Jie Tang

Orthogonal time frequency space (OTFS) modulation is a promising technology for high-Doppler-effect mobile communication. However, fractional Doppler shifts cause inter Doppler interference, degrading the channel estimation (CE) performance, particularly at low signal-to-noise ratios (SNRs). Previous approaches have framed Doppler shift estimation as a direction-of-arrival problem but have limited accuracy because of the finite number of available Doppler points. To overcome this challenge, we propose a delay-staggered pilot scheme that leverages the abundant resources in the delay domain, effectively increasing the number of points for CE and enhancing its accuracy. Additionally, we introduce a novel angle-of-arrival estimation method tailored to the unique channel characteristics of OTFS modulation, reducing the antenna array requirements. Simulation results demonstrate that the proposed method achieves superior NMSE performance compared with existing approaches, including embedded pilot-based CE and grid evolution-based CE algorithms.

Accurate State of Health (SOH) estimation and Remaining Useful Life (RUL) prediction on uninterruptible power supply (UPS) batteries are critical for ensuring system reliability. However, battery degradation processes exhibit significant non-linearity and non-stationarity, posing challenges for traditional deep learning models in capturing long-term dependencies and handling sensor noise. In this paper, we propose BatteryMoE, a novel normalization-free sparse Mixture-of-Experts (MoE) Transformer designed for robust battery prognostics. Distinct from standard Transformers, BatteryMoE incorporates three key physics-aware architectural innovations: (1) It introduces a dynamic error function mechanism to replace traditional normalization layers, constructing a normalization-free architecture that enhances robustness against high-variance battery data. (2) It adopts an imaginary-enhanced Rotary Positional Embedding that utilizes the full complex-valued attention score to capture both local charge–discharge patterns and global long-term degradation trends, significantly improving RUL extrapolation. (3) A sparse MoE strategy is employed to dynamically activate specialized experts for different aging stages (e.g., linear degradation vs. capacity plunge), balancing model capacity with robust predictive performance. Extensive experiments conducted on five widely used datasets show that BatteryMoE achieved significant results.

In the evolving landscape of mobile edge computing (MEC), enhancing communication reliability and computation efficiency to support increasingly stringent low-latency services remains a fundamental challenge. Rotatable antenna (RA) is a promising technology that introduces new spatial degrees-of-freedom (DoFs) to tackle this challenge. In this letter, we investigate an RA-enabled MEC system where antenna bore-sight directions can be independently adjusted to proactively improve wireless channel conditions for latency-critical users. We aim to minimize the maximum computation latency by jointly optimizing the MEC server’s computing resource allocation, receive beamforming, and the deflection angles of all RAs. To address the resulting non-convex problem, we develop an efficient alternating optimization (AO) framework. Specifically, the optimal edge computing resource allocation is derived based on the Karush–Kuhn–Tucker (KKT) conditions. Given the computing resources, the receive beamforming is optimized using semidefinite relaxation (SDR) combined with a bisection search. Furthermore, the RA deflection angles are optimized via fractional programming (FP) and successive convex approximation (SCA). Simulation results verify that the proposed RA-enabled MEC scheme significantly reduces the maximum computation latency compared with conventional benchmark methods.

Accurately capturing spatiotemporal dependencies and enabling effective decision support are core challenges in Intelligent Transportation Systems (ITS). Existing research often treats traffic prediction and path planning as isolated tasks. Moreover, mainstream prediction models struggle with long-term periodic patterns, while Reinforcement Learning (RL)-based planning often suffers from inefficient exploration in sparse topologies. To address these issues, this paper proposes a unified framework combining a spectral-temporal Graph Neural Network (GNN) and bi-directional decoding RL. Specifically, a time-frequency dual-stream adaptive learning module is introduced for prediction. Fast Fourier Transform (FFT) and Gated Recurrent Unit (GRU) are employed to capture global frequency periodicities and local temporal dynamics, respectively. Their adaptive fusion effectively mitigates the long-sequence information forgetting problem. For path planning, the task is formulated as sequence generation. A graph-aware attention encoder with adjacency masking is designed, and heuristic feature embeddings are incorporated to guide efficient exploration. Furthermore, a bi-directional autoregressive decoding strategy enhances robustness against topological bottlenecks. On PEMSD4 and PEMSD8, the proposed predictor achieves MAE/RMSE/MAPE values of 18.211/30.433/12.006 and 13.587/23.566/8.955, respectively. Path-planning simulations on the PEMSD4-derived sparse topology further demonstrate stable bi-directional RL optimization, faster convergence with heuristic guidance, and a sparsity-aware encoder that reduces redundant attention interactions in sparse road networks. These results validate the effectiveness of the proposed “predict-then-plan” paradigm.

The integration of fluid antenna systems (FAS) into integrated sensing and communication (ISAC) networks offers promising reconfigurability for air–ground scenarios in low-altitude economic (LAE) environments. This paper investigates a monostatic FAS-aided ISAC architecture comprising a two-dimensional (2D) FAS-based transmit array and a fixed-position receive array, aiming to enhance the joint performance across multiple ground user equipments (GUEs) and one aerial user equipment (AUE). A joint optimization problem is formulated to maximize a weighted utility metric integrating the communication signal-to-interference-plus-noise ratio (SINR) of the AUE and the sensing signal-to-clutter-plus-noise ratio (SCNR), subject to the transmit power constraint, inter-antenna spacing constraint, deployment geometry, and the minimum SINR requirement of GUEs. The resulting problem involves coupled spatial variables and fractional structures, which are addressed via an alternating optimization (AO) framework. Specifically, the receive beamformer is derived in closed form, the transmit beamformer is optimized via fractional programming (FP) and successive convex approximation (SCA), and the antenna positions are iteratively refined on a per-element basis through second-order Taylor expansion to construct convex surrogate upper bounds. Numerical results verify that the proposed FAS-enhanced ISAC system substantially outperforms benchmark schemes.

Integrated sensing and communication (ISAC) is regarded as a promising solution for the development of the emerging low-altitude economy (LAE). Given the necessity of accurate and realistic wireless channel models for ISAC evaluation and optimization, this paper proposes an LAE-oriented ISAC channel modeling framework. By incorporating the target unmanned aerial vehicle (UAV) scattering response, we decouple the ISAC channel into target and background channels. For the target channel, the model is formulated as a cascade of the transmitting base station (BS)-target link, the target-sensing BS link, and the scattering response from the target UAV. For the background channel, a novel parameter is introduced to separate the influence of the target UAV. Furthermore, key statistical properties, including the space-time-frequency correlation function, coherence distance, Doppler power spectral density, root mean square delay spread, and stationarity interval, are derived and analyzed. The accuracy of the proposed channel model is verified by the close agreement between simulated statistics properties and measured data, and the effectiveness of the cascaded method is validated by the close agreement between the concatenation output and simulation results.

Integrated sensing and communication (ISAC) is envisioned as a promising technology to empower the emerging low-altitude economy (LAE). However, current networks primarily serve terrestrial users, resulting in insufficient low-altitude coverage. Furthermore, standalone ISAC systems exhibit restricted sensing ranges, as fixed antenna orientations suffer from significant gain loss at large angles. In view of this, we propose cooperative ISAC enabled by rotatable antenna (RA) technology. RA aligns its boresight with target directions through antenna rotation, effectively mitigating gain loss at large angles. This compact and low-complexity design provides robust coverage and high-precision sensing, making it well-suited for deployment in the LAE. Specifically, multiple RA-equipped base stations (BSs) employ coordinated beamforming to provide robust connectivity for authorized unmanned aerial vehicles (UAVs), while concurrently monitoring distributed critical locations within the target airspace for potential intrusions. To implement this scheme, we maximize the system sum-rate subject to various practical constraints by jointly optimizing BS-UAV associations, transmit beamforming, RA orientation, and UAV trajectories. This nonconvex problem is addressed by an efficient alternating optimization (AO) algorithm that integrates semidefinite relaxation (SDR) and successive convex approximation (SCA). Finally, numerical results demonstrate that the proposed RA-enhanced ISAC system outperforms baselines in line-of-sight (LoS)-dominated, large-angle low-altitude scenarios.

Fluid antenna systems (FAS) are emerging as a transformative enabler for sixth-generation (6G) wireless communications, providing unprecedented spatial diversity through dynamic reconfiguration of antenna ports. However, the inherent spatial correlation among ports poses significant challenges for accurate analysis. Conventional models such as Jakes are analytically intractable, while oversimplified constant-correlation models fail to capture the true behavior. In this work, we address these challenges by applying the variable block-correlation model (VBCM)—originally proposed by Ramírez-Espinosa et al. in 2024—to FAS security analysis, and by developing comprehensive optimization methods to enhance analytical accuracy. We derive new closed-form expressions for average secrecy capacity (ASC) and secrecy outage probability (SOP), demonstrating that the VBCM framework achieves simulation-aligned accuracy, with relative errors consistently below 5% (compared to 10–15% for constant-correlation models). To maximize ASC, we further design two algorithms: a grid search (GS) method and a gradient descent (GD) method. Numerical results reveal that the VBCM-based approach not only provides reliable insights into FAS security performance, but also yields substantial gains—ASC improvements exceeding 120% in high-threat scenarios and 18–19% performance enhancements for compact antenna configurations. These findings underscore the practical value of integrating VBCM into FAS security analysis and optimization, establishing it as a powerful tool for advancing 6G communication systems.

Most existing antenna array-based source localization methods rely on fixed-position arrays (FPAs) and strict assumptions about source field conditions (near-field or far-field), which limits their effectiveness in complex, dynamic real-world scenarios where high-precision localization is required. In contrast, this paper introduces a novel scalable fluid antenna system (SFAS) that can dynamically adjust its aperture configuration to optimize performance for different localization tasks. Within this framework, we develop a two-stage source localization strategy based on the exact spatial geometry (ESG) model: the first stage uses a compact aperture configuration for initial direction-of-arrival (DOA) estimation, while the second stage employs an expanded aperture for enhanced DOA and range estimation. The proposed approach eliminates the traditional need for signal separation or isolation to classify source types and enables a single SFAS array to achieve high localization accuracy without field-specific assumptions, model simplifications, or approximations, representing a new paradigm in array-based source localization. Extensive simulations demonstrate the superiority of the proposed method in terms of localization accuracy, computational efficiency, and robustness to different source types.

Unlike conventional fixed-antenna architectures, rotatable antenna (RA) has shown great potential in enhancing wireless communication performance by exploiting additional spatial degrees of freedom (DoFs) in a cost-effective manner. In this letter, we propose a novel RA-enabled covert communication system, where an RA array-based transmitter (Alice) sends covert information to a legitimate user (Bob) in the presence of multiple wardens (Willies). To maximize the covert rate, we optimize the transmit beamforming vector and the rotational angles of individual RAs, subject to the constraints on covertness, transmit power, and antenna rotational range. To address the non-convex formulated problem, we decompose it into two subproblems and propose an efficient alternating optimization (AO) algorithm to solve the two subproblems iteratively, where the second-order cone programming (SOCP) method and successive convex approximation (SCA) approach are applied separately. Simulation results demonstrate that the proposed RA-enabled covert communication system can provide significantly superior covertness performance to other benchmark schemes.