Uncertainty-Aware Jamming Mitigation with Active RIS: A Robust Stackelberg Game Approach

• URL: http://arxiv.org/abs/2602.18165v1
• Date: Fri, 20 Feb 2026 12:02:01 GMT
• Title: Uncertainty-Aware Jamming Mitigation with Active RIS: A Robust Stackelberg Game Approach
• Authors: Xiao Tang, Zhen Ma, Limeng Dong, Yichen Wang, Qinghe Du, Dusit Niyato, Zhu Han,
• Abstract summary: This paper investigates the jamming mitigation by leveraging an active reconfigurable intelligent surface (ARIS)<n>We adopt the Stackelberg game formulation to model the strategic interaction between the legitimate side and the adversary.<n>We first derive the optimal jamming policy as the follower's best response, which is then incorporated into the legitimate-side optimization for robust anti-jamming design.
• Score: 65.06640919319413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Malicious jamming presents a pervasive threat to the secure communications, where the challenge becomes increasingly severe due to the growing capability of the jammer allowing the adaptation to legitimate transmissions. This paper investigates the jamming mitigation by leveraging an active reconfigurable intelligent surface (ARIS), where the channel uncertainties are particularly addressed for robust anti-jamming design. Towards this issue, we adopt the Stackelberg game formulation to model the strategic interaction between the legitimate side and the adversary, acting as the leader and follower, respectively. We prove the existence of the game equilibrium and adopt the backward induction method for equilibrium analysis. We first derive the optimal jamming policy as the follower's best response, which is then incorporated into the legitimate-side optimization for robust anti-jamming design. We address the uncertainty issue and reformulate the legitimate-side problem by exploiting the error bounds to combat the worst-case jamming attacks. The problem is decomposed within a block successive upper bound minimization (BSUM) framework to tackle the power allocation, transceiving beamforming, and active reflection, respectively, which are iterated towards the robust jamming mitigation scheme. Simulation results are provided to demonstrate the effectiveness of the proposed scheme in protecting the legitimate transmissions under uncertainties, and the superior performance in terms of jamming mitigation as compared with the baselines.

Related papers

• How Worst-Case Are Adversarial Attacks? Linking Adversarial and Perturbation Robustness [4.60092781176058]
  Adrial attacks are widely used to identify model vulnerabilities, but their validity as proxies for robustness to random perturbations remains debated.<n>We ask whether an adversarial example provides a representative estimate of misprediction risk under perturbations of the same magnitude.<n>We study the limits of this connection by proposing an attack strategy designed to probe vulnerabilities in regimes that are statistically closer to uniform noise.
  arXiv  Detail & Related papers  (2026-01-20T22:24:47Z)
• Potent but Stealthy: Rethink Profile Pollution against Sequential Recommendation via Bi-level Constrained Reinforcement Paradigm [44.622203626828345]
  Sequential Recommenders, which exploit dynamic user intents through interaction sequences, are vulnerable to adversarial attacks.<n>This paper focuses on the Profile Pollution Attack that subtly contaminates partial user interactions to induce targeted mispredictions.<n>We propose a constrained reinforcement driven attack CREAT that synergizes a bi-level optimization framework with multi-reward reinforcement learning to balance adversarial efficacy and stealthiness.
  arXiv  Detail & Related papers  (2025-11-12T15:00:52Z)
• Exploring Semantic-constrained Adversarial Example with Instruction Uncertainty Reduction [51.50282796099369]
  This paper develops a multi-dimensional instruction uncertainty reduction framework to generate semantically constrained adversarial examples.<n>By predicting the language-guided sampling process, the optimization process will be stabilized by the designed ResAdv-DDIM sampler.<n>We realize the reference-free generation of semantically constrained 3D adversarial examples for the first time.
  arXiv  Detail & Related papers  (2025-10-27T04:02:52Z)
• Physical Layer Deception based on Semantic Distortion [58.38604209714828]
  Physical layer deception (PLD) is a framework that integrates physical layer security (PLS) with deception techniques.<n>We extend this framework to a semantic communication model and conduct a theoretical analysis using semantic distortion as the performance metric.
  arXiv  Detail & Related papers  (2025-10-16T18:23:35Z)
• One Token Embedding Is Enough to Deadlock Your Large Reasoning Model [91.48868589442837]
  We present the Deadlock Attack, a resource exhaustion method that hijacks an LRM's generative control flow.<n>Our method achieves a 100% attack success rate across four advanced LRMs.
  arXiv  Detail & Related papers  (2025-10-12T07:42:57Z)
• RiskPO: Risk-based Policy Optimization via Verifiable Reward for LLM Post-Training [13.309653291779233]
  Reinforcement learning with verifiable reward has emerged as a central paradigm for post-training large language models (LLMs)<n>We argue that these issues stem from overemphasizing high-probability output sequences while neglecting rare but informative reasoning paths.<n>We propose Risk-based Policy Optimization (RiskPO), which substitutes classical mean-based objectives with principled risk measures.
  arXiv  Detail & Related papers  (2025-10-01T13:53:09Z)
• CovertAuth: Joint Covert Communication and Authentication in MmWave Systems [23.84881074442097]
  Beam alignment (BA) is a crucial process in millimeter-wave (mmWave) communications.<n>BA is particularly vulnerable to eavesdropping and identity impersonation attacks.<n>This paper proposes a novel secure framework named CovertAuth to enhance the security of the BA phase against such attacks.
  arXiv  Detail & Related papers  (2025-07-11T09:19:25Z)
• Achieving Hiding and Smart Anti-Jamming Communication: A Parallel DRL Approach against Moving Reactive Jammer [11.429298787140992]
  reactive jammer initiates high-power tracking jamming upon detecting any transmission activity.<n>This presents dual imperatives: maintaining hiding to avoid the jammer's detection and simultaneously evading indiscriminate jamming.<n>Current methodologies struggle with the complexity of simultaneously optimizing these two requirements.
  arXiv  Detail & Related papers  (2025-02-04T15:03:11Z)
• Jailbreaking as a Reward Misspecification Problem [80.52431374743998]
  We propose a novel perspective that attributes this vulnerability to reward misspecification during the alignment process.<n>We introduce a metric ReGap to quantify the extent of reward misspecification and demonstrate its effectiveness.<n>We present ReMiss, a system for automated red teaming that generates adversarial prompts in a reward-misspecified space.
  arXiv  Detail & Related papers  (2024-06-20T15:12:27Z)
• On Minimizing Adversarial Counterfactual Error in Adversarial RL [18.044879441434432]
  adversarial noise poses significant risks in safety-critical scenarios.<n>We introduce a novel objective called Adversarial Counterfactual Error (ACoE)<n>Our method significantly outperforms current state-of-the-art approaches for addressing adversarial RL challenges.
  arXiv  Detail & Related papers  (2024-06-07T08:14:24Z)
• Towards Robust Speech-to-Text Adversarial Attack [78.5097679815944]
  This paper introduces a novel adversarial algorithm for attacking the state-of-the-art speech-to-text systems, namely DeepSpeech, Kaldi, and Lingvo. Our approach is based on developing an extension for the conventional distortion condition of the adversarial optimization formulation. Minimizing over this metric, which measures the discrepancies between original and adversarial samples' distributions, contributes to crafting signals very close to the subspace of legitimate speech recordings.
  arXiv  Detail & Related papers  (2021-03-15T01:51:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.