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Aug 2026

Multi-Hop RIS ISAC for Target Positioning: A Tensor Decomposition-Based Approach

Reconfigurable intelligent surface (RIS) has demonstrated remarkable potential to enhance the performance of integrated sensing and communication (ISAC), particularly when the line-of-sight (LoS) paths are obstructed. By controlling the reconfigurable elements on the surface, RIS can establish virtual LoS paths and pro...

Yi-Rui Luo, Xiaoyan Ma, Yong-Liang Guan et al. · 0 citations
Preprint Jul 2026

CSI RefineNet: A Soft Data-Aided Iterative Receiver for High-Mobility OFDM Systems

In high-mobility orthogonal frequency division multiplexing (OFDM) systems, rapid channel variation can make the channel state information (CSI) estimated from pilots inaccurate for data subcarriers, leading to a mismatch with their effective channel. To address this issue, this paper proposes a CSI RefineNet receiver,...

Yirui Luo, Yihang Xie, Yao Ge et al. · 0 citations
Jul 2026

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...

Yi-Rui Luo, Yong-Liang Guan, Yao Ge et al. · 0 citations
Preprint Aug 2026

Structured-Sparsity-Aware Joint User Activity Detection and Channel Estimation for OTFS-Based Grant-Free Random Access

Grant-free random access (GFRA) is a promising solution for massive machine-type communications (mMTC) in future wireless networks. However, reliable user activity detection and channel estimation are critical challenges, particularly when orthogonal time-frequency space (OTFS) modulation is integrated with GFRA to add...

Yao Ge, Yirui Luo, Yuhao Chi et al. · 0 citations
#reinforcement learning Review Open access Aug 2026

Bringing Reinforcement Learning to Multi-Period Financial Planning: A Bridge Between Learning-Enabled and Stochastic Optimization

This survey traces the mathematical evolution of multi-period financial planning over the past several decades, focusing on selected key methods ranging from classical stochastic optimization to learning-enabled decision systems.

Yi-Rui Luo, John M. Mulvey · 0 citations

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