Virtual Adversarial Training-Based Semisupervised Specific Emitter Identification

Deep learning is a new direction of research for specific emitter identification (SEI). Radio frequency (RF) fingerprints of the emitter signal are small and sensitive to noise. It is difficult to assign labels containing category information in noncooperative communication scenarios. This makes network models obtained by conventional supervised learning methods perform unsatisfactorily, leading to poor identification performance. To address this limitation, this paper proposes a semisupervised SEI algorithm based on bispectrum analysis and virtual adversarial training (VAT). Bispectrum analysis is performed on RF signals to enhance individual discriminability. A convolutional neural network (CNN) is used for RF fingerprint extraction. We used a small amount of labelled data to train the CNN in an adversarial manner to improve the antinoise performance of the network in a supervised model. Virtual adversarial samples were calculated for VAT, which made full use of labelled and large unlabelled training data to further improve the generalization capability of the network. Results of numerical experiments on a set of six universal software radio peripheral (USRP; model B210) devices demonstrated the stable and fast convergence performance of the proposed method, which exhibited approximately 90% classification accuracy at 10 dB. Finally, the classification performance of our method was verified using other evaluation metrics including receiver operating characteristic and precision-recall.

5G F-OFDM Waveform Based Software-Defined Radio Technology

Filtered-orthogonal frequency division multiplexing (F-OFDM) is a quasi-orthogonal waveform candidate for the applications of the fifth generation (5G) communication system. In this study, an F-OFDM waveform with unequal sub-band sizes is proposed to improve the spectrum efficiency (SE) of the 5G system. The proposed waveform is modeled with the Blackman window-sinc filter and is developed based on the software-defined radio (SDR) technology for practical implementation. The result shows that the F-OFDM performance of the simulation and hardware implementation is approximately the same. The SE using the proposed F-OFDM waveform is 6% and 5.8% higher than the SE using the conventional OFDM waveform under the simulation in the LabVIEW NXG simulator and under the practical use in the universal software radio peripheral (USRP) platform, respectively.

USRP Implementation of Spectrum Sensing OFDM-Based Cognitive Radio Networks Using Energy Ratio Algorithm

This paper explains the Universal Software Radio Peripheral (USRP) Experiment results of Spectrum Sensing Algorithms based on the Energy Ration Algorithm for Cognitive Radio Networks which is latterly suggested in Spectrum observation for OFDM-Based Cognitive Radio Networks by using Energy Ratio Algorithm. This is completed through detecting the variance in the strength of the signal during a variety of confined OFDM subcarriers are used to ensure that the availability of the essential user is facilely discovered. Extensive experiments are performed, in particular, the effects of Signal to Noise Ratio (SNR). This paper observed that the experimental results gave lower detection performance compared to the simulation results. That’s due to existence of other systems which operate on same frequency band of 2.4GHz.

Realization of GPS Dynamic Hijacking Based on Software Radio

GPS positioning technology has a wide range of use scenarios, but due to the known characteristics of GPS signal open reception and modulation methods, many solutions can easily deceive GPS signals. Once this technology is abused in violation of regulations, it may cause major safety and property losses. In order to better study the anti-hijacking technology of PGS signals, this article starts with the generative dynamic hijacking of PGS, gives the implementation method based on software radio, and further learns the code phase and dopper between asynchronous hijacking and synchronous hijacking through software radio. The compensation calculation method of Leer frequency shift parameters, and verified the effect of the software radio’s GPS dynamic hijacking implementation method through examples, and provide theoretical support for the design and improvement of future anti-hijacking systems.

Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories

In a Wi-Fi indoor positioning system (IPS), the performance of the IPS depends on the channel state information (CSI), which is often limited due to the multipath fading effect, especially in indoor environments involving multiple non-line-of-sight propagation paths. In this paper, we propose a novel IPS utilizing trajectory CSI observed from predetermined trajectories instead of the CSI collected at each stationary location; thus, the proposed method enables all the CSI along each route to be continuously encountered in the observation. Further, by using a generative adversarial network (GAN), which helps enlarge the training dataset, the cost of trajectory CSI collection can be significantly reduced. To fully exploit the trajectory CSI’s spatial and temporal information, the proposed IPS employs a deep learning network of a one-dimensional convolutional neural network–long short-term memory (1DCNN-LSTM). The proposed IPS was hardware-implemented, where digital signal processors and a universal software radio peripheral were used as a modem and radio frequency transceiver, respectively, for both access point and mobile device of Wi-Fi. We verified that the proposed IPS based on the trajectory CSI far outperforms the state-of-the-art IPS based on the CSI collected from stationary locations through extensive experimental tests and computer simulations.

Design of a Power-Efficient Low Complexity Non Maximally Coefficient Symmetry Multi Rate Filter Bank for Wideband Channelization

A solution for existing and upcoming wireless communication standards is a software-defined radio (SDR) that extracts the desired radio channel. Channelizer is supposed to be the computationally complex part of SDR. In multi-standard wireless communication, the Software Radio Channelizer is often used to extract individual channels from a wideband input signal. Despite the effective channelizer design that reduces computing complexity, delay and power consumption remain a problem. Thus, to promote the effectiveness of the channelizer, we have provided the Non-Maximally Coefficient Symmetry Multirate Filter Bank. In this paper, to improve the hardware efficiency and functionality of the proposed schemes, we propose a polyphase decomposition and coefficient symmetry incorporated into the Non-Maximally Coefficient Symmetry Multirate Filter Bank. For sharp wideband channelizers, the proposed methods are suitable. Furthermore, polyphase decomposition filter and coefficient symmetry is incorporated into the Non-Maximally Coefficient Symmetry Multirate Filter Bank to improve the hardware efficiency, power efficient, flexibility, reduce hardware size and functionality of the proposed methods. To prove the complexity enhancement of the proposed system, the design to be the communication standard for complexity comparison.

Performance evaluation of new blind OFDM signal recognition based on properties of the second-order statistics using universal software radio peripheral platform

<div>In the context of cognitive radio (CR) or various military and civilian applications, modulation recognition (MR) is one of the most popular technical processes in the field of communication system recognition, by which the modulation type of the unknown received signal can be identified automatically by estimating one or more parameters of the modulated signal. This paper presents the performance evaluation of the new proposed blind system recognition method using only a particular property of the second-order statistics of the orthogonal frequency division multiplexing (OFDM) modulated signal. The effectiveness of the proposed method is illustrated using the implementation on universal software radio peripheral (USRP) platform. A comparison with computer simulations using MATLAB software is also performed, emphasizing the good performances of the method while the results obtained are close. We show the efficiency and behavior of the proposed method in the context of wireless communication systems based on OFDM modulation (3GPP/LTE, WiMAX, DVBT-2K, IEEE 802.22-1K,IEEE 802.22-2K, IEEE 802.22-4K). The proposed method can detect OF DM signals among other digital signals in a systematic and intelligent way even with low SNR values (when approaching to SNR=-2dB, the decision criteria tends towards 0).</div>

Lightweight Physical Layer Aided Key Agreement and Authentication for the Internet of Things

In this paper, we propose a lightweight physical layer aided authentication and key agreement (PL-AKA) protocol in the Internet of Things (IoT). The conventional evolved packet system AKA (EPS-AKA) used in long-term evolution (LTE) systems may suffer from congestion in core networks by the large signaling overhead as the number of IoT devices increases. Thus, in order to alleviate the overhead, we consider cross-layer authentication by integrating physical layer approaches to cryptography-based schemes. To demonstrate the feasibility of the PL-AKA, universal software radio peripheral (USRP) based tests are conducted as well as numerical simulations. The proposed scheme shows a significant reduction in the signaling overhead, compared to the conventional EPS-AKA in both the simulation and experiment. Therefore, the proposed lightweight PL-AKA has the potential for practical and efficient implementation of large-scale IoT networks.

M-CUBE

Millimeter-wave (mmWave) technologies represent a cornerstone for emerging wireless network infrastructure, and for RF sensing systems in security, health, and automotive domains. However, the lack of an experimental platform has been impeding research in this field. In this article, we propose to fill the gap with M3 (M-Cube), the first mmWave massive MIMO software radio. M3 features a fully reconfigurable array of phased arrays, with up to 8 RF chains and 256 antenna elements. Despite the orders of magnitude larger antenna arrays, its cost is orders of magnitude lower, even when compared with state-of-the-art single RF chain mmWave software radios. Case studies have demonstrated the usefulness of M3 design for research in mmWave massive MIMO communication and sensing.