Safeguarding multiuser communication using full‐duplex jamming and Q‐learning algorithm

Qiu, Xiaoying; Jiang, Ting; Wang, Ning

doi:10.1049/iet-com.2018.0059

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…The prologue to AI based lightweight physical layer security is introduced [30], managing numerical models. Also, to ensure the security of wireless communication medium, a feedforward neural system is introduced in [31,32,33] to group adversarial attackers. In the meantime, during the time spent while preparing and processing data, the program isolates the trust of the devices into various levels and assess its performance.…”

Section: Related Workmentioning

confidence: 99%

CNN-based Security Authentication for Wireless Multimedia Devices

SK¹,

Koundinya²

2021

IJWMT

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Security is a major concern for wireless multimedia networks because of their role in providing various services. Traditional security techniques have inadequacies in identifying emerging security threats and also lacks in computing efficiency. Furthermore, conventional upper-layer authentication doesn't provide any protection for physical layer, thus leading to leakage of privacy data. Keep these issues in mind, the paper has envisioned an artificial intelligence-based security authentication system that is lightweight, adaptive and doesn't require any explicit programming. The neural network is built on convolutional filters which explore the data and learns the features or characteristic of the data. With this learned feature, the model will be able to recognize whether a wireless multimedia device present in a network is legitimate or not. Experimental analysis and validation have been performed on the trained model and ensure that the authentication of wireless multimedia devices can be achieved and also ensuring lightweight authentication system, which ensures less computation needs. The different neural model is also trained using gaussian noise of different standard deviation so that it can be used in a practical scenario like smart industry etc.

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Section: Related Workmentioning

confidence: 99%

CNN-based Security Authentication for Wireless Multimedia Devices

SK¹,

Koundinya²

2021

IJWMT

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“…In this section, unlike [11][12][13][14][15] where unsupervised and supervised learning are used, we implement the system model based on reinforcement learning, as shown in Figure 2.…”

Section: Reinforcement Learning For Multi-agentmentioning

confidence: 99%

“…Whereas in [14,15], the authors present a novel deep reinforcement learning-based joint spectrum sensing and power control algorithm for downlink communications in a cognitive small cell. Regrettably, the authors did not investigate the outage probability for the whole system.…”

Section: Introductionmentioning

confidence: 99%

Throughput Maximization for full-duplex two-way relay with finite buffers

Lin

2020

IJEECS

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<div>Optimal queueing control of multi-hop networks remains a challenging problem, e</div><div>specially in two-way relaying systems, even in the most straightforward scenarios.</div><div>In this paper, we explore two-way relaying having a full-duplex decode-and-forward</div><div>relay with two fifinite buffers. Principally, we propose a novel concept based on the</div><div>multi-agent reinforcement learning (that maximizes the cumulative network through</div><div>put) based on the combination of the buffer states and the lossy links; a decision is</div><div>generated as to whether it can transmit, receive or even simultaneously receive and</div><div>transmit information. Towards this objective, chieflfly, based on the queue state transi</div><div>tion and the lossy links, an analytic Markov decision process is proposed to analyze</div><div>this scheme, and the throughput and queueing delay are derived. Our numerical results</div><div>reveal exciting insights. First, artifificial intelligence based on reinforcement learning</div><div>is optimal when the length of the buffer is superior to a certain threshold. Second, we</div><div>demonstrate that reinforcement learning can boost transmission effificiency and prevent</div><div>buffer overflflow.</div>

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“…In recent years, as the amount of data has grown and computing power has increased, many new artificial intelligence methods have emerged, including reinforcement learning, transfer learning, and deep learning. Reinforcement learning can operate with only limited knowledge of the environment and with limited feedback on the quality of the decisions, and it is widely used in smart grid networks [11], communication networks [12], etc. Transfer learning can migrate the model for big data to small data and realize personalized migration, which is the direction of future development.…”

Section: Introductionmentioning

confidence: 99%

Nonuniformity Correction of Single Infrared Images Based on Deep Filter Neural Network

Jian

Wang

2018

Symmetry

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The fixed-pattern noise (FPN) caused by nonuniform optoelectronic response limits the sensitivity of an infrared imaging system and severely reduces the image quality. Therefore, nonuniform correction of infrared images is very important. In this paper, we propose a deep filter neural network to solve the problems of network underfitting and complex training with convolutional neural network (CNN) applications in nonuniform correction. Our work is mainly based on the idea of deep learning, where the nonuniform image noise features are fully learned from a large number of simulated training images. The network is designed by introducing the filter and the subtraction structure. The background interference of the image is removed by the filter, so the learning model is gathered in the nonuniform noise. The subtraction structure is used to further reduce the input-to-output mapping range, which effectively simplifies the training process. The results from the test on infrared images shows that our algorithm is superior to the state-of-the-art algorithm in visual effects and quantitative measurements, providing a new method for deep learning in nonuniformity correction of single images.

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Safeguarding multiuser communication using full‐duplex jamming and Q‐learning algorithm

Cited by 7 publications

References 26 publications

CNN-based Security Authentication for Wireless Multimedia Devices

CNN-based Security Authentication for Wireless Multimedia Devices

Throughput Maximization for full-duplex two-way relay with finite buffers

Nonuniformity Correction of Single Infrared Images Based on Deep Filter Neural Network

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