Dense-Device-Enabled Cooperative Networks for Efficient and Secure Transmission

Han, Shuai; Xu, Sai; Meng, Weixiao; Li, Cheng

doi:10.1109/mnet.2018.1700292

Cited by 68 publications

(26 citation statements)

References 13 publications

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“…Physical layer authentication has many advantages, such as low computational requirement and network overhead [6]- [8], but it also faces many challenges. The main reason is that most of the existing physical layer authentication techniques rely on static mechanisms while encountering the complex and dynamic wireless environment of 5G-and-beyond wireless networks.…”

Section: Challenges For Existing Physical Layer Authenticationmentioning

confidence: 99%

Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks

Fang

Wang

Tomasin

2019

IEEE Wireless Commun.

123

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The fifth generation (5G) and beyond wireless networks are critical to support diverse vertical applications by connecting heterogeneous devices and machines, which directly increase vulnerability for various spoofing attacks. Conventional cryptographic and physical layer authentication techniques are facing some challenges in complex dynamic wireless environments, including significant security overhead, low reliability, as well as difficulties in pre-designing a precise authentication model, providing continuous protection, and learning time-varying attributes. In this article, we envision new authentication approaches based on machine learning techniques by opportunistically leveraging physical layer attributes, and introduce intelligence to authentication for more efficient security provisioning. Machine learning paradigms for intelligent authentication design are presented, namely for parametric/non-parametric and supervised/unsupervised/reinforcement learning algorithms. In a nutshell, the machine learning-based intelligent authentication approaches utilize specific features in the multi-dimensional domain for achieving cost-effective, more reliable, model-free, continuous, and situation-aware device validation under unknown network conditions and unpredictable dynamics. 2 heterogeneous devices and machines [1]. The dramatically growing number of low-cost devices and access points with increased mobility and heterogeneity leads to the extremely complex and dynamic environment of 5G-and-beyond wireless networks. Specifically, due to the open broadcast nature of radio signal propagation, widely adopted standardized transmission protocols, and intermittent communication characteristic, wireless communications are extremely vulnerable to interception and spoofing attacks [2]. As shown in the conventional 'Alice-Bob-Eve' model of Fig. 1, Alice and Bob are legitimate devices and communicate with each other in the presence of a Spoofer (Eve). This adversary dynamically intends to intercept legitimate communications and to impersonate Alice for obtaining illegal advantages from Bob. The main objective of Bob is to uniquely and unambiguously identify Alice by authentication techniques. Multi-dimensional information received at Bob Time Spoofing Signal Legitimate Signal Scattering cluster Scattering cluster Scattering cluster Eve (Spoofer) Alice Bob Process-related information Physical information Fig. 1: Alice-Bob-Eve model in the complex time-varying environment. Bob identifies Alice from Spoofer based on the multi-dimensional received information, which may be related to communication links, physical environment, and communication process, just to name a few.Although cryptographic techniques have been widely studied for authentication, they may fall short of the desired performance in many emerging scenarios of 5G-and-beyond wireless networks [2]. The fundamental weaknesses of conventional cryptography techniques are the increasing latencies, communication and computation overheads for achieving better security pe...

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Section: Challenges For Existing Physical Layer Authenticationmentioning

confidence: 99%

Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks

Fang

Wang

Tomasin

2019

IEEE Wireless Commun.

123

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“…The electromagnetic signal is transmitted by the millimeter wave base station and beamforming technology is used to reduce the interference of the clutter signal [15,16]. The reflected echo of the target is obtained by multipath reflection, and the location of UAV is transmitted through the data processing center [17,18]. Accurate identification and positioning are achieved in a timely manner.…”

Section: System Modelmentioning

confidence: 99%

Radar-Assisted UAV Detection and Identification Based on 5G in the Internet of Things

Zhao

Yang

et al. 2019

Wireless Communications and Mobile Computing

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Unmanned aerial vehicles (UAVs) have broad application potential for the Internet of Things (IoT) due to their small size, low cost, and flexible control. At present, the main positioning method for UAVs is the use of GPS. However, GPS positioning may be affected by stronger electromagnetic signals from spoofing attacks. In this study, a radar-assisted positioning method based on 5G millimeter waves is proposed. In 5G end-to-end network slices, the rotors of UAVs can be detected and identified by deploying 5G millimeter wave radar. High-resolution range profile (HRRP) is used to obtain the UAV location in the detection zone. Micro-Doppler characteristics are used to identify the UAVs and the cepstrum method is used to extract the number and speed information of the UAV rotor. The sinusoidal frequency modulation (SFM) parameter optimization method is used to separate multiple UAVs. The proposed method provides information on the number of UAVs, the position of the UAV, the number of rotors, and the rotation speed of each rotor. The simulation results show that the proposed radar detection method is well suited for UAV detection and identification and provides a valid GPS-independent method for UAV tracking.

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“…To this end, the spectral efficiency and transmission latency shall be greatly improved and reduced, respectively, under massive connected devices. To fulfill these requirements, novel radio access technologies are required, such as novel multiple access (MA) technologies, network architectures, encoding, and modulation methods [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Aided Detection Method for FTN-Based NOMA

Pan

Wang

et al. 2020

Wireless Communications and Mobile Computing

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The rapid booming of future smart city applications and Internet of things (IoT) has raised higher demands on the next-generation radio access technologies with respect to connection density, spectral efficiency (SE), transmission accuracy, and detection latency. Recently, faster-than-Nyquist (FTN) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies to achieve higher SE and massive connections, respectively. In this paper, we aim to exploit the joint benefits of FTN and NOMA by superimposing multiple FTN-based transmission signals on the same physical recourses. Considering the complicated intra- and interuser interferences introduced by the proposed transmission scheme, the conventional detection methods suffer from high computational complexity. To this end, we develop a novel sliding-window detection method by incorporating the state-of-the-art deep learning (DL) technology. The data-driven offline training is first applied to derive a near-optimal receiver for FTN-based NOMA, which is deployed online to achieve high detection accuracy as well as low latency. Monte Carlo simulation results validate that the proposed detector achieves higher detection accuracy than minimum mean squared error-frequency domain equalization (MMSE-FDE) and can even approach the performance of the maximum likelihood-based receiver with greatly reduced computational complexity, which is suitable for IoT applications in smart city with low latency and high reliability requirements.

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Dense-Device-Enabled Cooperative Networks for Efficient and Secure Transmission

Cited by 68 publications

References 13 publications

Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks

Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks

Radar-Assisted UAV Detection and Identification Based on 5G in the Internet of Things

A Deep Learning-Aided Detection Method for FTN-Based NOMA

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