arXiv subjects

Lingsheng Meng

Publications and source records attributed to Lingsheng Meng.

10 recordsLinked to original sources

arXiv2026

Enabling Flexible AFDM-ISAC Design: When Ambiguity Shaping Meets PAPR Control

Affine frequency division multiplexing (AFDM) has emerged as an enabling waveform for integrated sensing and communication (ISAC) due to its intrinsic chirp signaling nature. Nevertheless, the practicality of AFDM-ISAC systems needs to address two major technical challenges, i.e., high ambiguity function (AF) sidelobes and high peak-to-average power ratio (PAPR). By exploiting the reserved chirp-subcarrier (RCS) symbols and per-subcarrier pre-chirp parameters, we develop a flexible and unified AFDM waveform design framework for AF shaping and PAPR control. The proposed framework supports three tunable modes: AF shaping via weighted integrated sidelobe level (ISL) minimization, PAPR minimization, and joint AF shaping and PAPR control under a prescribed PAPR constraint. To solve the formulated nonconvex problem and to accommodate the discrete-phase constraints on the pre-chirp parameters, a joint ISL-PAPR discrete-phase majorization-minimization (JIPD-MM) algorithm is developed. Simulation results verify the effectiveness of the proposed framework under all the three design modes, with the benchmark comparisons conducted at similar effective spectral efficiencies. It is shown that the resulting weighted-ISL and PAPR gains lead to improved weak-target detectability in multi-target scenarios and lower bit error rate (BER) under power-amplifier (PA) nonlinearity.

eess.SP↗

arXiv2026

Data-aided Channel Estimation and Sensing With Sparse Bayesian Learning for AFDM-ISAC System

Affine frequency division multiplexing (AFDM) has emerged as a promising waveform for next-generation integrated sensing and communication (ISAC) systems. However, it becomes challenging to improve spectral efficiency while simultaneously obtaining accurate channel and sensing-related parameters, particularly in doubly-dispersive channels with fractional delays and fractional Doppler shifts. To tackle this challenge, by formulating the channel estimation task as a multiple measurement vector (MMV) off-grid sparse recovery problem, we propose a data-aided grid-evolution sparse Bayesian learning (D-GESBL) scheme for channel estimation and sensing under a superimposed pilot framework. Specifically, we develop an efficient data-aided iterative receiver, in which reliably decoded data symbols are fed back as additional pseudo-pilot information to assist channel estimation and sensing. To mitigate off-grid mismatch and improve the overall estimation accuracy, we develop a grid evolution procedure that iteratively adjusts the virtual grids in the discrete affine Fourier (DAF) domain according to the estimated off-grid components. Furthermore, by integrating the generalized approximate message passing (GAMP) algorithm into the proposed SBL framework, we also develop a low-complexity data-aided GAMP-based grid-evolution SBL (D-GAMP-GESBL) algorithm. Finally, the numerical results validate the effectiveness of our proposed schemes and demonstrate their superiority over existing state-of-the-art methods.

eess.SP↗

arXiv2026

Directional AI Advice: Experimental Evidence from Healthcare

Generative AI is fast becoming the first place people turn for expert advice. The advice it provides can be directional rather than neutral, shaped in part by the choices of its designers and regulators. When clients consult AI before meeting an expert, they carry this directional advice into a relationship that once rested on the expert's judgment alone. We study its consequences in healthcare through a large-scale preregistered field experiment at a Chinese hospital, where we randomize patients' access to an AI chatbot before their outpatient visit. Examination of the conversation logs shows that the chatbot routinely cautions against the use of medications, especially Traditional Chinese Medicine and antibiotics, while issuing clean recommendations for diagnostic testing, consistent with the liability-driven guardrails encoded in AI training. This directionality propagates into clinical practice. Prescription rates decline among treated patients while diagnostic testing increases, and these effects are more pronounced among physicians who are receptive to patient input and those with more intensive prescribing styles. Beyond shifting healthcare utilization, survey results show that AI access reduces patient compliance and satisfaction, shifting the balance of authority between patients and physicians.

econ.GN↗

arXiv2026

The double splitting iteration method for solving the large indefinite least squares problem

Addressing large-scale indefinite least squares (ILS) problem poses notable computational bottlenecks in the field of numerical linear algebra. State-of-the-art iterative schemes for such problems are predominantly constructed upon the single splitting of the coefficient matrix derived from the corresponding normal equation. In this work, we put forward an innovative iterative framework grounded in the double splitting of normal equations tailored for ILS problem. Specifically, we elaborate on a distinct implementations of the double splitting strategy, which offer constructive insights and methodological references for subsequent research on double splitting-based iterative methods. Two numerical experiments further corroborate that the proposed double splitting iterative paradigm outperforms conventional single splitting approaches in both computational efficiency and convergence robustness.

math.NA↗

arXiv2025

Design of a bioinspired robophysical antenna for insect-scale tactile perception and navigation

The American cockroach (Periplaneta americana) uses its soft antennae to guide decision making by extracting rich tactile information from tens of thousands of distributed mechanosensors. Although tactile sensors enable robust, autonomous perception and navigation in natural systems, replicating these capabilities in insect-scale robots remains challenging due to stringent size, weight, and power constraints that limit existing sensor technologies. To overcome these limitations, we introduce CITRAS (Cockroach Inspired Tactile Robotic Antenna Sensor), a bioinspired, multi-segmented, compliant laminate sensor with embedded capacitive angle sensors. CITRAS is compact (73.7x15.6x2.1 mm), lightweight (491 mg), and low-power (32 mW), enabling seamless integration with miniature robotic platforms. The segmented compliant structure passively bends in response to environmental stimuli, achieving accurate hinge angle measurements with maximum errors of just 0.79 degree (quasistatic bending) and 3.58 degree (dynamic bending). Experimental evaluations demonstrate CITRAS' multifunctional tactile perception capabilities: predicting base-to-tip distances with 7.75 % error, estimating environmental gap widths with 6.73 % error, and distinguishing surface textures through differential sensor response. The future integration of this bioinspired tactile antenna in insect-scale robots addresses critical sensing gaps, promising enhanced autonomous exploration, obstacle avoidance, and environmental mapping in complex, confined environments.

cs.RO↗

arXiv2025

Local Ambiguity Shaping for Doppler-Resilient Sequences Under Spectral and PAPR Constraints

This paper focuses on designing Doppler-resilient sequences with low local Ambiguity Function (AF) sidelobes, subject to certain spectral and Peak-to-Average Power Ratio (PAPR) constraints. To achieve this, we propose two distinctoptimization algorithms: (i) an Alternating Minimization (AM) algorithm for superior Weighted Peak Sidelobe Level (WPSL) minimization, and (ii) a low-complexity Augmented Lagrangian-assisted Majorization Minimization (ALaMM) algorithm with effective WPSL suppression. The proposed schemes hold great potential for sequence design in future 6G and integrated sensing and communication applications, supporting robust sensing under spectral coexistence constraints in high-mobility scenarios.

eess.SP↗

arXiv2025

The GMRES method for solving the large indefinite least squares problem via an accelerated preconditioner

In this research, to solve the large indefinite least squares problem, we firstly transform its normal equation into a sparse block three-by-three linear systems, then use GMRES method with an accelerated preconditioner to solve it. The construction idea of the preconditioner comes from the thought of Luo et.al [Luo, WH., Gu, XM., Carpentieri, B., BIT 62, 1983-2004(2022)], and the advantage of this is that the preconditioner is closer to the coefficient matrix of the block three-by-three linear systems when the parameter approachs zero. Theoretically, the iteration method under the preconditioner satisfies the conditional convergence, and all eigenvalues of the preconditioned matrix are real numbers and gathered at point $(1,0)$ as parameter is close to $0$. In the end, numerical results reflect that the theoretical results is correct and the proposed preconditioner is effective by comparing with serval existing preconditioners.

math.NA↗

arXiv2025

Generalized Arlery-Tan-Rabaste-Levenshtein Lower Bounds on Ambiguity Function and Their Asymptotic Achievability

This paper presents generalized Arlery-Tan-Rabaste-Levenshtein lower bounds on the maximum aperiodic ambiguity function (AF) magnitude of unimodular sequences under certain delay-Doppler low ambiguity zones (LAZ). Our core idea is to explore the upper and lower bounds on the Frobenius norm of the weighted auto- and cross-AF matrices by introducing two weight vectors associated with the delay and Doppler shifts, respectively. As a second major contribution, we demonstrate that our derived lower bounds are asymptotically achievable with selected Chu sequence sets by analyzing their maximum auto- and cross- AF magnitudes within certain LAZ.

cs.IT↗

arXiv2025

Flag Sequence Set Design for Low-Complexity Delay-Doppler Estimation

This paper studies Flag sequences for low-complexity delay-Doppler estimation by exploiting their distinctive peak-curtain ambiguity functions (AFs). Unlike the existing Flag sequence designs that are limited to prime lengths and periodic auto-AFs, we aim to design Flag sequence sets of arbitrary lengths with low (nontrivial) periodic/aperiodic auto- and cross-AFs. Since every Flag sequence consists of a Curtain sequence and a Peak sequence, we first investigate the algebraic design of Curtain sequence sets of arbitrary lengths. Our proposed design gives rise to novel Curtain sequence sets with ideal curtain auto-AFs and zero/near-zero cross-AFs within the delay-Doppler zone of operation. Leveraging these Curtain sequence sets, two optimization problems are formulated to minimize the weighted integrated masked sidelobe level (WImSL) of the Flag sequence set. Accelerated parallel partially majorization-minimization algorithms are proposed to jointly optimize the transmit Flag sequences and symmetric/asymmetric reference sequences stored in the receiver. Simulations demonstrate that our proposed Flag sequences lead to improved WImSL and peak-to-max-masked-sidelobe ratio compared with the existing Flag sequences. Additionally, our Flag sequences under the Flag method exhibit Mean Squared Errors that approach the Cramér-Rao lower bound and the sampling bound at high signal-to-noise power ratios.

cs.IT↗

arXiv2024

Linear Precoding Design for OTFS Systems in Time/Frequency Selective Fading Channels

Even orthogonal time frequency space (OTFS) has been shown as a promising modulation scheme for high mobility doubly-selective fading channels, its attainability of full diversity order in either time or frequency selective fading channels has not been clarified. By performing pairwise error probability (PEP) analysis, we observe that the original OTFS system can not always guarantee full exploitation of the embedded diversity in either time or frequency selective fading channels. To address this issue and further improve system performance, this work proposes linear precoding solutions based on algebraic number theory for OTFS systems over time and frequency selective fading channels, respectively. The proposed linear precoded OTFS systems can guarantee the maximal diversity and potential coding gains in time/frequency selective fading channels without any transmission rate loss and do not require the channel state information (CSI) at the transmitter. Simulation results are finally provided to illustrate the superiority of our proposed precoded OTFS over both the original unprecoded and the existing phase rotation OTFS systems in time/frequency selective fading channels.

eess.SP↗