Universal Software Radio Peripheral

Abstract: Radio frequency (RF) fingerprint is the inherent hardware characteristics and has been employed to classify and identify wireless devices in many Internet of Things applications. This paper extracts novel RF fingerprint features, designs a hybrid and adaptive classification scheme adjusting to the environment conditions, and carries out extensive experiments to evaluate the performance. In particular, four modulation features, namely differential constellation trace figure, carrier frequency offset, modulation offset and I/Q offset extracted from constellation trace figure, are employed. The feature weights under different channel conditions are calculated at the training stage. These features are combined smartly with the weights selected according to the estimated signal to noise ratio at the classification stage. We construct a testbed using universal software radio peripheral platform as the receiver and 54 ZigBee nodes as the candidate devices to be classified, which are the most ZigBee devices ever tested. Extensive experiments are carried out to evaluate the classification performance under different channel conditions, namely line-of-sight (LOS) and nonline-of-sight scenarios. We then validate the robustness by carrying out the classification process 18 months after the training, which is the longest time gap. We also use a different receiver platform for classification for the first time. The classification error rate is as low as 0.048 in LOS scenario, and 0.1105 even when a different receiver is used for classification 18 months after the training. Our hybrid classification scheme has thus been demonstrated effective in classifying a large amount of ZigBee devices.

Abstract: Cognitive radio has been proposed in recent years to promote the spectrum utilization by exploiting the existence of spectrum holes. The heterogeneity of both spectrum availability and traffic demand in secondary users has brought significant challenge for efficient spectrum allocation in cognitive radio networks. Observing that spectrum resource can be better matched to traffic demand of secondary users with the help of relay node that has rich spectrum resource, in this paper we exploit a new research direction for cognitive radio networks by utilizing cooperative relay to assist the transmission and improve spectrum efficiency. An infrastructure-based secondary network architecture has been proposed to leverage relay-assisted discontiguous OFDM (D-OFDM) for data transmission. In this architecture, relay node will be selected which can bridge the source and the destination using its common channels between those two nodes. With the introduction of cooperative relay, many unique problems should be considered, especially the issue for relay selection and spectrum allocation. We propose a central- ized heuristic solution to address the new resource allocation problem. To demonstrate the feasibility and performance of cooperative relay for cognitive radio, a new MAC protocol has been proposed and implemented in a Universal Software Radio Peripheral (USRP)-based testbed. Experimental results show that the throughput of the whole system is greatly increased by exploiting the benefit of cooperative relay.

Abstract: With the increasing popularity of the Internet of Things (IoT), device identification, and authentication has become a critical security issue. Recently, radio frequency (RF) fingerprint-based identification schemes have attracted wide attention as they extract the inherent characteristics of hardware circuits which is very hard to forge. However, existing RF fingerprint-based approaches face the problems of unstable region of interest (ROI), high-cost feature design, and incomplete automation. To address these problems, this paper proposes a multisampling convolutional neural network (MSCNN) to extract RF fingerprint from the selected ROI for classifying ZigBee devices. A signal-to-noise ratio (SNR) adaptive ROI selection algorithm is also developed to alleviate the effect of semi-steady behavior of ZigBee devices owing to sleep mode switching. The proposed MSCNN uses multiple downsampling transformations for multiscale feature extraction and classification automatically. To validate and evaluate the performance of our proposed method, we design a testbed consisting of one low-cost universal software radio peripheral (USRP) as the receiver and 54 CC2530 devices as targets for identification. Extensive experiments are conducted to demonstrate the feasibility and reliability of MSCNN both in the line-of-sight (LOS) scenarios and non-LOS (NLOS) scenarios. The classification accuracy is as high as 97% under the LOS scenarios around SNR = 30 dB. Our scheme is robust over a wide range of SNRs under the LOS scenarios as well as under the NLOS scenarios.

Abstract: Experimental research on wireless communication protocols frequently requires full access to all protocol layers, down to and including the physical layer. Software Defined Radio (SDR) hardware platforms, together with real-time signal processing frameworks, offer a basis to implement transceivers that can allow such experimentation and sophisticated measurements. We present a complete Orthogonal Frequency Division Multiplexing (OFDM) receiver implemented in GNU Radio and fitted for operation with an Ettus USRP N210. To the best of our knowledge, this is the first prototype of a GNU Radio based OFDM receiver for this technology. Our receiver comprises all layers up to parsing the MAC header and extracting the payload of IEEE 802.11a/g/p networks. It supports both WiFi with a bandwidth of 20 MHz and IEEE 802.11p DSRC with a bandwidth of 10 MHz. We validated and verified our implementation by means of interoperability tests, and present representative performance measurements. By making the code available as Open Source we provide an easy-to-access system that can be readily used for experimenting with novel signal processing algorithms.