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arxiv: 2512.16224 · v2 · submitted 2025-12-18 · 📡 eess.SP

Simultaneous Secrecy and Covert Communications (SSACC) in Mobility-Aware RIS-Aided Networks

Yanyu Cheng , Yujian Hu , Haoran Liu , Hua Zhong , Wei Wang , Pan Li This is my paper

Pith reviewed 2026-05-16 21:59 UTC · model grok-4.3

classification 📡 eess.SP
keywords RISsecrecy capacitycovert communicationsdeep reinforcement learninggenerative diffusion modelspower allocationuser mobility
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The pith

A generative diffusion model with reinforcement learning balances secrecy capacity and detection error probability in mobile RIS networks.

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

The paper proposes a simultaneous secrecy and covert communications scheme in a reconfigurable intelligent surface aided network with a cooperative jammer. It derives closed-form expressions for average minimum detection error probability and average secrecy capacity under a worst-case eavesdropper scenario. To balance these under user mobility, it introduces a generative diffusion model combined with deep reinforcement learning for power allocation optimization. Simulations show this approach converges faster and performs better than standard deep deterministic policy gradient methods.

Core claim

The proposed GDM-DRL algorithm achieves faster convergence and superior performance compared to conventional DDPG methods in balancing security and capacity performance under user mobility.

What carries the argument

The generative diffusion model integrated with deep reinforcement learning for optimizing power allocation in the SSACC scheme.

Load-bearing premise

The eavesdropper can optimally adjust the detection threshold to minimize the detection error probability in the worst-case scenario.

What would settle it

A simulation run where the proposed GDM-DRL algorithm fails to show faster convergence or higher values of both AMDEP and ASC than DDPG would refute the performance claim.

Figures

Figures reproduced from arXiv: 2512.16224 by Haoran Liu, Hua Zhong, Pan Li, Wei Wang, Yanyu Cheng, Yujian Hu.

Figure 1
Figure 1. Figure 1: System model for SSACC system. The figure depicts [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Average channel capacities and ASC versus Alice’s [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: QoE versus 𝑃𝑚𝑎𝑥, where 𝜅 = 0.8 and 𝜅 = 0.2, respectively. is observed. Since the AMDEP threshold is typically above 80%, the analytical results for AMDEP are suitable for use in the optimization problem. In Fig. 5b, it can be seen that as 𝜅 increases, the ASC first grows, attaining a maximum, and then gradually decreases with further increases in 𝜅 and the derived results align closely with the simulation … view at source ↗
Figure 5
Figure 5. Figure 5: c illustrates the variation of Quality of Experience (QoE) in relation to 𝜅 (where 𝜅 = 𝑃𝐴/𝑃𝑚𝑎𝑥 = 1 − 𝑃ˆ 𝐽 /𝑃𝑚𝑎𝑥). The observed trend is primarily influenced by the ASC, exhibiting an initial increase followed by a subsequent decrease as 𝜅 varies. In the second part of QoE, however, a declining AMDEP combined with a rapidly increasing 𝑅𝑊 results in a diminished impact on QoE. This leads to an overall trend … view at source ↗
Figure 6
Figure 6. Figure 6: Test reward curves of GDM-aided and DDPG-aided [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: QoE and AMDEP versus 𝜅 under different environ￾mental distance parameters when 𝑃max = 40 dBm. The discrete markers indicate the optimal power allocation solutions output by the GDMOPT algorithm. covertness and secrecy performance and designing a GDM￾enhanced DRL algorithm, we demonstrated that the proposed approach can effectively optimize power allocation to improve system security under diverse user loca… view at source ↗
read the original abstract

In this paper, we propose a simultaneous secrecy and covert communications (SSACC) scheme in a reconfigurable intelligent surface (RIS)-aided network with a cooperative jammer. The scheme enhances communication security by maximizing the secrecy capacity and the detection error probability (DEP). Under a worst-case scenario for covert communications, we consider that the eavesdropper can optimally adjust the detection threshold to minimize the DEP. Accordingly, we derive closedform expressions for both average minimum DEP (AMDEP) and average secrecy capacity (ASC). To balance AMDEP and ASC, we propose a new performance metric and design an algorithm based on generative diffusion models (GDM) and deep reinforcement learning (DRL). The algorithm maximizes data rates under user mobility while ensuring high AMDEP and ASC by optimizing power allocation. Simulation results demonstrate that the proposed algorithm achieves faster convergence and superior performance compared to conventional deep deterministic policy gradient (DDPG) methods, thereby validating its effectiveness in balancing security and capacity performance.

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

1 major / 2 minor

Summary. The manuscript proposes a simultaneous secrecy and covert communications (SSACC) scheme in a mobility-aware RIS-aided network with a cooperative jammer. It derives closed-form expressions for average minimum detection error probability (AMDEP) and average secrecy capacity (ASC) under a worst-case eavesdropper that optimally tunes its detection threshold to minimize DEP. A new balancing metric is defined, and a generative diffusion model-deep reinforcement learning (GDM-DRL) algorithm is introduced for power allocation to maximize data rates while maintaining high AMDEP and ASC. Simulations claim faster convergence and superior performance versus conventional DDPG.

Significance. If the closed-form derivations hold and the GDM-DRL policy generalizes beyond the simulated environments, the work would provide analytical expressions and an efficient optimizer for jointly achieving secrecy and covertness in dynamic RIS networks. The integration of generative diffusion models into the DRL framework for this balancing task represents a technical contribution that could influence future learning-based secure communication designs.

major comments (1)
  1. [Performance analysis and metric definition] The closed-form expressions for AMDEP and ASC (derived in the performance analysis section) rest on the assumption that the eavesdropper can optimally adjust its detection threshold to minimize DEP under the worst-case scenario. This assumption directly defines the new performance metric that serves as the reward function for the GDM-DRL algorithm. In mobility-aware settings with time-varying channels, it is unclear whether the eavesdropper can achieve this optimum in real time (e.g., due to imperfect CSI or detection latency), which risks mis-specifying both the analytical results and the reported superiority of GDM-DRL over DDPG.
minor comments (2)
  1. [Abstract and Numerical Results] The abstract and simulation section would benefit from explicit reporting of the number of Monte Carlo trials, channel model parameters (e.g., Rician factors, mobility speed ranges), and error bars or confidence intervals on the convergence and performance curves to allow readers to assess the statistical significance of the GDM-DRL gains.
  2. [Problem Formulation] Notation for the new balancing metric should be introduced with a clear equation number and its relationship to AMDEP and ASC should be stated explicitly in the optimization problem formulation.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comment below.

read point-by-point responses

  1. Referee: The closed-form expressions for AMDEP and ASC (derived in the performance analysis section) rest on the assumption that the eavesdropper can optimally adjust its detection threshold to minimize DEP under the worst-case scenario. This assumption directly defines the new performance metric that serves as the reward function for the GDM-DRL algorithm. In mobility-aware settings with time-varying channels, it is unclear whether the eavesdropper can achieve this optimum in real time (e.g., due to imperfect CSI or detection latency), which risks mis-specifying both the analytical results and the reported superiority of GDM-DRL over DDPG.

    Authors: We appreciate the referee's observation. The worst-case eavesdropper assumption, where the eavesdropper optimally tunes its detection threshold to minimize the DEP, is a standard approach in the covert communications literature to derive analytical expressions for the minimum detection error probability. This provides a conservative bound on the covertness performance. The closed-form expressions for AMDEP and ASC are derived by averaging over the mobility-induced channel distributions, assuming the eavesdropper has access to the statistical channel information. While we acknowledge that in practical mobility scenarios with time-varying channels, achieving real-time optimal threshold adjustment may be hindered by imperfect CSI or detection latency, our analysis focuses on the theoretical performance limits under this model. Importantly, both the proposed GDM-DRL and the baseline DDPG algorithms are evaluated under the same metric and assumptions, ensuring a fair comparison of their relative performance in optimizing the power allocation. To further strengthen the manuscript, we will include additional discussion on the practical implications of this assumption and provide simulation results under scenarios with imperfect CSI and delayed threshold optimization. Therefore, we will make partial revisions to the performance analysis and simulation sections. revision: partial

Circularity Check

0 steps flagged

No significant circularity; derivations rest on explicit assumptions and standard models

full rationale

The paper states an explicit worst-case eavesdropper assumption and derives closed-form AMDEP/ASC expressions from standard statistical channel models. These expressions then define the reward metric for the GDM-DRL optimizer, which is trained in simulation rather than by fitting the target quantities to themselves. No self-definitional loops, fitted inputs renamed as predictions, or load-bearing self-citations appear in the derivation chain. The central performance claims therefore remain independent of the inputs by construction.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard wireless fading and mobility models for the closed-form derivations plus a tunable balancing parameter in the new performance metric that is optimized by the learning algorithm.

free parameters (1)
  • performance metric balancing parameter
    A weight or scaling factor in the new metric that trades off AMDEP against ASC and is tuned during DRL optimization.
axioms (1)
  • domain assumption Standard statistical models for RIS-reflected channels, jammer signals, and user mobility are valid for averaging over fading and movement.
    Invoked to obtain closed-form expressions for average minimum DEP and average secrecy capacity.

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    Relation between the paper passage and the cited Recognition theorem.

    Under a worst-case scenario for covert communications, we consider that the eavesdropper can optimally adjust the detection threshold to minimize the DEP. Accordingly, we derive closed-form expressions for both average minimum DEP (AMDEP) and average secrecy capacity (ASC).

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Reference graph

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