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Wenchi Cheng

Publications and source records attributed to Wenchi Cheng.

At least 19 recordsLinked to original sources
arXiv2026

E2-Conditioned Finite-Horizon Effective Capacity for Public-Safety MCX over Shared O-RAN

Supporting public-safety Mission Critical Services (MCX) over a shared Open radio access network (O-RAN) requires service assurance over finite incident horizons, while ordinary mobile traffic competes for the same resources and heterogeneous E2 domains expose different observations, control actions, and actuation latencies. Existing RAN key performance indicators are retrospective, whereas conventional effective capacity characterizes an asymptotic stationary regime and therefore suppresses both the initial E2-observed condition and the finite selection-to-actuation transient. To solve this problem, in this paper we develop an E2-conditioned finite-horizon effective capacity (FH-EC) framework for public-safety MCX over shared O-RAN. Specifically, we first establish a finite-horizon O-RAN MCX-based service model that incorporates E2-observed network states, control actuation latency, and correlation-aware connectivity diversity. Based on this model, we derive an FH-EC formulation that characterizes the executable service capability within a finite mission horizon. Furthermore, we transform FH-EC into confidence-calibrated capability profiles and develop an MCX orchestration framework with contract certification, shared-resource protection, and FH-EC-driven profile selection at the Near-RT RAN Intelligent Controller (RIC). Coupled MATLAB/ns-3 evaluations demonstrate the predicted short-horizon state and actuation effects and show that adaptive connectivity selection improves MCX supportability under O-DU degradation while satisfying the configured multi-QoS and non-MCX protection requirements.

cs.NI↗
arXiv2026

Unified Analytical Framework for Emergency RIS-UAV Networks Under Practical Impairments

Heterogeneous unmanned aerial vehicle (UAV) networks embedded with reconfigurable intelligent surfaces (RISs) present a promising paradigm for emergency wireless communications (EWC), offering enhanced coverage and resilience in harsh environments. However, extreme conditions in disaster areas necessitate robust performance evaluation under practical impairments, including outdated/imperfect channel state information (CSI) and discrete RIS phase shifts. Existing works lack a unified analytical framework for modeling CSI errors, employing inconsistent approaches that treat errors either as channel gain or as equivalent interference, leading to ambiguous benchmarks. To address this, we propose the $\zeta$-Model, a unified receiver-equivalent signal-to-noise (SNR) framework that continuously parameterizes residual-error exploitability via $\zeta$. This framework unifies the information-theoretic model (ITM) and the engineering baseline model (EBM) as the optimistic and pessimistic benchmark receiver treatments, while incorporating the simplified engineering model (SEM) as a tractable approximation. By employing the Fisher-Snedecor $\mathcal{F}$ distribution to capture severe fading and shadowing, we derive moment-matching-based closed-form or finite-sum approximate expressions and asymptotic expressions for average capacity (AC), effective capacity (EC), and outage probability (OP) under the proposed unified framework and its boundary cases. Validated by Monte Carlo simulations, our framework quantifies performance limits and provides crucial insights for designing robust and efficient EWC systems under various channel conditions and system impairments.

eess.SP↗
arXiv2026

ML-Based Hierarchical Prediction for Practical Energy Scheduling in Dynamic NTN-WPT Systems

With advancements in long-distance wireless power transfer (WPT) and space-based energy technologies, integrating WPT into non-terrestrial networks (NTNs), referred to as NTN-WPT, is emerging as a promising approach for next-generation wireless networks. This paper proposes an energy-scheduling approach that jointly optimizes energy efficiency, task completion rate, and task waiting time for power transfer from low Earth orbit satellites to terrestrial mobile user devices (UDs). To address scheduling challenges caused by satellite and UD mobility and channel uncertainty from stochastic propagation effects, we decompose the problem into three subproblems within a three-layer predictive framework: 1) a state prediction layer forecasts UD and satellite states; 2) an interaction mapping layer uses a graph neural network (GNN) to model energy transfer efficiency; and 3) a decision-making layer determines the energy allocation plan. Distinct machine learning (ML) methods are tailored to each layer. To balance the competing objectives, we adopt a multi-objective reinforcement learning (MORL) technique that scalarizes them into a weighted-sum reward, transforming the multi-objective problem into a tractable single-objective problem. We further introduce a multi-agent deep learning model integrating self-attention with multi-agent proximal policy optimization (MAPPO) to improve objective balancing. Simulation results show that the proposed approach achieves a better overall trade-off than baseline methods, maintaining competitive task completion rates and energy efficiency while reducing task waiting times, and remains robust under highly variable conditions.

eess.SY↗
arXiv2026

An Ultra-Low Profile Metasurface for Enhanced Non-Diffractive OAM Beam Generation Based on Sparse Feed Array

This paper proposes a novel ultra-low profile transmitted metasurface to generate enhanced non-diffractive orbital angular momentum (OAM) beams, employing a sparse feed array (SFA) to create a quasi-plane wave excitation for the first time. Our simulation indicates that with uniform amplitude excitation, the non-diffraction performance of Bessel beam produced by metasurface, surpasses that of conventional single-feed excitation. Based on this principle, a 5 * 5 sparse feed array is introduced and positioned less than one wavelength from the metasurface, ensuring a quasi-uniform amplitude excitation across all units. Further, this metasurface leverages its flexible phase control capability and integrates the spatial phase, OAM phase, and axicon phase to generate an enhanced high-order Bessel beam. The simulated results confirm a successful non-diffractive Bessel beam generation carrying OAM with mode l = +2, exhibiting reduced beam divergence and higher gain. This design also offers benefits of ultra-low profile, high aperture efficiency, low structural complexity.

eess.SP↗
arXiv2026

Flexible Wideband Filtering Monopole Antenna With Stable High Omnidirectionality

This article presents a wideband flexible filtering monopole antenna with symmetric structure for stable high omnidirectionality. It is based on a monopole antenna, which is printed on a single-layer flexible substrate. Two folded parasitic strips with different length are devised on both sides of the driven monopole, giving filtering responses in the higher and lower band without filtering circuits. Since the asymmetric filtering structure adversely affects in-band omnidirectionality, this baseline design is extended with symmetric filtering structure to improve omnidirectionality and bandwidth. In the proposed design, a pair of parasitic strips are devised on the both sides of monopole antenna symmetrically, achieving a radiation null in the higher band. Then, by loading a pair of folded parasitic strips on the both sides of feed line with slotted metal ground, a radiation null is realized in the lower band. Besides, the driven monopole is slotted symmetrically for wideband operation. By adopting a fully symmetric filtering structure, the proposed design effectively suppresses the impact of the parasitic elements on the in-band omnidirectional radiation pattern, thereby achieving high omnidirectionality. Furthermore, the proposed antenna exhibits stable performance under different bending radii. To verify our design concept, an antenna prototype is fabricated. Both the flat and bent antennas are measured. The results show that the proposed antenna has a -10 dB impedance bandwidth of 45.6%, an in-band gain about 2 dBi, and an out-of-band radiation suppression more than 11 dB. The measured omnidirectionality has variations less than 0.8 dB without bending and 1 dB with a bending radius of 30 mm. This design offers several advantages including stable high omnidirectionality across a wide bandwidth, flexible conformal capability, and filtering property.

eess.SP↗
arXiv2026

MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks

In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks. For time-critical rescue missions, information freshness is crucial because decisions based on outdated data may lead to ineffective control actions. This paper investigates age of information (AoI) minimization for UAV-assisted emergency communications with heterogeneous emergency services. We model bursty packet arrivals using a Markov-modulated Poisson process and adopt finite blocklength theory to capture the coupling among transmission duration, packet completion, and AoI evolution. To balance delay-tolerant long-packet transmission and urgent short-packet response, we propose a mini-slot-embedded scheduling mechanism with adaptive checkpoint-interval selection. We formulate the joint optimization of UAV trajectory control, user scheduling, and checkpoint-interval selection as a multi-agent decision problem, and develop MA-HEAD-Net, an adaptive rule-guided multi-agent deep reinforcement learning framework. MA-HEAD-Net incorporates communication-domain rule priors into a gated multi-head policy, where adaptive gates regulate the contributions of rule-prior and learned-policy logits for different subtasks. The policy and gating components are jointly optimized under multi-agent proximal policy optimization. Simulation results show that MA-HEAD-Net improves policy-formation efficiency compared with representative multi-agent deep reinforcement learning baselines and achieves lower AoI than both learning-based and heuristic methods in dynamic UAV-assisted emergency communication scenarios.

cs.AI↗
arXiv2026

Flexible Wearable Filtering Antenna With Stable Performance for IoT Devices

In this article, a flexible and lightweight filtering wearable antenna without extra circuits is presented. The proposed antenna starts from a flexible directional antenna with lightweight structure, which includes a layer of ultra-thin flexible substrate, a metal ground layer, and a flexible foam layer sandwiched between them. Then, by introducing two pairs of vertical slots to the radiation patch printed on the flexible substrate, two radiation nulls are realized at both band edges without extra circuits. Moreover, to mitigate the deterioration of in-band radiation under different curvature, a pair of inverted Fshaped slots are loaded on the radiation patch. The coupling of Fshaped slots suppresses non-radiated lateral current components along the curvature direction, maintaining stable performance after bending. In addition, deformation analysis of the proposed antenna with a three-layer human tissue model under different bending radii is carefully carried out, showing stable bandwidth, effective out-of-band radiation suppression, and low specific absorption rate (SAR) value. To verify this method, a prototype is fabricated. Measurements are conducted both in free space and conformal on the curved body tissue. The results show that the proposed antenna achieves a bandwidth from 2.7 GHz to 3 GHz, an out-of-band radiation suppression more than 11 dB with maxmium suppression of 23 dB, and an average gain of 8.5 dBi. As a flexible wearable antenna with stable performance and integrated reliable filtering features, it has several advantages including flexible wearable structure, stable filtering properties, lightweight characteristic, and low SAR. This makes it an excellent candidate for wearable IoT applications.

eess.SP↗
arXiv2026

Secrecy Energy Efficiency for IRS-Assisted Low-Altitude Communications: A D3QN-PER Based Approach

To address the security and energy efficiency challenges in low-altitude economy (LAE) wireless communications, we develop a secure synergistic network integrating unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS), with an emphasis on maximizing secrecy energy efficiency (SEE) for downlink transmission scenarios. In particular, firstly, we establish the channel transmission models for UAV-IRS assisted LAE communications network. Then, we formulate a non-convex fractional optimization problem for SEE maximization, involving three tightly coupled variables, i.e., the beamforming, IRS phase and UAV trajectory. To tackle the fractional structure and variable coupling, Dinkelbach's method and equivalent transformations are leveraged to reformulate the objective function, which is then decoupled and decomposed into three independent subproblems via an alternating optimization strategy for iterative resolution. Slack variables and Semidefinite Relaxation (SDR) are further employed to convexify the subproblems of beamforming and IRS phase shift optimization, thereby obtaining their optimal solutions. For the UAV trajectory optimization subproblem, we propose a D3QN-PER algorithm, which integrates a Dueling Double Deep Q-Network with Prioritized Experience Replay, to tackle the slow convergence and training instability inherent in conventional Deep Q-Network (DQN). Numerical simulations validate the performance for our proposed joint optimization scheme. Comparative results demonstrate that the developed D3QN-PER-based algorithm outperforms existing state-of-the-art learning approaches which verifies its superiority in improving SEE for UAV-IRS-assisted LAE wireless communications network.

cs.IT↗
arXiv2026

3D Spherical Fluid Antennas for Spatially Reconfigurable Communications

As sixth-generation (6G) wireless systems evolve toward higher frequency bands, large-scale antenna arrays, and intelligent interaction with the wireless environment, conventional fixed-position antennas (FPAs) are increasingly constrained by limited spatial degrees of freedom and insufficient hardware-level adaptability. Fluid antenna systems (FAS) provide new physical-layer flexibility by dynamically reconfiguring antenna ports, geometries, and radiation characteristics. However, existing studies have mainly focused on one- or two-dimensional apertures, leaving the spatial reconfigurability required for complex three-dimensional (3D) propagation environments insufficiently exploited. In this article, we present a 3D spherical fluid antenna system (3D SFAS) architecture for flexible spatially reconfigurable communications. By activating radiating elements in different spherical regions, 3D SFAS realizes array-level spatial reconfiguration through flexible region switching. Within the selected regions, element-level reconfiguration further adjusts the effective aperture size, array topology, and radiation characteristics. This joint framework enables flexible beamforming, concurrent multi-region transmission, blockage-adaptive aperture switching, effective-aperture reconfiguration, and high-resolution 3D aperture control. We also discuss its potential applications in space-air-ground integrated networks, high-mobility communications, integrated sensing and communication systems, and emergency communications. Numerical results demonstrate the potential of 3D SFAS to improve wireless communication performance through flexible spatial reconfiguration. Overall, 3D SFAS extends FAS design beyond 2D position switching toward comprehensive 3D spatial reconfigurability.

eess.SP↗
arXiv2026

Control-Certified Wireless Resource Allocation for Digital-Twin-Enabled UAV Swarms

Wireless resource allocation in digital-twin-enabled unmanned aerial vehicle (UAV) swarms must be both network-feasible and certifiably safe for closed-loop control. Existing packet-level or scalar-priority schedulers cannot meaningfully compare heterogeneous multi-hop actions that differ simultaneously in route, retransmission depth, blocklength, bidirectional delay, delivery probability, and TDMA slot cost. This paper introduces a certificate-guided resource allocation framework for low-altitude multi-hop UAV swarms. A digital twin maps predicted topology, channel, route, and controller-side state into a shared five-dimensional quality-of-service (QoS) certificate comprising uplink/downlink delay bounds, directional delivery guarantees, and a certified upper bound on the interval between successful bidirectional interactions. A state-conditioned stochastic drift test then admits only certificates whose augmented Lyapunov drift is nonpositive under the current controller state. Admitted actions are reduced to certified supply frontiers by removing dominated route-slot configurations, and the online scheduler maximizes Lyapunov-drift reduction under a shared TDMA slot budget via exact dynamic programming. Closed-loop ns-3 simulations demonstrate that the proposed framework outperforms fixed-service, certificate-filtered fixed-priority, dynamic-transmission-count, and value-of-information baselines in both tracking accuracy and high-risk state suppression under identical communication budgets.

eess.SY↗
arXiv2026

Utility-Aware Progressive Inference over UDP Packet Blocks for Emergency Communications

Emergency communications increasingly rely on remote visual inference for timely hazard detection under stringent bandwidth and latency constraints. However, conventional UDP-based visual delivery typically performs inference only after the full payload has been received, even though partially received packet blocks may already contain sufficient task-relevant evidence for reliable decision making. This paper proposes a utility-aware progressive inference framework for emergency communications, which operates directly on UDP packet blocks and determines when sufficient task value has been accumulated for early hazard recognition. Specifically, the sender estimates packet-level decision utility as lightweight control metadata, while the receiver progressively updates partial observations, accumulates the utility of received packets, and triggers an early stop once the normalized utility exceeds a prescribed threshold. Experiments on a fire-scene detection dataset show that, at the main operating point, the proposed method reduces the average packet budget by 34.2% and the decision delay by 1209.17 ms while retaining 91.5% of the full-reception match rate. The method also maintains its advantage over the stability-based baseline under moderate packet loss and different packet-arrival orders. These results demonstrate that packet-level utility provides an effective basis for communication-efficient and delay-aware hazard recognition over UDP-based emergency links.

eess.SP↗
arXiv2026

In-Network Artificial Computing Enhanced Light Model-Switching for Emergency Communications Networks

Emergency communications networks require in-network intelligence for timely traffic handling under dynamic demands and runtime constraints. In these environments, packets may need different inference behaviors, and conventional model replacement via control-plane updates is too slow for responsive operation. We propose an in-network artificial computing framework with lightweight model-switching, where multiple Binary Neural Network (BNN) models are kept resident within a shared execution framework. Packet metadata selects the active model at packet granularity with O(1) selection cost. A fixed 1024-byte payload is aligned with x86 AVX-512, enabling efficient memory access. The framework is realized on an eBPF/XDP + AF_XDP stack. Experimental results show that the system sustains 1.894 Mpps with a 0.528 us inference latency, while model selection adds only 0.005 us. Our results demonstrate that different resident models induce distinct packet-processing behaviors, that scaling to 16 slots preserves low switching overhead, and that online model switching completes without wrong-verdict packets. These results show the practicality of lightweight in-network artificial computing on commodity hardware.

cs.NI↗
arXiv2026

Anti-Jamming Optimization for EM-Compliant Active RIS via Decoupling Architecture

Wireless communication systems are increasingly vulnerable to sophisticated jamming attacks with the rapid evolution of jamming technologies and advanced signal processing techniques. While traditional anti-jamming techniques offer limited performance gains, active reconfigurable intelligent surfaces (RISs) have emerged as a promising channel-domain solution for improving resilience against jamming. Nonetheless, existing studies often rely on simplified electromagnetic (EM) models that do not fully capture mutual coupling (MC) and impedance mismatches in RIS hardware. In this paper, we propose an EM-compliant active (EMC-Active) RIS model for anti-jamming systems, explicitly incorporating the EM and physical properties at active RIS, such as MC effects, channel correlation, and discrete phase. To evaluate the anti-jamming performance of the proposed EMC-Active RIS, we develop a low-complexity alternating optimization (AO) algorithm based on the decoupling architecture (DA) to maximize the ergodic achievable rate. By leveraging the DA to explicitly eliminate MC effects among REs, the original coupled system is transformed into a tractable and scalable uncoupled representation. Numerical results demonstrate that the DA-based AO algorithm can significantly reduce the modeling and optimization complexity and efficiently solve the problem in an alternating manner with substantially reduced iteration overhead.

cs.IT↗
arXiv2026

Fundamental for Delay and Reliability Guarantees for Emergency UAV

To support mission-critical services in emergency scenarios, wireless networks are required to provide stringent guarantees under massive Ultra-Reliable Low-Latency Communications (mURLLC) constraints. Distributed unmanned aerial vehicle (UAV)-based massive multiple-input multiple-output (MIMO) architectures have recently emerged as a promising solution for rapidly deployable emergency communication systems. However, how to fundamentally characterize and guarantee statistical quality-of-service (QoS) for such systems in the finite blocklength regime remains largely unexplored. To overcome these challenges, in this paper we develop a fundamental analytical framework for delay and reliability bounded QoS guarantees in distributed UAV-based massive MIMO emergency networks under finite blocklength coding (FBC). By rigorously modeling the stochastic service process of distributed massive MIMO fading channels, we derive statistical characterizations the delay and error-rate bounded QoS exponents. We also establish QoS-driven controlling functions, including the $\epsilon$-effective capacity and the feasible QoS region. Finally, the obtained simulation results validate and evaluate our developed modeling techniques and asymptotic formulations to support mURLLC.

cs.IT↗
arXiv2026

Enhancing AAV-Enabled Secure Communications via Synthetic Aperture Beamforming

In this paper, we consider a synthetic aperture secure beamforming approach for a virtual multiple-input multiple output (MIMO) broadcast channel in the presence of hybrid wiretapping environments. Our goal is to design the flight node deployment constructed by a single-antenna mobile autonomous aerial vehicle (AAV), corresponding transmission symbol strategy, transmit precoding, and received beamforming to maximize the system channel capacity. Leveraging the synthetic aperture beamforming, we aim to provide spatial gain along a predefined angle in free space while reducing it in others and thus enhance physical layer (PHY) security. To this end, we analyze the expression of the asymptotic channel eigenvalues to optimize the AAV flight node deployment. For the optimal precoding design, an energy-efficient method that minimizes the transmit power consumption is studied based on the given virtual MIMO channel, while meeting the quality of service (QoS) for the base station (BS), leakage tolerance of eavesdroppers (Eves), and per-node power constraints. The power minimization problem is a non convex program, which is then reformulated as a tractable form after some mathematical manipulations. Moreover, we design the received beamforming by applying the linearly constrained minimum variance (LCMV) method such that the jamming can be effectively suppressed. Numerical results demonstrate the superiority of the proposed method in promoting capacity.

eess.SP↗
arXiv2026

Statistical-Geometric Degeneracy in UAV Search: A Physics-Aware Asymmetric Filtering Approach

Post-disaster survivor localization using Unmanned Aerial Vehicles (UAVs) faces a fundamental physical challenge: the prevalence of Non-Line-of-Sight (NLOS) propagation in collapsed structures. Unlike standard Gaussian noise, signal reflection from debris introduces strictly non-negative ranging biases. Existing robust estimators, typically designed with symmetric loss functions (e.g., Huber or Tukey), implicitly rely on the assumption of error symmetry. Consequently, they experience a theoretical mismatch in this regime, leading to a phenomenon we formally identify as Statistical-Geometric Degeneracy (SGD)-a state where the estimator stagnates due to the coupling of persistent asymmetric bias and limited observation geometry. While emerging data-driven approaches offer alternatives, they often struggle with the scarcity of training data and the sim-to-real gap inherent in unstructured disaster zones. In this work, we propose a physically-grounded solution, the AsymmetricHuberEKF, which explicitly incorporates the non-negative physical prior of NLOS biases via a derived asymmetric loss function. Theoretically, we show that standard symmetric filters correspond to a degenerate case of our framework where the physical constraint is relaxed. Furthermore, we demonstrate that resolving SGD requires not just a robust filter, but specific bilateral information, which we achieve through a co-designed active sensing strategy. Validated in a 2D nadir-view scanning scenario, our approach significantly accelerates convergence compared to symmetric baselines, offering a resilient building block for search operations where data is scarce and geometry is constrained.

cs.RO↗
arXiv2026

Movable Antenna Enabled Reconfigurable Array Topologies for Structured Beam Communications

Spatially structured beams have emerged as a promising technology for enhancing spectrum efficiency (SE) in sixth-generation (6G) networks. However, structured beam schemes based on fixed-position antennas (FPAs) fail to fully exploit the array aperture, thereby limiting their topological reconfigurability and adaptability to diverse communication scenarios. To overcome these limitations, this paper proposes a novel structured beam communication framework exploiting movable antennas (MAs) to achieve reconfigurable array topologies. Specifically, we develop an MA-based geometric modeling framework to construct a variety of practical array topologies, thereby enabling the realization of diverse array configurations utilizing a unified hardware platform. Furthermore, we investigate the joint design of the array topology and the structured beamforming vector to efficiently exploit the array aperture and facilitate the multiplexing of orthogonal spatial modes. Accordingly, we formulate the corresponding beam generation and demodulation schemes and derive the channel gains under varying array topologies. We also propose an alternating optimization algorithm to jointly optimize the array topology configuration, the antenna element positions, and the structured beamforming vector, with the aim of maximizing the system SE. Numerical results demonstrate that the proposed joint design significantly enhances the SE compared to conventional FPA schemes. By synergizing the spatial multiplexing degrees of freedom (DoFs) of structured beams with the mobility DoFs of MAs within 2D planar regions, this work establishes a reconfigurable and practical framework for structured beam-based wireless communications.

eess.SP↗
arXiv2026

Spectral Graph Analysis for Predicting QoE Fairness Sensitivity in Wireless Communication Networks

The evaluation of Quality of Experience (QoE) fairness depends not only on its current state but, more critically, on its sensitivity to changes in Service Level Agreement (SLA) parameters. However, the academic community has long lacked a predictive method connecting underlying topology to high-level service fairness. To bridge this gap, this paper analyzes a QoE imbalance index ($I$) through the lens of spectral graph theory.Our core contribution is the proof of a novel exponential spectral upper bound. This bound reveals that the improvement of QoE fairness exhibits an exponential decay behavior only above a performance threshold determined jointly by network size and connectivity. Its core decay rate is dominated by the weaker of two factors: the SLA stringency ($a$) and the network's spectral gap ($c\lambda_2$). The upper bound unifies the service protocol and the topological bottleneck within a single performance bound formula for the first time.This theoretical relationship also reveals a clear bottleneck effect, where the system's fairness ceiling is determined by the weaker link between service parameters and network structure. This finding provides a bottleneck-driven principle for resource optimization in network design and enables goal-driven reverse engineering. Extensive numerical experiments on various random graph models and real-world network topologies robustly validate the correctness and universality of our analytical framework.

cs.IT↗