arXiv subjects

Qingli Yan

Publications and source records attributed to Qingli Yan.

5 recordsLinked to original sources

arXiv2026

Two-Stage Refinement Sparse Channel Estimation for Reconfigurable Intelligent Metasurface Antenna (RIMSA) Massive MIMO

To meet the increasing demands for high data rates and large capacity, next generation wireless communication systems require transceivers equipped with a large number of antennas. Massive multiple-input multiple-output (MIMO) with metasurface antennas has emerged as a promising solution. In this paper, we investigate the channel estimation problem for the emerging reconfigurable intelligent metasurface antenna (RIMSA) array systems. Specifically, we develop a two-stage refinement (TSR) channel estimation method based on the compressed sensing (CS) principle. In the first stage, we exploit the antenna structure of RIMSA to receive pilots by setting identical phase response vectors across all RIMSAs. In this manner, the coherence of the measurement matrix under the CS framework is reduced and the channel estimation performance is improved. However, this special design introduces channel direction-of-arrival (DoA) estimation ambiguity and yields an ambiguous candidate DoA set. In the second stage, we optimize the phase responses of the metamaterial elements to resolve the ambiguity and accurately estimate the DoAs and channel coefficients. Overall, the estimation accuracy is improved in the first stage at the cost of ambiguity, and this ambiguity is eliminated in the second stage. We illustrate the performance advantages of the TSR method by presenting the numerical results and comparing it with the existing methods.

cs.IT↗

arXiv2026

Combating Suppressive Jamming with Dynamic Agile Reconfigurable Intelligent Surface Antenna Array (DARISAA)

Suppressive jamming is a severe challenge to digital receivers in wireless communications, since high-power jamming may cause analog-to-digital converter (ADC) overload or automatic gain control (AGC)-limited quantization blocking of weak desired signals. Analog beamforming in the spatial domain can be employed to combat suppressive jamming signals before they reach the ADC. In this paper, we introduce a comprehensive anti-jamming scheme considering both direction-of-arrival (DoA) acquisition and suppressive jamming elimination based on a Dynamic Agile Reconfigurable Intelligent Surface Antenna Array (DARISAA). DARISAA is a novel type of reconfigurable antenna array composed of many metamaterial elements, which can dynamically adapt its phase responses, enabling suppressive jamming cancellation at the antenna-end. Accurate DoA estimation schemes for jamming and desired signals are proposed based on a subspace approach by utilizing the dynamic agility of the DARISAA. By leveraging the spatial-domain preprocessing capability of DARISAA and the digital beamforming of the multi-channel receiver, an analog (antenna)-digital hybrid anti-jamming scheme is developed to effectively suppress high-power jamming and significantly improve the signal-to-interference-plus-noise ratio (SINR) even with low-resolution ADCs. Simulation results demonstrate that the proposed comprehensive anti-jamming scheme achieves significant SINR enhancement, highlighting the advantages of DARISAA-enabled spatial front-end processing for jamming suppression.

eess.SP↗

arXiv2025

LLM-RIMSA: Large Language Models driven Reconfigurable Intelligent Metasurface Antenna Systems

The evolution of 6G networks demands ultra-massive connectivity and intelligent radio environments, yet existing reconfigurable intelligent surface (RIS) technologies face critical limitations in hardware efficiency, dynamic control, and scalability. This paper introduces LLM-RIMSA, a transformative framework that integrates large language models (LLMs) with a novel reconfigurable intelligent metasurface antenna (RIMSA) architecture to address these challenges. Unlike conventional RIS designs, RIMSA employs parallel coaxial feeding and 2D metasurface integration, enabling each individual metamaterial element to independently adjust both its amplitude and phase. While traditional optimization and deep learning (DL) methods struggle with high-dimensional state spaces and prohibitive training costs for RIMSA control, LLM-RIMSA leverages pre-trained LLMs cross-modal reasoning and few-shot learning capabilities to dynamically optimize RIMSA configurations. Simulations demonstrate that LLM-RIMSA achieves state-of-the-art performance, outperforming conventional DL-based methods in sum rate while reducing training overhead. The proposed framework pave the way for LLM-driven intelligent radio environments.

eess.SP↗

arXiv2022

RIS-Assisted Green Secure Communications: Active RIS or Passive RIS?

Reconfigurable Intelligent Surface (RIS) is one of the promising techniques for 6G wireless communications, and recently has also been shown to be able to improve secure communications. However, there is a "double fading" effect in the reflection link between base station and user, thus passive RIS only achieves a negligible secrecy gain in typical communications scenarios.In this letter, we propose an active RIS-aided multi-antenna physical layer secrecy transmission scheme, where the active RIS can amplify the signal actively. Our aim is to minimize the transmit power subject to the constraint of secrecy rate. To solve the non-convex optimization problem, a penalty-based alternating minimization (AltMin) algorithm is proposed to optimize both the beamformer at the transmitter and the reflection matrix at RIS. Simulation results show that active RIS can resist the impact of "double fading" effect effectively, and is more energy efficient than passive RIS.

cs.IT↗

arXiv2022

SSDPT: Self-Supervised Dual-Path Transformer for Anomalous Sound Detection in Machine Condition Monitoring

Anomalous sound detection for machine condition monitoring has great potential in the development of Industry 4.0. However, these anomalous sounds of machines are usually unavailable in normal conditions. Therefore, the models employed have to learn acoustic representations with normal sounds for training, and detect anomalous sounds while testing. In this article, we propose a self-supervised dual-path Transformer (SSDPT) network to detect anomalous sounds in machine monitoring. The SSDPT network splits the acoustic features into segments and employs several DPT blocks for time and frequency modeling. DPT blocks use attention modules to alternately model the interactive information about the frequency and temporal components of the segmented acoustic features. To address the problem of lack of anomalous sound, we adopt a self-supervised learning approach to train the network with normal sound. Specifically, this approach randomly masks and reconstructs the acoustic features, and jointly classifies machine identity information to improve the performance of anomalous sound detection. We evaluated our method on the DCASE2021 task2 dataset. The experimental results show that the SSDPT network achieves a significant increase in the harmonic mean AUC score, in comparison to present state-of-the-art methods of anomalous sound detection.

eess.AS↗