Hybrid Reconfigurable Intelligent Surfaces

• HRIS are electromagnetic metasurfaces with programmable reflection and built-in sensing capabilities for local channel estimation and environmental mapping.
• They employ tunable power splitters and analog/digital combining networks to optimize dual functions, reducing training overhead and boosting spectral efficiency.
• HRIS support secure ISAC operations by balancing communication and sensing trade-offs through optimized power-splitting and adaptive configuration techniques.

Hybrid Reconfigurable Intelligent Surfaces (HRIS) are a class of electromagnetic metasurfaces that generalize the traditional reconfigurable intelligent surface (RIS) architecture by endowing each meta-atom both with programmable reflection capability and local, low-power sensing or reception functionality. Unlike conventional RIS—which are limited to almost-passive phase and amplitude control over incident waves—an HRIS incorporates tunable power splitters and analog or digital combining networks at the elemental level. This enables simultaneous, autonomous manipulation of a fraction of the impinging wave for communication (reflected beamforming) and processing of the remaining energy for local estimation, sensing, or environmental mapping. Recent studies have shown that such dual-function metasurfaces yield substantial reductions in training overhead, improved channel estimation accuracy, self-localization, enhanced physical-layer security, and can facilitate robust, scalable integrated sensing and communications (ISAC) in next-generation wireless networks (Zhang et al., 2022, Alexandropoulos et al., 22 Jul 2025, Gavras et al., 2024, Ghazalian et al., 2024, Ghazalian et al., 2022).

1. Architectural Principles and Signal Models

HRIS architectures integrate several key components: (i) meta-atom array with phase-shifters, (ii) per-element power splitters, (iii) analog combining network (for sensing), and (iv) a digital controller for real-time reconfiguration. Each meta-atom applies a unit-modulus phase shift γ and splits the power ratio ρ between reflection and sensing. The split fraction ρ∈[0,1] is adjustable. Reflection is effected via a phase-shifter; sensing is realized by steering the fractional energy (ρ) into a combiner connected to one or more RF chains.

The signal model for an HRIS with N elements and N_r receive chains is

$$\mathbf{y}_{\text{RF}}(n) = \Psi(\boldsymbol{\rho}, \boldsymbol{\psi})\,\mathbf{r}(n), \qquad \mathbf{y}_{\text{RC}}(n) = \Phi(\boldsymbol{\rho}, \boldsymbol{\phi})\,\mathbf{r}(n),$$

where $\Psi$ is a diagonal matrix capturing the reflection coefficients, and $\Phi$ encodes the analog combining network for the sensed signals (Zhang et al., 2022, Alexandropoulos et al., 22 Jul 2025). The incident field $\mathbf{r}(n)$ is split at each element according to local power control and phase settings. The HRIS extends the RIS operation from purely reflective ($\rho=1$) to arbitrary dual-use split ($0 \leq \rho \leq 1$), supporting full-duplex mode.

2. Channel Estimation and Sensing Advantages

The ability of an HRIS to sense a portion of the incident wave enables direct acquisition of local CSI, which eliminates the dependence on cumbersome cascaded channel estimation in passive RIS deployments. For a multi-user uplink with K users, HRIS with N elements and N_r receive chains, perfect recovery of both UT→HRIS and HRIS→BS channels is possible with $\tau \geq N \max\{1, K/N_r\}$ pilot symbols in the noiseless case, as opposed to $K N M$ pilots required for cascaded estimation in passive RIS (Zhang et al., 2022, Zhang et al., 2022, Alexandropoulos et al., 22 Jul 2025). For noisy channels, closed-form expressions for the minimum mean-square error (MMSE) at the HRIS and BS are derived in terms of the analog combining matrices and pilot SNR, providing rigorous theoretical performance bounds (Zhang et al., 2022).

Sensing also enables autonomous functions: angle-of-arrival estimation, localization, and self-configuration. The HRIS can execute local MUSIC, Capon, or compressive sensing algorithms for AoA estimation with sub-degree RMSE, matching the Cramér–Rao bound across practical SNRs (Alexandropoulos et al., 2021, Gavras et al., 26 Apr 2025). Furthermore, joint user and HRIS localization (6D surface + 3D user) is possible by parameterizing the signal model in terms of unknown positions and orientations, and employing multi-stage ML or geometric solvers; fundamental performance is set by analytically derived CRLBs (Ghazalian et al., 2024, Ghazalian et al., 2022).

3. Dual-Function HRIS Design: Power-Splitting and Trade-offs

A central design dimension for HRIS is the optimal power-splitting ratio ρ, which governs the SNR in the sensing and reflected branches. The trade-off is formalized via the CRLB for the parameters of interest, or by explicit joint optimization of communication rates and sensing metrics. Increasing ρ reallocates more power to sensing, improving local CSI or localization error bounds but degrades the reflected beamforming gain and communication link quality; conversely, reducing ρ favors communications at the cost of sensing performance (Ghazalian et al., 2024, Ghazalian et al., 2022, Gavras et al., 2024, Gavras et al., 29 Apr 2025). Simulation and theoretical analysis reveal that a value of ρ≈0.5 often achieves near-optimal balance for multi-parameter estimation tasks.

The trade-off is exploited in algorithmic solutions: (i) Weighted sum-MSE minimization via automatic differentiation (Zhang et al., 2022), (ii) alternated SDP optimization for ISAC with secrecy constraints (Gavras et al., 29 Apr 2025), (iii) adaptive dynamic element mode selection (active/passive/dormant) for energy and spectral efficiency (Ratul et al., 2023), and (iv) reinforcement learning frameworks for beamforming and HRIS configuration (Tran et al., 26 Jan 2026).

4. Device-Level Implementations

Practical HRIS realization employs diverse electromagnetic and circuit technologies:

• Dual-dielectric metasurface unit cells that exploit split-ring resonators and high-ε_r dielectrics for miniaturization and orthogonal sensing–reflection support. Such designs, validated by full-wave simulations, incorporate SP4T load-tuning matrices to programmably quantize reflection phase and dual interleaved sensing arrays for channel estimation (Birari et al., 23 Jan 2025).
• Low-cost 1-bit metasurfaces leveraging PIN diodes and parallel-plate waveguides for binary beam steering and built-in AoA sensing. Phase randomization via slot coupler patterns suppresses quantization lobes, and onboard compressive sensing plus MLP algorithms support robust localization (Keshmiri et al., 7 Jul 2025).
• Substrate-integrated waveguide (SIW) architectures for simultaneous reflection–sensing, achieving high isolation (≈20 dB) and minimal reflection loss; analog combiners and partially connected networks afford scalability in large surfaces (Alexandropoulos et al., 2021, Birari et al., 23 Jan 2025).

The hybridization principle is extensible: active relay–reflecting surfaces combine passive elements with a small fraction of active (PA-fed) relays; dynamic architectures afford runtime adaptation to channel conditions and power budgets (Nguyen et al., 2021, Nguyen et al., 2021, Sankar et al., 2023, Wang et al., 2024).

5. Integrated Sensing and Communications (ISAC) and Secure Operation

The HRIS paradigm is a core enabler for next-generation ISAC frameworks, with simultaneous downlink (communication) and bistatic echo processing (sensing), supporting target localization, environmental mapping, and secure communications. Joint optimization formulations typically seek to maximize communication metrics (SINR, secrecy spectral efficiency) subject to constraints on sensing error (PEB, OEB, clock bias bounds), leading to multi-objective trade spaces (Gavras et al., 2024, Gavras et al., 29 Apr 2025, Gavras et al., 26 Apr 2025).

For secure ISAC, the HRIS is configured to guarantee a secrecy rate threshold while minimizing the position error bound of legitimate and adversarial users. The absorption ratio, surface size, array geometry, and analog combiner phases are jointly tuned in alternating fashion to maintain robustness under uncertainty while achieving desired operational objectives (Gavras et al., 29 Apr 2025).

6. Algorithmic Optimization and Scalability

State-of-the-art HRIS system design leverages scalable optimization routines:

• Automatic differentiation and first-order gradient solvers (Adam, gradient descent) directly applied to closed-form weighted sum-MSE or CRLB metrics, permitting efficient HRIS parameter selection (Zhang et al., 2022).
• SDP and semidefinite relaxations for beamformer and combiner optimization under non-convex unit-modulus/absorption constraints (Gavras et al., 2024, Gavras et al., 29 Apr 2025).
• Block coordinate descent with exact penalty methods enable binary mode and antenna selection in dynamic HRIS arrays (active/passive hybrid) (Wang et al., 2024).
• Alternating heuristics and closed-form rank-ordering rules for active/passive placement under channel-aware metrics (Sankar et al., 2023, Nguyen et al., 2021).
• Deep reinforcement learning mapping CSI directly to near-optimal HRIS configurations with sub-millisecond complexity, supporting real-time adaptation (Tran et al., 26 Jan 2026).

7. Empirical Performance and Design Guidelines

Extensive numerical results across multiple works demonstrate that HRIS architectures:

• Reduce pilot training overhead by up to 50% in multi-user channel estimation (Zhang et al., 2022, Zhang et al., 2022, Alexandropoulos et al., 22 Jul 2025).
• Achieve substantial gains in spectral and energy efficiency over conventional RIS, particularly in hybrid relay-reflecting configurations—dynamic HRIS with few active elements provides 42.8% SE and 41.8% EE improvements (Nguyen et al., 2021, Nguyen et al., 2021, Sankar et al., 2023).
• Enable sub-meter localization and sub-degree orientation estimation under moderate SNRs, tracking theoretical CRBs (Ghazalian et al., 2024, Ghazalian et al., 2022).
• Trade-off communication rate and sensing coverage, with optimal splitting ratios typically in the interval [0.3, 0.6] depending on application objectives (Gavras et al., 2024, Gavras et al., 29 Apr 2025, Gavras et al., 26 Apr 2025).
• Exhibit robust performance under practical scattering and hardware impairments, with graceful degradation and enhanced stability for carefully calibrated surfaces (Ghazalian et al., 2024, Alexandropoulos et al., 2021, Wang et al., 2024).

• Channel Estimation with Hybrid Reconfigurable Intelligent Metasurfaces (Zhang et al., 2022)
• Hybrid RISs for Simultaneous Tunable Reflections and Sensing (Alexandropoulos et al., 22 Jul 2025)
• Simultaneous Communications and Sensing with Hybrid Reconfigurable Intelligent Surfaces (Gavras et al., 2024)
• Joint 3D User and 6D Hybrid Reconfigurable Intelligent Surface Localization (Ghazalian et al., 2024)
• Joint User Localization and Location Calibration of A Hybrid Reconfigurable Intelligent Surface (Ghazalian et al., 2022)
• Dual Dielectric Metasurface for Simultaneous Sensing and Reconfigurable Reflections (Birari et al., 23 Jan 2025)
• Novel 1-bit Hybrid Reconfigurable Intelligent Surface (Keshmiri et al., 7 Jul 2025)
• Communications-Centric Secure ISAC with Hybrid Reconfigurable Intelligent Surfaces (Gavras et al., 29 Apr 2025)
• Tracking-Aided Multi-User MIMO Communications with Hybrid Reconfigurable Intelligent Surfaces (Gavras et al., 26 Apr 2025)
• Hybrid Relay-Reflecting Intelligent Surface-Assisted Wireless Communication (Nguyen et al., 2021)
• Hybrid Relay-Reflecting Intelligent Surface-Aided Wireless Communications: Opportunities, Challenges, and Future Perspectives (Nguyen et al., 2021)
• Optimal Placement of Active and Passive Elements in Hybrid RIS-assisted Communication Systems (Sankar et al., 2023)
• Robust Beamforming Design and Antenna Selection for Dynamic HRIS-aided MISO System (Wang et al., 2024)
• Adaptive Three Layer Hybrid Reconfigurable Intelligent Surface for 6G Wireless Communication: Trade-offs and Performance (Ratul et al., 2023)
• Deep Reinforcement Learning for Hybrid RIS Assisted MIMO Communications (Tran et al., 26 Jan 2026)

---

This encyclopedia entry synthesizes the current theoretical and practical research on HRIS, with reference to established literature and system models as found in contemporary arXiv works. The HRIS concept underpins a broad and growing set of methodologies in the ISAC and next-generation wireless domain, with clear implications for both academic research and applied system design.