Parametric Channel Estimation and Design for Active-RIS-Assisted Communications

TL;DR

This work addresses the challenge of acquiring accurate user-to-RIS CSI in RIS-aided communications by developing a parametric maximum likelihood estimator tailored for active RIS (ARIS) hardware. The method jointly estimates the ARIS channel gain $\beta$, phase $\omega$, and array-geometry parameters $\psi$ under near- and far-field conditions, with closed-form updates for $\hat{\omega}$ and $\hat{\beta}$ and a targeted search for $\hat{\psi}$; an adaptive training loop then refines the RIS configuration to concentrate pilots in informative directions. A phase-alignment strategy $\bar{\boldsymbol{\theta}}^{opt}=\exp(-j\angle(D_h \hat{g}))$ and an AMP-based amplitude shaping rule $p_n^{(est)}=C\alpha_n/(\beta_n+\gamma_n)$ are integrated to maximize coherent combining under a power constraint, followed by iterative pilot refinement using a reduced, nearly-orthogonal codebook of phase beams. Simulation results at 28 GHz show that ARIS with adaptive broad-beam initialization achieves near-optimal performance with only 7–8 pilots, significantly outperforming a passive RIS under the same total power budget in both near- and far-field scenarios, and demonstrates improved NMSE and spectral efficiency. These findings indicate that ARIS can markedly boost SE and energy efficiency in practical 6G deployments, though hardware impairments and calibration remain important considerations for real-world adoption.

Abstract

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. Unlike a passive RIS, where the reflected signal suffers from multiplicative path loss, an active RIS amplifies the signal, improving its practicality in real deployments. In this letter, we propose a parametric channel estimation method tailored for active RISs. The proposed approach integrates an active RIS 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 active RIS 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 and near-field 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 active RIS yields higher spectral efficiency (SE) by eliminating the multiplicative fading inherent in passive RISs and allocating more resources to data transmission

Figures (5)

• Figure 1: NMSE versus the pilot length when the user is at a random location in the near-field of the active RIS.
• Figure 2: The average Achievable Rate versus the pilot length when the user is at a random location in the near-field of the active RIS.
• Figure 3: NMSE versus the pilot length when the user is at a random location in the far-field of the active RIS.
• Figure 4: The average Achievable Rate versus the pilot length when the user is at a random location in the far-field of the active RIS.
• Figure 5: Achievable Rate versus the percentage of total power given to active RIS for three different total powers.