cs.NIMar 27, 2026

Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks

Authors: Deemah H. Tashman, Soumaya Cherkaoui

Organizations: LINCS Laboratory, Department of Computer and Software Engineering, Polytechnique Montréal, Montréal, QC, Canada, H3T 1J4

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Abstract

Cognitive radio networks (CRNs) are a key mechanism for alleviating spectrum scarcity by enabling secondary users (SUs) to opportunistically access licensed frequency bands without harmful interference to primary users (PUs). To address unreliable direct SU links and energy constraints common in next-generation wireless networks, this work introduces an adaptive, energy-aware hybrid reconfigurable intelligent surface (RIS) for underlay multiple-input single-output (MISO) CRNs. Distinct from prior approaches relying on static RIS architectures, our proposed RIS dynamically alternates between passive and active operation modes in real time according to harvested energy availability. We also model our scenario under practical hardware impairments and cascaded fading channels. We formulate and solve a joint transmit beamforming and RIS phase optimization problem via the soft actor-critic (SAC) deep reinforcement learning (DRL) method, leveraging its robustness in continuous and highly dynamic environments. Notably, we conduct the first systematic study of reward poisoning attacks on DRL agents in RIS-enhanced CRNs, and propose a lightweight, real-time defense based on reward clipping and statistical anomaly filtering. Numerical results demonstrate that the SAC-based approach consistently outperforms established DRL baselines, and that the dynamic hybrid RIS strikes a superior trade-off between throughput and energy consumption compared to fully passive and fully active alternatives. We further show the effectiveness of our defense in maintaining SU performance even under adversarial conditions. Our results advance the practical and secure deployment of RIS-assisted CRNs, and highlight crucial design insights for energy-constrained wireless systems.

Figures & tables

Ref.RIS ModeEnergy AdaptationChannel/ HardwareRL SecurityDRL
[ 20 , 21 , 22 ]Fixed hybridNoIdeal/idealNoDDPG/DPG
[ 9 , 15 , 16 , 17 , 18 ]PassiveNoIdeal/idealNoDDPG/SAC
[ 19 ]ActiveNoIdeal/idealNoDDPG/TD3
OursDynamic hybridYesCascaded /non-idealYesSAC
Table I: Comparison of Closely Related Works
Figure 1: The system model.
SymbolDefinition
AANumber of antennas at SU Transmitter
BBNumber of SU Receivers
RRNumber of RIS reflecting elements
WWNumber of PU receivers
Hs\mathbf{H_{s}}SU transmitter and RIS link channel matrix
hb\mathbf{h_{b}}RIS and SU receivers channel vector
Table II: List of Main Symbols
Defense TypeComputational CostEase of IntegrationRef.
Clipping + StatisticsMinimalVery easyOurs
Multi-env TrainingHighModerate[ 23 ]
Provable Robust OptimizationVery HighDifficult[ 24 ]
Bandit Game DefenseModerateModerate[ 25 ]
Certified Offline RLVery HighDifficult[ 26 ]
Table III: Comparison of Reward Poisoning Defense Strategies
Figure 2: SUs’ sum rate for different RIS reflecting elements (R)(R) . A=4A=4 , B=4B=4 , Pt=30P_{t}=30 dB, I=20I=20 dB, and κs=1\kappa_{s}=1 , κb=1\kappa_{b}=1 .
Figure 3: SUs’ sum rate for different cascade levels (κ)(\kappa) , SU transmitter antennas ( AA ), and SU receivers (B)(B) . R=30R=30 , Pt=30P_{t}=30 dB, I=20I=20 dB, and κs=κb=κ\kappa_{s}=\kappa_{b}=\kappa .
Figure 4: SUs’ average rate (reward) for different values of the RIS minimum reflection amplitude (βm)(\beta_{m}) and the maximum transmission power available at the SU transmitter (Pt)(P_{t}) . κs=κb=1\kappa_{s}=\kappa_{b}=1 , and I=20I=20 dB.
Figure 5: A comparison between the SUs’ average rate for the SAC, TD3, DDPG, and Random policies. Pt=1P_{t}=1 dB, κs=κb=1\kappa_{s}=\kappa_{b}=1 , I=1I=1 dB, and βm=0.6\beta_{m}=0.6 .
Figure 6: SUs’ average reward (rate) for different number of PU receivers (W)(W) for the SAC and DDPG policies. Pt=30P_{t}=30 dB, κs=κb=κ\kappa_{s}=\kappa_{b}=\kappa , and I=10I=10 dB.
Figure 7: A Comparison of SUs average rate for passive, active, and dynamic hybrid RIS at varying τ\tau .
Figure 8: A comparison between the passive and active mode usage in hybrid RIS system.
Figure 9: A comparison between the dynamic hybrid RIS and fixed hybrid RIS. τ=20\tau=20 Joules, Pt=30P_{t}=30 dB, I=20I=20 dB, and κs=κb=2\kappa_{s}=\kappa_{b}=2 .
τ\tau (J)Hybrid (J)Active (J)Energy Savings (%)
500.0920.35674.2%
400.1480.34356.8%
300.2260.35436.3%
100.3060.35213.1%
Table IV: Average Energy Consumption Comparison.
Figure 10: A trade-off between energy consumption and performance (average converged rate) for the fully active and hybrid RIS for different values of τ\tau . Pt=30P_{t}=30 dB, I=10I=10 dB, and κs=κb=2\kappa_{s}=\kappa_{b}=2 .
Figure 11: The SUs’ average rate for the invert and scale poisoning attacks for different values of the reward threshold (AT)(A_{T}) and the scaling factor (SC)(S_{C}) . τ=30\tau=30 Joules, Pt=30P_{t}=30 dB, I=20I=20 dB, and κs=κb=2\kappa_{s}=\kappa_{b}=2 .
Figure 12: Impact of the defense mechanism on the invert poisoning attack. τ=30\tau=30 Joules, Pt=30P_{t}=30 dB, I=20I=20 dB, AT=0.5A_{T}=0.5 , and κs=κb=2\kappa_{s}=\kappa_{b}=2 .
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