---
title: Hybrid Reconfigurable Intelligent Surfaces
url: https://www.emergentmind.com/topics/hybrid-reconfigurable-intelligent-surfaces-hris
type: topic
---

# Hybrid Reconfigurable Intelligent Surfaces

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 [2206.03913][2507.16550][2410.15836][2401.03852][2210.10150].

## 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 [2206.03913][2507.16550]. 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 [2206.03913][2202.05673][2507.16550]. 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 [2206.03913].

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 [2104.04690][2504.18846]. 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 [2401.03852][2210.10150].

## 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 [2401.03852][2210.10150][2410.15836][2504.20608]. 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 [2206.03913], (ii) alternated SDP optimization for ISAC with secrecy constraints [2504.20608], (iii) adaptive dynamic element mode selection (active/passive/dormant) for energy and spectral efficiency [2309.14087], and (iv) reinforcement learning frameworks for beamforming and HRIS configuration [2601.18453].

## 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 [2501.14042].
- **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 [2507.04675].
- **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 [2104.04690][2501.14042].

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 [2103.03900][2104.02039][2301.06725][2404.00598].

## 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 [2410.15836][2504.20608][2504.18846].

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 [2504.20608].

## 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 [2206.03913].
- **SDP and semidefinite relaxations** for beamformer and combiner optimization under non-convex unit-modulus/absorption constraints [2410.15836][2504.20608].
- **Block coordinate descent with exact penalty methods** enable binary mode and antenna selection in dynamic HRIS arrays (active/passive hybrid) [2404.00598].
- **Alternating heuristics and closed-form rank-ordering rules** for active/passive placement under channel-aware metrics [2301.06725][2104.02039].
- **Deep reinforcement learning** mapping CSI directly to near-optimal HRIS configurations with sub-millisecond complexity, supporting real-time adaptation [2601.18453].

## 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 [2206.03913][2202.05673][2507.16550].
- **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 [2104.02039][2103.03900][2301.06725].
- **Enable sub-meter localization and sub-degree orientation estimation** under moderate SNRs, tracking theoretical CRBs [2401.03852][2210.10150].
- **Trade-off communication rate and sensing coverage**, with optimal splitting ratios typically in the interval [0.3, 0.6] depending on application objectives [2410.15836][2504.20608][2504.18846].
- **Exhibit robust performance under practical scattering and hardware impairments**, with graceful degradation and enhanced stability for carefully calibrated surfaces [2401.03852][2104.04690][2404.00598].

## References

- Channel Estimation with Hybrid Reconfigurable Intelligent Metasurfaces [2206.03913]
- Hybrid RISs for Simultaneous Tunable Reflections and Sensing [2507.16550]
- Simultaneous Communications and Sensing with Hybrid Reconfigurable Intelligent Surfaces [2410.15836]
- Joint 3D User and 6D Hybrid Reconfigurable Intelligent Surface Localization [2401.03852]
- Joint User Localization and Location Calibration of A Hybrid Reconfigurable Intelligent Surface [2210.10150]
- Dual Dielectric Metasurface for Simultaneous Sensing and Reconfigurable Reflections [2501.14042]
- Novel 1-bit Hybrid Reconfigurable Intelligent Surface [2507.04675]
- Communications-Centric Secure ISAC with Hybrid Reconfigurable Intelligent Surfaces [2504.20608]
- Tracking-Aided Multi-User MIMO Communications with Hybrid Reconfigurable Intelligent Surfaces [2504.18846]
- Hybrid Relay-Reflecting Intelligent Surface-Assisted Wireless Communication [2103.03900]
- Hybrid Relay-Reflecting Intelligent Surface-Aided Wireless Communications: Opportunities, Challenges, and Future Perspectives [2104.02039]
- Optimal Placement of Active and Passive Elements in Hybrid RIS-assisted Communication Systems [2301.06725]
- Robust Beamforming Design and Antenna Selection for Dynamic HRIS-aided MISO System [2404.00598]
- Adaptive Three Layer Hybrid Reconfigurable Intelligent Surface for 6G Wireless Communication: Trade-offs and Performance [2309.14087]
- Deep Reinforcement Learning for Hybrid RIS Assisted MIMO Communications [2601.18453]

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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.

Source: https://www.emergentmind.com/topics/hybrid-reconfigurable-intelligent-surfaces-hris