Towards physical layer identification of cognitive radio devices

Andrews, Seth; Gerdes, Ryan; Li, Ming

doi:10.1109/cns.2017.8228658

Cited by 10 publications

(11 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm starts out with BSs communicating the information about all connected SUs SU and PUs with other BSs in different cluster [see Fig. 12: step (1) and (2)]. The purpose of this step is to have a knowledge of updated traffic status of the cluster.…”

Section: Methodsmentioning

confidence: 99%

“…In [46], author investigates security at physical layer of cognitive radios in non-orthogonal multiple access (Non-Orthogonal Multiple Access (NOMA)) networks. Author in [2] propose Dynamic spectrum access (Dynamic Spectrum Access (DSA)) to use the existing spectrum without any interference. They use device fingerprinting to detect any change in bandwidth of the non-cognitive device in the spectrum.…”

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Cognitive Radio in Digital Agriculture: A Physical Layer Overlay Based Underground Radio Propagation System

Raza¹,

Salam²,

Karabiyik³

2020

Preprint

View full text Add to dashboard Cite

In digital agriculture, the cognitive radio technology is being envisaged as solution to spectral shortage problems by allowing agricultural cognitive users to co-exist with PU in the same spectrum on the field. Cognitive radios increase system capacity and spectral efficiency by sensing the spectrum and adapting the transmission parameters. This design requires a robust, adaptable and flexible physical layer to support cognitive radio functionality. In this paper, two different approaches are proposed to improve the application of cognitive radios in agriculture. A novel physical layer architecture for cognitive radio based on cognition, cooperation, and cognitive interference avoidance has been developed by using power control for digital agriculture applications. The design is based on sensing of spectrum usage, detecting the message/spreading code of PU, cognitive relaying, cooperation, and cognition of channel parameters. Moreover, the power and rate allocation, ergodic, and outage capacity formulas are also presented. Furthermore, a two-hop load balancing approach is presented using a cognitive radios as a relay node in the network to improve throughput and optimize the load between different area of an agricultural field.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Cognitive Radio in Digital Agriculture: A Physical Layer Overlay Based Underground Radio Propagation System

Raza¹,

Salam²,

Karabiyik³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, secure authentication between multiple transmitters and users should be taken into account. There are some existing works that focus on PLA in mission critical machine-type communication (MTC) and cognitive radio networks [33,58,[102][103][104][105] .…”

Section: A Multiuser Communication Networkmentioning

confidence: 99%

Physical layer authentication in wireless communication networks: A survey

Bai

Zhu

Liu

et al. 2020

J. Commun. Inf. Netw.

View full text Add to dashboard Cite

Physical layer security (PLS) in wireless communication systems has attracted extensive research attentions in recent years. Unlike cryptography-based methods applied in upper-layer in network, PLS methods are applied in physical layers and can provide information-theoretic security by utilizing the randomness of signals and wireless channels. In this survey, we provide a comprehensive review in the domain of physical layer authentication (PLA) in wireless communication systems, including the concepts, several key techniques of typical PLA architectures as well as future challenges and research trends in more sophisticated communication systems. The survey begins with an overview of the background and basic concepts of PLA, such as the general model of wireless security communication system, typical frameworks of key-based/less PLA systems, and the common attack models. We then discuss the major concerns and key techniques that are applied in PLA systems, where three types of authentication schemes are considered, i.e., the authentication based on channel information, radio-frequency and identity watermarks. Basic models and representative research results about key approaches and techniques applied to the authentication systems above are subsequently covered. Finally, the associated challenges and potential research trends of PLA in future communication systems are presented at the end of the survey paper.

show abstract

“…To improve the identification accuracy, these methods often need an additional anechoic chamber to obtain pure RFF features in the training phase. In addition, a transfer learning method is proposed in [34] for the identification of devices with changing bandwidth. It is also applied to the removal of the bias introduced by RF receivers through the use of one golden reference [35].…”

Section: Introductionmentioning

confidence: 99%

“…It is also applied to the removal of the bias introduced by RF receivers through the use of one golden reference [35]. The core idea of [34] and [35] is to know the change of environment with the aid of a known reference device. This idea can also be used to address the changing location issue.…”

Section: Introductionmentioning

confidence: 99%

Location-Invariant Physical Layer Identification Approach for WiFi Devices

Xing

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Recently, Radio Frequency Fingerprinting (RFF) becomes a promising technique which augments existing multifactor authentication schemes at the device level to counter forgery and related threats. As RFF leverages the discriminable hardware imperfections reflected in Radio Frequency (RF) signals for device identification, it has a good property of scalability, accuracy, energy-efficiency and tamper resistance. However, its identification accuracy might be compromised when the locations of training and testing are different, which is a more realistic assumption in practical scenarios. To address this issue, we study the location-invariant RFF feature extraction and identification method for WiFi Network Interface Cards (NICs). Firstly, we present an RFF feature extraction approach named Differential Phase of Pilots (DPoP). To further address the low-dimensional feature space problem, we propose another novel RFF extraction approach named Amplitude of Quotient (AoQ). AoQ exploits the fact that the RFFs of two Long Training Sequences (LTSs) in WiFi frames exhibit semi-steady characteristics and two LTSs in the same frame have similar channel frequency responses. Next, we use Euclidean distance and Deep Neural Network (DNN) for AoQ authentication and identification, respectively. Experimental results verify the effectiveness of our proposed AoQ method among 55 WiFi NICs of 5 models. The identification accuracy is higher than 95% and the Equal Error Rate (EER) is around 4% when SNR is higher than 40 dB. INDEX TERMS Wireless communications, physical layer security, radio frequency fingerprint, device identification, 802.11n OFDM.

show abstract

Towards physical layer identification of cognitive radio devices

Cited by 10 publications

References 18 publications

Cognitive Radio in Digital Agriculture: A Physical Layer Overlay Based Underground Radio Propagation System

Cognitive Radio in Digital Agriculture: A Physical Layer Overlay Based Underground Radio Propagation System

Physical layer authentication in wireless communication networks: A survey

Location-Invariant Physical Layer Identification Approach for WiFi Devices

Contact Info

Product

Resources

About