility consistent with CKM scatterer priors provides corrective information and suppresses ambiguity. Simulations demonstrate that the proposed scheme outperforms fingerprinting and offers a robust and scalable solution to address the challenging NLoS localization for integrated sensing and communication (ISAC) systems.

eess.SP↗

arXiv2026

Rotatable Antenna Enhanced Multicast Communication System

Rotatable antenna (RA) provides additional spatial degrees of freedom (DoFs) for communication systems by enabling per-antenna dynamic boresight adjustment, which is attractive for fairness-oriented multicast transmission. This letter investigates an RA-enhanced downlink multi-group multicast system. Specifically, we aim to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all users by jointly optimizing the multicast beamforming vectors and the RA boresight directions under transmit power and rotation constraints. To solve this non-convex problem, we first reformulate the max-min SINR objective via quadratic transform. Then, we develop an alternating optimization (AO) algorithm that iteratively updates the multicast beamforming and RA boresight directions. The beamforming vectors are obtained from a convex subproblem, while the boresight directions are refined using a successive convex approximation (SCA) procedure. Simulation results verify that the proposed RA-based scheme substantially enhances the fairness performance compared with fixed antenna-based and random-orientation benchmarks.

cs.IT↗

arXiv2026

3D Spherical Directly-Connected Antenna Array for Low-Altitude UAV Swarm ISAC

Recently a novel multi-antenna architecture termed ray antenna array (RAA) was proposed, where several simple uniform linear arrays (sULAs) are arranged in a ray-like structure to enhance communication and sensing performance. By eliminating the need for phase shifters, it also significantly reduces hardware costs. However, RAA is prone to signal blockage and has no elevation angle resolution capability in three-dimensional (3D) scenarios. To address such issues, in this paper we propose a novel spherical directly-connected antenna array (DCAA), which composes of multiple simple uniform planar arrays (sUPAs) placed over a spherical surface. All elements within each sUPA are directly connected. Compared to conventional arrays with hybrid analog/digital beamforming (HBF), DCAA significantly reduces hardware cost, improves energy focusing, and provides superior and uniform angular res olution for 3D space. These advantages make DCAA particularly suitable for integrated sensing and communication (ISAC) in low-altitude unmanned aerial vehicles (UAV) swarm scenarios, where targets may frequently move away from the boresight of traditional arrays, degrading both communication and sensing performance. Simulation results demonstrate that the proposed spherical DCAA achieves significantly better angular resolution and higher spectral efficiency than conventional array with HBF, highlighting its strong potential for UAV swarm ISAC systems.

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arXiv2026

Clutter-Resilient ISAC for Low-Altitude Wireless Networks: A 5G Base Station-Compatible Protocol, Waveform, and Prototype

Integrated sensing and communications (ISAC) has been envisioned as a promising solution to support emerging services in low-altitude wireless networks (LAWNs), where upgrading 5G ground base stations (GBS) toward new active sensing systems with wide coverage, low cost, high accuracy, and favorable spectrum compatibility, is strongly desired. However, such an evolution faces several critical challenges, particularly in the detection and tracking of weak and slow unmanned aerial vehicles (UAVs). These challenges include ISAC waveform design, clutter cancellation resilient to high clutter-to-noise ratios (CNRs), and efficient Doppler separation between UAVs and clutter. To that end, we summarize potential solutions and raise a comprehensive framework on implementing the 5Gadvanced (5G-A) GBS. Outfield experiments demonstrate that the developed 5G-A GBS can effectively track weak and slow targets at distances exceeding 1 kilometer, while incurring only a 1.2% downlink rate loss relative to commercial 5G-A GBS.

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arXiv2026

A Unified Multicarrier Waveform Framework for Next-generation Wireless Networks: Principles, Performance, and Challenges

Next-generation wireless networks require enhanced flexibility, efficiency, and reliability in physical layer waveform design to address the challenges posed by heterogeneous channel conditions and stringent quality-of-service demands. To this end, this paper proposes a unified multicarrier waveform framework that provides a systematic characterization and practical implementation guidelines to facilitate waveform selection for the sixth-generation (6G) mobile networks and beyond. We commence by examining the design principles of the state-of-the-art waveforms, which are categorized into one-dimensional modulation waveforms (e.g., orthogonal frequency division multiplexing (OFDM) and affine frequency division multiplexing (AFDM)) and two-dimensional modulation waveforms (e.g., orthogonal time frequency space (OTFS)). Their inherent resilience against various channel-induced interference is further studied, revealing their distinct suitability in diverse channel conditions. Furthermore, an in-depth performance analysis is presented by comparing their key performance indicators (KPIs), followed by an extensive exploration of these advanced waveforms in various applications. Consequently, this work aims to serve as a pivotal reference for waveform adoption in future 6G standardization and network deployment.

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arXiv2026

UAV Swarm Enabled Aerial Movable Antenna System for Low-Altitude Economy: From Far-Field to Near-Field Communication

Unmanned aerial vehicle (UAV) with the intrinsic three-dimensional (3D) mobility provides an ideal platform for implementing aerial movable antenna (AMA) system enabled by UAV swarm cooperation. Besides, AMA system is readily to achieve an extremely large-scale array aperture, rendering the conventional far-field uniform plane wave (UPW) model no longer valid for aerial-to-ground links. This paper studies the UAV swarm enabled near-field AMA communication, by taking into account the non-uniform spherical wave (NUSW) model, where UAV swarm trajectory simultaneously influences the channel amplitude and phase. We formulate a general optimization problem to maximize the minimum average communication rate over user equipments (UEs), by jointly optimizing the 3D UAV swarm trajectory and receive beamforming for all UEs. To draw useful insights, the special case of single UE is first studied, and successive convex approximation (SCA) technique is proposed to efficiently optimize the UAV swarm trajectory. For the special case of placement optimization, the optimal placement positions of UAVs for cases of single UAV and two UAVs are derived in closed-form. Then, for the special case of two UEs, we show that an inter-UE interference (IUI)-free communication can be achieved by symmetrically placing an even number of UAVs along a hyperbola, with its foci corresponding to the locations of the two UEs. Furthermore, for arbitrary number of UEs, an alternating optimization algorithm is proposed to efficiently tackle the non-convex optimization problem. Numerical results validate the significant performance gains over the benchmark schemes.

cs.IT↗

arXiv2026

A Scalable Cloud-Edge Collaborative CKM Construction Framework Enabled by a Foundation Prior Model

Channel knowledge maps (CKMs) provide a site-specific, location-indexed knowledge base that supports environment-aware communications and sensing in 6G networks. In practical deployments, CKM observations are often noisy and irregular due to coverage-induced sparsity and hardware-induced linear/nonlinear degradations. Conventional end-to-end algorithms couple CKM prior information with task- and device-specific observations, and require labeled data and separate training for each construction configuration, which is expensive and therefore incompatible with scalable edge deployments. Motivated by the trends toward cloud-edge collaboration and the Artificial Intelligence - Radio Access Network (AI-RAN) paradigm, we develop a cloud-edge collaborative framework for scalable CKM construction, which enables knowledge sharing across tasks, devices, and regions by explicitly decoupling a generalizable CKM prior from the information provided by local observations. A foundation model is trained once in the cloud using unlabeled data to learn a generalizable CKM prior. During inference, edge nodes combine the shared prior with local observations. Experiments on the CKMImageNet dataset show that the proposed method achieves competitive construction accuracy while substantially reducing training cost and data requirements, mitigating negative transfer, and offering clear advantages in generalization and deployment scalability.

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