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Parametric Channel Estimation and Design for Active-RIS-Assisted Communications
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DOI：10.1109/lcomm.2026.3711925.png)
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Abstract
En 中文
Reconfigurable Intelligent Surface (RIS) technology has emerged as a key enabler for future wireless communications. However, its potential is constrained by the difficulty of acquiring accurate user-to-RIS channel state information (CSI), due to the cascaded channel structure and the high pilot overhead of non-parametric methods. While both passive and active RISs (ARISs) suffer from severe multiplicative path loss, an ARIS directly compensates for this attenuation by amplifying the reflected signal, thus improving its practicality in real deployments. In this letter, we propose a parametric channel estimation method tailored for ARISs. The proposed approach integrates an ARIS model with an adaptive Maximum Likelihood Estimator (MLE) to recover the main channel parameters using a minimal number of pilots. To further enhance performance, an adaptive ARIS configuration strategy is employed, which refines the beam direction based on an initial user location estimate. Moreover, an orthogonal angle-pair codebook is used instead of the conventional Discrete Fourier Transform (DFT) codebook, significantly reducing the codebook size and ensuring reliable operation for both far-field (FF) and near-field (NF) users. Extensive simulations demonstrate that the proposed method achieves near-optimal performance with very few pilots compared to non-parametric approaches. Its performance is also benchmarked against that of a traditional passive RIS under the same total power budget to ensure fairness. Results show that the ARIS yields higher spectral efficiency (SE) by mitigating the multiplicative fading inherent in passive RISs and allocating more resources to data transmission.
Keywords:
Active reconfigurable intelligent surface
channel estimation
MLE
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