arXiv subjects

Haiyao Yu

Publications and source records attributed to Haiyao Yu.

6 recordsLinked to original sources

arXiv2026

Semantic-Assisted Joint Channel Estimation and Symbol Detection for MU-MIMO Systems

This paper presents a semantic-assisted joint channel estimation and symbol detection (Sem-JCED) receiver framework for text transmission in multi-user multiple-input multiple-output (MU-MIMO) systems. Sem-JCED integrates a linear minimum mean-square error (LMMSE) channel estimator, an expectation propagation (EP) symbol detector, and a semantic corrector based on ByT5, a byte-level language model. At each outer iteration, LMMSE estimates the channel from known pilots and data symbol feedback, and EP detects the data symbols. ByT5 then maps the data symbol probabilities to byte probabilities, which provide soft symbol statistics and semantic priors for feedback to LMMSE and EP, respectively, in the next iteration. Simulation results show that Sem-JCED requires about 7~dB lower signal-to-noise ratio (SNR) than conventional JCED to reach a bit error rate (BER) of $1\times10^{-2}$ with four users and four receive antennas, and outperforms maximum-likelihood detection with perfect channel knowledge over the simulated SNR range.

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arXiv2026

Self-Supervised Passive WiFi CSI-Based Motion Tracking with Physics-Informed Deep SSM

Inferring target position from WiFi channel state information (CSI) is challenging due to the noisy and heterogeneous nature of CSI-derived observations. Measurements such as angle, delay, and Doppler provide only imperfect information about target position and velocity, particularly in dynamic multipath environments where observation reliability varies. Motivated by this challenge, this paper proposes a physics-informed Bayesian state space framework for passive CSI-based tracking. We introduce a context-conditioned deep state space model (DSSM) that captures temporal motion and environmental dynamics, while adaptively modeling time-varying observation uncertainties. By incorporating geometric constraints, this framework enables heterogeneous CSI measurement modalities to jointly regularize the consistency and confidence of the tracking process. Experimental results demonstrate that the proposed framework yields smoother trajectory estimates with reduced temporal fluctuations and outperforms existing methods in tracking accuracy.

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arXiv2026

Cross-Band Movable-Antenna Communications under Frequency-Dependent Multipath

Movable antennas (MAs) provide additional spatial degrees of freedom by adapting their physical positions to the propagation environment. However, existing studies mainly focus on single-band or contiguous wideband transmission, leaving the cross-band spatial behavior of MAs under frequency-dependent multipath propagation largely unexplored. To address this gap, we investigate a downlink multi-band MA communication system where a common physical MA configuration is shared across multiple communication bands. Such sharing induces an inherent configuration conflict, as different bands often favor substantially divergent MA configurations. We first develop a frequency-dependent multipath channel model with shared propagation geometry but band-dependent multipath component powers and phases. Using this model, we formulate a sum-rate maximization problem by jointly optimizing user-band assignment, MA positioning, and transmit precoding. We further establish a cross-band spatial scaling relationship for the normalized channel power and derive both a position-independent channel gain upper bound and a sum-rate upper bound. Based on these results, we propose a cross-band analysis-guided optimization (CAGO) algorithm and a cross-band graph transformer (CBGT) to reduce online optimization complexity. Numerical results validate the derived cross-band spatial relationship and show that CAGO consistently outperforms the narrowband, wideband, and fixed-antenna benchmarks while remaining close to the idealized independent-MA benchmark. Additionally, CBGT with online refinement achieves near-CAGO sum-rate performance with substantially lower online computational cost, providing a practical framework for exploiting the spatial reconfiguration capability of MAs in multi-band communication systems.

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arXiv2026

Cross-View Vision-Aided Proactive BS Selection and Beam Prediction for mmWave V2I Communications

This paper investigates environmental-sensing-aided proactive base station (BS) selection and beam prediction for millimeter-wave (mmWave) vehicle-to-infrastructure (V2I) wireless systems. We exploit onboard panoramic street-view images and a preloaded satellite map to predict communication-relevant environmental information around the vehicle, including nearby building footprints and heights. The predicted height map provides a compact environmental prior and is combined with historical mobility information to jointly predict the next-slot line-of-sight (LoS) state, transmission rate, and transmit and receive beam selections. On our dataset covering different real-world regions across New South Wales, Australia, the proposed framework achieves 91.4% LoS classification accuracy, 0.638 bps/Hz mean absolute error of data rate prediction, and more than 40% higher transmission rate than the conventional reactive baseline in geographically unseen regions, outperforming all evaluated deployable learning-based baselines. The dataset and code will be released at https://github.com/Huzijiao/Cross-view_V2I

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arXiv2026

Neural Network-Based Delay-Doppler-Assisted Channel Estimation for OFDM

Conventional orthogonal frequency division multiplexing (OFDM) channel estimation relies on single-tap estimation and time-frequency (TF) interpolation, which becomes unreliable in high-mobility channels because Doppler-induced inter-carrier interference (ICI) invalidates the underlying element-wise TF model. This paper proposes a neural-network-based delay-Doppler (DD)-assisted channel estimation framework for OFDM over doubly selective channels. We first derive an ICI-aware TF domain input-output relation and formulate channel estimation as a DD recovery problem. Unlike conventional sparse recovery approaches, the proposed framework does not require the equivalent DD domain channel vector to be strictly sparse, thereby accommodating the leakage induced by fractional delay and Doppler shifts. Since the data symbols are unknown during channel estimation, the sensing matrix is constructed using only the known pilot symbols. As a result, data-induced interference is not explicitly modeled, leading to a structured mismatch in the pilot observations. To tackle this challenge, the adopted network iteratively exchanges observation- and channel-domain features through the sensing matrix to learn the mapping from these contaminated observations to the equivalent DD domain channel, which is subsequently used to reconstruct the TF-domain channel. Simulation results show that the proposed method achieves lower normalized mean-square error and bit-error rate than conventional OFDM estimators.

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arXiv2024

Floor-Plan-aided Indoor Localization: Zero-Shot Learning Framework, Data Sets, and Prototype

Machine learning has been considered a promising approach for indoor localization. Nevertheless, the sample efficiency, scalability, and generalization ability remain open issues of implementing learning-based algorithms in practical systems. In this paper, we establish a zero-shot learning framework that does not need real-world measurements in a new communication environment. Specifically, a graph neural network that is scalable to the number of access points (APs) and mobile devices (MDs) is used for obtaining coarse locations of MDs. Based on the coarse locations, the floor-plan image between an MD and an AP is exploited to improve localization accuracy in a floor-plan-aided deep neural network. To further improve the generalization ability, we develop a synthetic data generator that provides synthetic data samples in different scenarios, where real-world samples are not available. We implement the framework in a prototype that estimates the locations of MDs. Experimental results show that our zero-shot learning method can reduce localization errors by around $30$\% to $55$\% compared with three baselines from the existing literature.

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