arXiv subjects

Tianqi Mao

Publications and source records attributed to Tianqi Mao.

At least 19 recordsLinked to original sources

arXiv2026

Movable Antenna-Enhanced MIMO-OFDM ISAC: Ambiguity Function Analysis, Waveform Design and Antenna Position Optimization

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) provides abundant spatial and time-frequency degrees of freedom for integrated sensing and communication (ISAC), while movable antennas (MAs) further introduce reconfigurable spatial freedom through array geometry adjustment. However, how the array geometry and information bearing MIMO-OFDM waveform jointly shape the three dimensional (3D) ambiguity response remains insufficiently understood, and conventional two dimensional (2D) range-Doppler metrics cannot fully characterize this coupling. This paper investigates MA-enhanced MIMO-OFDM ISAC with joint design of the transmit MA positions and symbol-level precoding (SLP) waveform. The discrete periodic angle-range-Doppler ambiguity function is first derived, and its structure is characterized in terms of waveform rank. It is proved that a rank one waveform yields a 3D ambiguity response that is separable between the angular dimension and the range-Doppler plane, whereas a higher rank waveform combines multiple spatial components with different weights across candidate angles, allowing the range-Doppler response to vary with the candidate angle and enabling the MA positions to further shape the 3D ambiguity response. Based on this analysis, a 3D integrated sidelobe level ratio (ISLR) is defined, the joint optimization of the MA positions and SLP waveform is formulated, and a penalty dual decomposition based alternating optimization algorithm is developed. For the radar-only case, the existence of at least one rank one globally optimal solution is established, enabling a lossless reduction from the original high dimensional MIMO-OFDM waveform design to a low dimensional joint design of the spatial beam and MA positions.

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arXiv2026

Index Modulation Aided Phase-Rotated AFDM for Efficient Integrated Sensing and Communication: Framework, Optimization and Performance Analysis

Affine frequency division multiplexing (AFDM) has emerged as a competitive waveform for integrated sensing and communication (ISAC) in high-mobility scenarios, but its data-dependent high peak-to-average power ratio (PAPR) and ambiguity function (AF) sidelobes may degrade the power-amplifier efficiency as well as the sensing performance. To mitigate the issues, one promising candidate is the phase-rotation optimization philosophy, which necessitates auxiliary side information (SI) to guarantee successful data recovery at the receiver. However, using additional signalling overhead to deliver the SI degrades the spectral efficiency. Against the background, this paper proposes a power level index modulation (PLIM)-aided phase-rotated AFDM (PLIM-PR-AFDM) scheme, which embeds the phase-rotation SI within the transmitted block. At the transmitter, the proposed scheme jointly optimizes the waveform by coordinating the PLIM power allocation, phase rotation, and embedding of SI. Specifically, subcarriers are flexibly allocated with different transmit power levels. High-power subcarriers are phase-rotated to suppress the PAPR and fast-slow-time AF sidelobes. By decomposing the fast-slow-time AF sidelobe energy into per-block periodic autocorrelation function (PACF) terms, the sensing objective can be formulated as a PACF-weighted integrated sidelobe level (WISL) minimization problem, while selected low-power subcarriers embed the phase bits as SI through constellation labels. Furthermore, the PLIM power patterns of each transmitted block can convey additional index bits, which compensates for the spectral efficiency loss caused by SI transmission. Moreover, the error performance is analyzed by deriving an upper bound of the average bit error probability (ABEP) and identifying the condition for full diversity.

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arXiv2026

Adaptive $c_2$-Perturbed AFDM Waveform Design for Integrated Sensing and Communication

Affine frequency division multiplexing (AFDM) is a promising waveform for integrated sensing and communication (ISAC) systems owing to its superior performance in time--frequency doubly dispersive channels. However, AFDM still faces a pair of challenges: high PAPR and random data symbols produce imperfect autocorrelation sidelobes. To address these challenges, this paper proposes a real-time data-driven framework that optimizes the pre-chirp parameter $c_2$ to enhance the AFDM-ISAC performance. Specifically, a side-information-free optimization problem is formulated to reduce PAPR and the weighted integrated sidelobe levels of both aperiodic and periodic autocorrelation functions, with complexity comparable to that of the conventional AFDM receiver. Furthermore, an efficient non-monotone line-search spectral projected-gradient algorithm is developed by exploiting closed-form gradients. Simulation results demonstrate that the proposed method achieves a superior sensing vs. communications trade-off and is capable of striking a promoted bit error rate performance in the presence of severe power amplifier nonlinearity.

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arXiv2026

Probabilistic Symbol-Level Precoding based Affine Frequency Division Multiplexing Transmission

Affine frequency division multiplexing (AFDM) has recently gained significant attention due to its robustness against time-frequency doubly selective channel fading. However, the high computational complexity at the receiver poses a critical challenge for practical deployment. To overcome this issue, we propose a probabilistic symbol-level precoding (SLP)-based AFDM transmission framework, in which the processing burden in downlink transmission is shifted from the user to the base station (BS), enabling direct symbol detection without channel estimation or equalization at the receiver. In the proposed framework, the BS exploits the uplink channel state information (CSI) to design the downlink transmit waveform based on uplink-downlink channel reciprocity. In particular, we innovatively introduce a probabilistic SLP technology by explicitly characterizing the likelihood of symbol detection errors under noise perturbations. Specifically, the transmitted symbols are optimized to minimize the likelihood that the received symbols fall into erroneous decision regions, where the resulting error-probability minimization problem is subsequently approximated as a second order cone programming (SOCP) problem by exploiting the monotonicity of the objective function. Simulation results show that the proposed probabilistic SLP-based scheme achieves performance comparable to that of conventional AFDM receivers, whilst enjoying significant reduction of computational complexity at the receiver end. These results demonstrate the effectiveness and practical potential of the proposed approach.

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arXiv2026

Beyond Fast Contractions: Attenuation and Recovery of Matrix-Engine Speedups in High-Order Finite Elements

Modern processors increasingly provide matrix engines whose peak arithmetic throughput greatly exceeds conventional SIMD, but scientific applications rarely realize this advantage end to end. We examine this gap in SPECFEM3D's dominant stiffness operator on the Arm LX2 CPUs that power the flagship Lineshine supercomputer. Against a matched, high-performance SVE baseline on the same cores, SME's $4\times$ single-precision peak advantage falls to $2.2\times$ for isolated tensor contractions and $1.1\times$ for the complete operator. Our factorized diagnostic attributes the loss to pointwise computation, indirect field movement and synchronization, and irregular coefficient delivery. Explicit SIMD mitigates pointwise work, raising the full-operator speedup to $1.3\times$. Field-layout changes mitigate indirect movement and synchronization, while vector-blocked coefficient streaming reduces irregular-access costs; together they raise speedup to $1.6\times$ at high order. A contraction-free control bounds further contraction-only gains at $1.11$--$1.32\times$. Realizing matrix-engine performance therefore requires co-designing the entire operator path, not merely replacing its contraction kernel.

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arXiv2026

Movable Subarray-Aided ISAC in Hybrid Near-Far Field Channels

This letter investigates an integrated sensing and communication (ISAC) system aided by movable subarrays (MSAs) using a hybrid near-far field channel model. The sensing target and communication users are assumed to lie in the near field of the overall MSA aperture but in the far-field region of each subarray. Accordingly, a hybrid near-far field channel model is established, and the equivalent Fisher information matrix and Cramér-Rao bound (CRB) for joint range, elevation, and azimuth estimation are derived. The transmit beamforming matrix and subarray positions are jointly optimized to minimize the trace CRB subject to minimum communication signal-to-interference-plus-noise ratio (SINR), maximum transmit power and subarray movement constraints. An alternating optimization algorithm is developed combining iterative rank-one-penalized semidefinite relaxation with projected finite-difference block descent and backtracking. Numerical results show that the hybrid-field model closely matches the spherical-wave model, while MSAs substantially reduce the CRB.

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arXiv2026

Joint Chirp Parameter Selection and Low-Complexity MMSE Receiver Design for AFDM Systems

Affine frequency division multiplexing (AFDM) has emerged as a promising waveform against doubly selective channels under high-mobility communication scenarios. Optimal chirp parameter selection and reduced-complexity receiver design in AFDM are essential for achieving satisfactory bit error rate (BER) performance with low computational complexity. In this paper, we investigate the joint optimization of chirp-parameter selection and low-complexity minimum mean square error (MMSE)-based receiver design by exploiting the structural characteristics of the AFDM effective channel matrix (ECM). First, a simplified BER performance metric is derived by leveraging the diagonal and circulant structure of the discrete affine Fourier transformation (DAFT), based on which a fast circulant-diagonal aggregation (FCDA) algorithm is developed for efficient $c_1$ selection. Then, a low-complexity banded MMSE (LC-BMMSE) receiver is developed by constructing a cyclic-banded ECM through path-wise structured sparsification, where banded Cholesky factorization is employed to avoid direct matrix inversion. Building upon the proposed BER metric and the LC-BMMSE receiver, a hierarchical-search-based joint chirp parameter and structured sparsification (HS-JCPS) algorithm is further proposed to jointly optimize the chirp parameter and sparsification pattern under a given complexity constraint. Simulation results demonstrate that the proposed FCDA reduces the search time for the optimal $c_1$ by an order of magnitude compared with using a BER-based criterion. Moreover, the proposed HS-JCPS algorithm with the LC-BMMSE receiver can identify a near-optimal $c_1$, while attaining a superior performance-complexity tradeoff.

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arXiv2026

Rotatable Antenna-Enhanced Secure Integrated Sensing and Communications Under Imperfect CSI

A rotatable antenna (RA)-enhanced secure integrated sensing and communications system is investigated, where an RA-based transceiver simultaneously communicates with legitimate users and senses a target that is regarded as a potential eavesdropper. Under imperfect eavesdropping channel state information (CSI), a max-min data rate optimization problem is formulated by jointly optimizing the transmit beamforming, artificial noise (AN) covariance matrix, and transmit/receive boresights of RAs, subject to the maximum information leakage and minimum sensing power constraints. To address the highly non-convex problem, the information leakage and sensing power constraints are transformed into convex ones via S-Procedure method and Cauchy-Schwarz inequality, respectively. Subsequently, an alternating optimization algorithm is developed to decompose the reformulated problem into two subproblems. In particular, the transmit beamforming and AN covariance matrix are optimized by utilizing successive convex approximation and semi-definite relaxation methods, while the RA boresights are obtained by invoking the particle swarm optimization. Simulation results show that the RA-based scheme significantly outperforms the benchmarks, and offers enhanced robustness against imperfect CSI with the increase of the maximum rotation range.

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arXiv2026

High-Order Spectral Element Methods for Wave Propagation on ARM Multicore CPU with SME: Optimizations and Implications

Wave propagation based on the spectral element method (SEM) is a representative HPC workload, but existing SEM implementations are not well matched to emerging ARM multicore CPUs with Scalable Matrix Extension (SME). We present an SME-enabled optimization of \textsc{SPECFEM3D} on the emerging LX2 processor that combines an SME-aware batched small-matrix kernel for SEM tensor-product operators, a memory-aware hybrid MPI+OpenMP execution scheme for limited-HBM systems, and a dispersion-based iso-accuracy study of the $(h,p)$ tradeoff. At fixed polynomial order, the optimized implementation improves full-application performance by 4--6$\times$ over the original code and delivers clear gains over optimized non-SME CPU baselines. Beyond these implementation-level gains, our results suggest that SME shifts the performance-favorable operating point toward higher polynomial orders along the dispersion-based iso-accuracy frontier, further reducing time-to-solution and working-set size. These results indicate that SME affects not only kernel efficiency, but also the practical discretization tradeoff for SEM on modern ARM multicore platforms.

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arXiv2026

DAFT-s-AFDM Enabled ISAC Systems: Ambiguity Function Analysis and Waveform Design

Discrete affine Fourier transform spread affine frequency division multiplexing (DAFT-s-AFDM) is a promising waveform for integrated sensing and communication (ISAC) due to its low peak-to-average power ratio, robustness to Doppler shifts, and reduced multiuser interference in the uplink transmission. This paper presents a comprehensive ambiguity function (AF) analysis of DAFT-s-AFDM and derives the closed-form expression for the AF magnitude expectation. Several key insights into the impact of DAFT-s-AFDM parameters on ISAC performance are revealed, thus providing concrete guidance for the subsequent waveform design. Building on these insights, a novel probabilistic constellation shaping (PCS) framework is proposed for ISAC waveform enhancement, where the communication throughput and the sensing AF characteristics are jointly optimized by addressing a multi-objective problem. An efficient algorithm based on a closed-form bit error rate expression is developed to obtain the Pareto-optimal solutions. Extensive simulations validate the theoretical results and that the proposed PCS-enhanced DAFT-s-AFDM can significantly outperform the classical counterparts, achieving a superior and highly controllable tradeoff between the dual-functional performances.

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arXiv2026

Vision-Intelligence-Enabled Beam Tracking for Cross-Interface Optical Wireless Communication between Underwater and Low-Altitude Platforms

The rapid expansion of oceanic applications such as underwater surveillance and mineral exploration is driving the need for real-time wireless backhaul of massive observational data. Such demands are challenging to meet using the narrowband acoustic approach. Alternatively, with the participation of low-altitude platforms (LAPs), water-air optical wireless communication (OWC) has emerged as a promising solution owing to its high potential for broadband transmission. However, implementing water-air OWC remains challenging, particularly when signals penetrate the fluctuating interface, where dynamic refraction induces severe beam misalignment with airborne stations. This necessitates real-time transceiver alignment capable of adapting to complex oceanic dynamics, which remains largely unaddressed. Against this background, this paper establishes a mathematical channel model for water-air optical transmission across a time-varying sea surface. Based on the model, a vision-based beam tracking algorithm combining convolutional neural network and bi-directional long short-term memory with an attention mechanism is developed to extract key spatio-temporal features. Simulations verify that the proposed algorithm outperforms classical methods in maintaining received signal strength and suppressing vision noise, demonstrating its robustness for water-air OWC systems.

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arXiv2026

Low-Altitude Wireless Networks: A Comprehensive Survey

The rapid development of the low-altitude economy has imposed unprecedented demands on wireless infrastructure to accommodate large-scale drone deployments and facilitate intelligent services in dynamic airspace environments. However, unlocking its full potential in practical applications presents significant challenges. Traditional aerial systems predominantly focus on air-ground communication services, often neglecting the integration of sensing, computation, control, and energy-delivering functions, which hinders the ability to meet diverse mission-critical demands. Besides, the absence of systematic low-altitude airspace planning and management exacerbates issues regarding dynamic interference in three-dimensional space, coverage instability, and scalability. To overcome these challenges, a comprehensive framework, termed low-altitude wireless network (LAWN), has emerged to seamlessly integrate communication, sensing, computation, control, and air traffic management into a unified design. This article provides a comprehensive overview of LAWN systems, introducing LAWN system fundamentals and the evolution of functional designs. Subsequently, we delve into performance evaluation metrics and review critical concerns surrounding privacy and security in the open-air network environment. Finally, we present the cutting-edge developments in airspace structuring and air traffic management, providing insights to facilitate the practical deployment of LAWNs.

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arXiv2026

Continuous Quantum Aperture: Beamforming with a Single-Vapor-Cell Rydberg Receiver

Beamforming is conventionally understood as a collective property of many discrete antenna elements in both communication and radar fields, which links angular selectivity to array size, element spacing, and band-specific hardware. Here we uncover a fundamentally different beamforming mechanism achieved by a Rydberg atomic receiver: a Rydberg-atom vapor cell dressed by a local-oscillator field constitutes a continuous quantum aperture. In this regime, spatially-varying quantum coherence across the aperture provides continuous amplitude-phase control, allowing a directional beam pattern to emerge from one sensing volume rather than from an engineered array. We establish the theory of continuous quantum aperture and show that tailoring the local-oscillator field can directly program the aperture response. This enables reconfigurable single-peak, multipeak, and multiband beamforming within a single vapor cell. Experiments on a Rydberg atomic receiver prototype verify that practical beam patterns agree with theoretical predictions across aperture sizes, frequency bands, and local-oscillator configurations. Leveraging this new beamforming mechanism, we further demonstrate interference mitigation, multiuser access, and multiband multiuser access with the single-vapor-cell platform. Our results identify the continuous quantum aperture as a new operating principle of Rydberg atomic receivers and establish single-vapor-cell beamforming as an integrated and reconfigurable platform for spatially selective electromagnetic reception.

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arXiv2026

Low-Altitude Agentic Networks for Optical Wireless Communication and Sensing: An Oceanic Scenario

The cross-domain oceanic connectivity ranging from underwater to the sky has become increasingly indispensable for a plethora of data-consuming maritime applications, such as maritime meteorological monitoring and offshore exploration. However, broadband implementations can be severely hindered by the isolation from terrestrial networks, limited satellite resources, and the fundamental inability of radio waves to bridge the water-air interface at high rates. To this end, this paper introduces an optical network bridging underwater, air and near space, which features a number of cooperative low-altitude platforms (LAPs), serving as compute-capable, sensing-aware, and mission-adaptive agents. The network architecture consists of three scenario-specific segments, i.e., water-air direct link, low-altitude mesh network, and the near-space access network. With coordinate sensing and intelligent control, the system tightly couples beam tracking and resource optimization, enabling resilient networking under high mobility and harsh maritime dynamics. Furthermore, we review enabling technologies spanning from water-air channel modeling, adaptive beam alignment under sea-surface perturbations, to swarm-intelligence networking for decentralized control, integrated pose-topology planning, and optical Integrated sensing and communication (ISAC) for near-space target detection and beam alignment. Finally, open issues are also highlighted, constituting a clear roadmap toward scalable, secure, and ultra-broadband oceanic optical networks.

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arXiv2026

Rydberg Atomic Receivers for Multi-Band Communications and Sensing

Harnessing multi-level electron transitions, Rydberg Atomic REceivers (RAREs) can detect wireless signals across a wide range of frequency bands, from Megahertz to Terahertz. This capability enables multi-band wireless communications and sensing (CommunSense). Existing research on multi-band RAREs primarily focuses on experimental demonstrations, lacking a tractable model to mathematically characterize their mechanisms. This issue leaves the multi-band RARE as a black box and poses challenges in its practical applications. To fill in this gap, this paper investigates the underlying mechanism of multiband RAREs and explores their optimal performance. For the first time, an analytical transfer function with a closed-form expression for multi-band RAREs is derived by solving the quantum response of Rydberg atoms. It shows that a multiband RARE simultaneously serves as a multi-band atomic mixer for down-converting multi-band signals and a multi-band atomic amplifier that reflects its sensitivity to each band. Further analysis of the atomic amplifier unveils that the intrinsic gain at each frequency band can be decoupled into a global gain term and a Rabi attention term. The former determines the overall sensitivity of a RARE to all frequency bands of wireless signals. The latter influences the allocation of the overall sensitivity to each frequency band, representing a unique attention mechanism of multi-band RAREs. The optimal design of the global gain is provided to maximize the overall sensitivity of multi-band RAREs. Subsequently, the optimal Rabi attentions are also derived to maximize the practical multi-band CommunSense performance. An experiment platform is built to validate the effectiveness of the derived transfer function, and numerical results confirm the superiority of multi-band RAREs.

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arXiv2025

New Paradigm for Integrated Sensing and Communication with Rydberg Atomic Receiver

The RYDberg Atomic Receiver (RYDAR) has been demonstrated to surmount the limitation on both the sensitivity and operating bandwidth of the classical electronic counterpart, which can theoretically detect indiscernible electric signals below -174 dBm/Hz with optical measurement through Rydberg-state atoms. Such miracle has established a new quantum-based paradigm for communications and sensing, which motivates a revolution of the transceiver design philosophies to fully unleash the potential of RYDAR towards next-generation networks. Against this background, this article provides a thorough investigation of Rydberg atomic communications and sensing from theory to hardware implementations. Specifically, we highlight the great opportunities from the hybridization between the RYDAR and the cutting-edge integrated sensing and communication (ISAC), followed by essential preliminaries of the quantum-based receiver. Then we propose a theoretical framework for broadband ISAC based on RYDAR, demonstrated by the proof-of-concept experiments. Afterwards, the enabling technologies for the ISAC framework are explored ranging from channel characterization, waveform design to array-based receiver configurations, where the open problems are also summarized. Finally, the future applications of RYDAR-based ISAC are envisioned, indicating its significant potential for both civilian and military purposes.

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arXiv2025

Intelligent Metasurface-Enabled Integrated Sensing and Communication: Unified Framework and Key Technologies

As the demand for ubiquitous connectivity and high-precision environmental awareness grows, integrated sensing and communication (ISAC) has emerged as a key technology for sixth-generation (6G) wireless networks. Intelligent metasurfaces (IMs) have also been widely adopted in ISAC scenarios due to their efficient, programmable control over electromagnetic waves. This provides a versatile solution that meets the dual-function requirements of next-generation networks. Although reconfigurable intelligent surfaces (RISs) have been extensively studied for manipulating the propagation channel between base and mobile stations, the full potential of IMs in ISAC transceiver design remains under-explored. Against this backdrop, this article explores emerging IM-enabled transceiver designs for ISAC systems. It begins with an overview of representative IM architectures, their unique principles, and their inherent advantages in EM wave manipulation. Next, a unified ISAC framework is established to systematically model the design and derivation of diverse IM-enabled transceiver structures. This lays the foundation for performance optimization, trade-offs, and analysis. The paper then discusses several critical technologies for IM-enabled ISAC transceivers, including dedicated channel modeling, effective channel estimation, tailored beamforming strategies, and dual-functional waveform design.

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arXiv2025

NEFT: A Unified Transformer Framework for Efficient Near-Field CSI Feedback in XL-MIMO Systems

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems, operating in the near-field region due to their massive antenna arrays, are key enablers of next-generation wireless communications but face significant challenges in channel state information (CSI) feedback. Deep learning has emerged as a powerful tool by learning compact channel features for feedback. However, existing methods struggle to capture the intricate structure of near-field CSI and incur prohibitive computational overhead on practical mobile devices. To overcome these limitations, we propose the near-field efficient feedback Transformer (NEFT) family for accurate near-field CSI feedback with reduced overhead under diverse hardware constraints. NEFT builds on a hierarchical vision Transformer backbone with progressive token reduction and multi-scale feature extraction, enabling compact and effective modeling of near-field channel characteristics. Furthermore, NEFT is extended with lightweight variants: NEFT-Compact applies multi-level knowledge distillation (KD) to reduce model complexity while preserving accuracy; NEFT-Hybrid adopts an attention-free CNN encoder to reduce encoder-side computation; and NEFT-Edge combines NEFT-Hybrid with KD to enable deployment on highly resource-constrained edge devices. Extensive simulations show that NEFT achieves a 15--21dB improvement in normalized mean-squared error over state-of-the-art methods, NEFT-Compact and NEFT-Edge reduce total FLOPs by 25-36% with negligible accuracy loss, while NEFT-Hybrid reduces encoder-side complexity by up to 64%, enabling deployment in highly asymmetric device scenarios. These results establish NEFT as a practical and scalable solution for near-field CSI feedback in XL-MIMO systems.

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