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arxiv: 2507.00714 · v3 · submitted 2025-07-01 · 📡 eess.SP

Physical Layer Group Key Generation With the Aid of Reconfigurable Intelligent Surfaces

Vahid Shahiri , Guyue Li , Hamid Behroozi This is my paper

Pith reviewed 2026-05-19 06:50 UTC · model grok-4.3

classification 📡 eess.SP
keywords reconfigurable intelligent surfacesgroup key generationphysical layer securityactive RISpassive RISchannel alignmentkey generation rate
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The pith

Reconfigurable intelligent surfaces align user channels to extract common group secret keys using existing channel data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that a reconfigurable intelligent surface can be tuned so the reflected paths from several user terminals become similar enough for those terminals to pull identical secret key bits directly from the wireless channel. Tuning relies on channel information the surface already holds, so no new probe signals are needed between the users or from the network. Both passive and active surfaces are tested; the active version produces markedly lower channel mismatch and more than four times the key generation rate when total power is held constant. The approach succeeds even when the physical environment stays fixed, a setting where most earlier physical-layer key methods produce almost no usable randomness.

Core claim

The surface phase shifts are chosen to minimize the differences among the aggregate reflected channels seen by the user terminals, allowing the terminals to quantize those channels into matching key sequences. The design uses already-acquired CSI and solves the resulting optimization problems with successive convex approximation and semidefinite relaxation plus Gaussian randomization. Numerical evaluation shows that an active RIS reduces normalized mean squared error and key error rate while delivering more than four times the key generation rate of a passive RIS under the same power budget, and the generated keys pass standard randomness tests.

What carries the argument

RIS phase-shift optimization that equalizes aggregate reflecting channels across users using pre-acquired CSI

If this is right

  • High key generation rates become possible even when the wireless environment is static.
  • Active RIS outperforms passive RIS by more than a factor of four in key generation rate for the same power budget.
  • No additional probing signals are required from the user terminals or the network.
  • Performance can be quantified with normalized mean squared error, key error rate, key generation rate, and randomness metrics.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method could reduce reliance on centralized key servers in large IoT deployments by letting devices derive keys locally from the radio channel.
  • Updating the surface configuration periodically might extend the scheme to slowly moving users without losing the static-environment advantage.
  • The same surface could potentially handle both communication beamforming and key alignment in the same time slot.

Load-bearing premise

The reconfigurable intelligent surface already holds accurate channel state information for every user terminal and can apply that information to set its phase shifts without any extra measurements or signaling.

What would settle it

Deploy a physical active RIS and several user devices in a static indoor environment, optimize the phase shifts from measured CSI, then check whether the observed key error rate falls and the key generation rate rises by the factor reported in the simulations.

Figures

Figures reproduced from arXiv: 2507.00714 by Guyue Li, Hamid Behroozi, Vahid Shahiri.

Figure 1
Figure 1. Figure 1: System model: Alice acting as an AP assists [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: NMSE of the proposed GKG method versus group size for [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: KER of the proposed GKG method versus available power [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: KER of the proposed GKG method versus number of the UTs [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: KGR of the proposed GKG method versus available power [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The performance of Eve for PRIS and ARIS. [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
read the original abstract

Reconfigurable intelligent surfaces (RIS) have the ability to alter the wireless environment by making changes in the impinging signal. While RIS has been extensively studied for enhancing wireless communications, its potential for facilitating group key generation (GKG) remains unexplored. In this study, we exploit the RIS to make the aggregate reflecting channels of different user terminals (UTs) as similar as possible to be able to extract common group secret keys from their channels. Specifically, the RIS will adjust its parameters to pave the way for GKG based on the physical channels of the UTs. Our method exploits the already gathered channel state information (CSI) in the RIS to beneficially design the phase shifts and does not impose additional probing burden on the network. We consider both passive RIS (PRIS) and active RIS (ARIS) to generate the group keys. The PRIS is widely adopted in physical layer key generation (PLKG) studies due to its use of passive elements, whereas the ARIS demonstrates superior capability in aligning the aggregate reflected channels among nodes in the GKG scenario, as demonstrated in this study. We will exploit various optimization methods like successive convex approximation (SCA) and semidefinite relaxation with Gaussian randomization (SDR-GR) to address the raised optimization problems. Unlike most of the studies in the literature, our scheme can achieve a high GKG rate in static environments as well. Finally, we will examine the performance of the proposed method by normalized mean squared error (NMSE), key error rate (KER), key generation rate (KGR) and key randomness metrics. Our numerical results verify that for the equal available power budget, the ARIS significantly outperforms PRIS in NMSE and KER, achieving more than four times higher KGR.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes using reconfigurable intelligent surfaces (both passive PRIS and active ARIS) to facilitate physical-layer group key generation among multiple user terminals. Phase shifts are designed via optimization (SCA and SDR-GR) on already-available CSI to align aggregate reflected channels, enabling extraction of common group keys without extra probing. Simulations compare NMSE, KER, KGR and randomness, claiming ARIS yields more than 4x higher KGR than PRIS under equal power budgets and works in static environments.

Significance. If the central claims hold, the work is significant for extending RIS to physical-layer security in static multi-user settings where conventional PLKG fails due to insufficient channel variation. The explicit PRIS-vs-ARIS comparison under power constraints and the use of existing CSI (avoiding extra overhead) are practical strengths; reproducible optimization formulations and numerical verification would strengthen impact.

major comments (2)
  1. [System Model / Problem Formulation] System model and problem formulation: the central claim that the scheme 'exploits the already gathered CSI in the RIS ... and does not impose additional probing burden' is load-bearing for the equal-power-budget ARIS-vs-PRIS comparison and the >4x KGR gain. No protocol, pilot overhead analysis, or power/time cost accounting is provided for acquiring accurate multi-UT CSI at the RIS, leaving the reported advantages conditional on an idealized zero-cost CSI regime.
  2. [Numerical Results] Numerical results section: performance claims (NMSE, KER, KGR) rest on SCA and SDR-GR solutions, yet no simulation parameters, convergence tolerances, stopping criteria, error bars, or verification that the relaxed solutions satisfy the original non-convex constraints are supplied. This undermines assessment of whether the reported gains are robust or scenario-specific.
minor comments (2)
  1. [Abstract] Abstract lacks any mention of simulation parameters, number of UTs, RIS size, or SNR ranges, which are standard for reproducibility in eess.SP papers.
  2. [Optimization Problems] Notation for aggregate reflected channels and the exact objective functions for PRIS versus ARIS should be stated explicitly with equation numbers for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help improve the clarity and rigor of our work. We address each major comment point by point below, indicating the revisions we will incorporate.

read point-by-point responses

  1. Referee: System model and problem formulation: the central claim that the scheme 'exploits the already gathered CSI in the RIS ... and does not impose additional probing burden' is load-bearing for the equal-power-budget ARIS-vs-PRIS comparison and the >4x KGR gain. No protocol, pilot overhead analysis, or power/time cost accounting is provided for acquiring accurate multi-UT CSI at the RIS, leaving the reported advantages conditional on an idealized zero-cost CSI regime.

    Authors: We acknowledge that the manuscript assumes CSI availability at the RIS as is standard in RIS literature for communication tasks, without providing an explicit overhead analysis for multi-UT estimation. This assumption underpins the claim of no additional probing specifically for key generation. We agree this point merits clarification. In the revised manuscript, we will add a dedicated paragraph in the system model section describing a standard TDD-based CSI acquisition protocol (with pilot symbols from the UTs to the RIS), its associated time and power overhead, and how it is shared with existing communication functions rather than being unique to the proposed scheme. This will strengthen the equal-power-budget comparison. revision: yes

  2. Referee: Numerical results section: performance claims (NMSE, KER, KGR) rest on SCA and SDR-GR solutions, yet no simulation parameters, convergence tolerances, stopping criteria, error bars, or verification that the relaxed solutions satisfy the original non-convex constraints are supplied. This undermines assessment of whether the reported gains are robust or scenario-specific.

    Authors: We agree that the current numerical results section lacks sufficient implementation details for reproducibility. In the revised manuscript, we will expand the simulation setup subsection to include all parameter values (e.g., number of RIS elements, UT locations, noise variances, power budgets), SCA convergence tolerance (set to 10^{-4}), SDR-GR randomization trials (1000), stopping criteria, and explicit verification that the obtained solutions satisfy the original unit-modulus and power constraints within a small tolerance. We will also add error bars to the KGR and KER plots based on 1000 Monte Carlo runs and include a brief robustness discussion across static and low-mobility scenarios. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected; derivation relies on external CSI assumption and numerical optimization

full rationale

The paper's core approach optimizes RIS phase shifts to align aggregate reflected channels for group key generation, using SCA and SDR-GR solvers on the assumed pre-existing CSI. Performance metrics (NMSE, KER, KGR) are obtained via simulation under equal power budgets, with ARIS vs PRIS comparisons presented as empirical outcomes rather than closed-form derivations. No self-definitional loops, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text. The 'already gathered CSI' premise is stated as an operating assumption that avoids extra probing, but this is not derived from the method itself and does not reduce any claimed result to a tautology by construction. The >4x KGR gain is a reported simulation finding, not forced by redefinition of inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that CSI is already available at the RIS and that standard convex optimizers can reliably align aggregate channels; no new physical entities or fitted constants beyond the optimization variables are introduced in the abstract.

axioms (1)
  • domain assumption RIS possesses accurate CSI of all user terminals without extra probing
    Abstract states the method 'does not impose additional probing burden on the network' and 'exploits the already gathered channel state information'.

pith-pipeline@v0.9.0 · 5856 in / 1178 out tokens · 45100 ms · 2026-05-19T06:50:55.708432+00:00 · methodology

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Reference graph

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