On Estimating the Autoregressive Coefficients of Time-Varying Fading Channels

Vinogradova, Julia; Fodor, Gábor; Hammarberg, Peter

doi:10.1109/vtc2022-spring54318.2022.9860593

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…In this paper, we directly use the estimated channel matrices

, and then consider a physical layer authentication strategy to detect malicious attacks. There are several algorithms to obtain wireless channel estimations [ 37 , 38 , 39 , 40 , 41 ].…”

Section: System Modelmentioning

confidence: 99%

Channel Prediction-Based Security Authentication for Artificial Intelligence of Things

Qiu

Zhuang

et al. 2023

Sensors

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The emerging physical-layer unclonable attribute-aided authentication (PLUA) schemes are capable of outperforming traditional isolated approaches, with the advantage of having reliable fingerprints. However, conventional PLUA methods face new challenges in artificial intelligence of things (AIoT) applications owing to their limited flexibility. These challenges arise from the distributed nature of AIoT devices and the involved information, as well as the requirement for short end-to-end latency. To address these challenges, we propose a security authentication scheme that utilizes intelligent prediction mechanisms to detect spoofing attack. Our approach is based on a dynamic authentication method using long short term memory (LSTM), where the edge computing node observes and predicts the time-varying channel information of access devices to detect clone nodes. Additionally, we introduce a Savitzky–Golay filter-assisted high order cumulant feature extraction model (SGF-HOCM) for preprocessing channel information. By utilizing future channel attributes instead of relying solely on previous channel information, our proposed approach enables authentication decisions. We have conducted extensive experiments in actual industrial environments to validate our prediction-based security strategy, which has achieved an accuracy of 97%.

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“…In this paper, we directly use the estimated channel matrices

, and then consider a physical layer authentication strategy to detect malicious attacks. There are several algorithms to obtain wireless channel estimations [ 37 , 38 , 39 , 40 , 41 ].…”

Section: System Modelmentioning

confidence: 99%

Channel Prediction-Based Security Authentication for Artificial Intelligence of Things

Qiu

Zhuang

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Prediction of Packet Loss Rate in Non-Stationary Networks Based on Time-Varying Autoregressive Sequences

Deng

2023

Electronics

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Currently, most of the existing link parameter prediction schemes assume that the link state remains constant during the measurement period, making it difficult to capture their time-varying characteristics. To solve this problem, this paper proposes a prediction problem for packet loss rate in a non-stationary network environment. The measurement period is divided into several adjacent time windows, and the packet loss rates measured passively in each time window are regarded as non-stationary time sequences for real-time tracking to obtain the changes in link packet loss rate at a small cost. By analyzing time-varying autoregressive (TVAR) sequences, a scheme for estimating the time-varying coefficient was presented. In addition, a prediction scheme for the packet loss rate in a non-stationary network was proposed based on TVAR sequences. Finally, this paper conducts experiments based on a non-stationary network simulation environment established by the improved Gilbert–Elliot model and a small wireless multi-hop network experiment platform built in reality. Simulation and experimental results show that the prediction scheme of the packet loss rate based on the TVAR sequence can accurately predict the packet loss rate.

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On Estimating the Autoregressive Coefficients of Time-Varying Fading Channels

Cited by 2 publications

References 14 publications

Channel Prediction-Based Security Authentication for Artificial Intelligence of Things

Channel Prediction-Based Security Authentication for Artificial Intelligence of Things

Prediction of Packet Loss Rate in Non-Stationary Networks Based on Time-Varying Autoregressive Sequences

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