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For validation and demonstration of high accuracy ranging and positioning algorithms and systems, a wideband radio signal generation and acquisition testbed, tightly synchronized in time and frequency, is needed. The development of such a testbed requires solutions to several challenges. Tight time and frequency synchronization, derived from a centrally distributed time-frequency reference signal, needs to be maintained in the hardware of the transmitter and receiver nodes, and wideband signal acquisition requires sustainable data throughput between the receiver and host PC as well as data storage at GB level. This article presents a testbed for wideband radio signal acquisition, for validation and demonstration of high accuracy ranging and positioning. It consists of multiple Ettus X310 universal software radio peripherals (USRPs) and supports high accuracy (<100 ps) time-deterministic, sustainable signal transmission and acquisition, with a bandwidth up to 320 MHz (in dual channel mode) and frequencies up to 6 GHz. Generation and processing of wideband arbitrary signal waveforms is done offline. To realize these features, radio frequency on chip (RFNoC) compatible HDL units were developed for integration in the X310 SDR platform. Wideband transmission and signal acquisition at a lower duty cycle is applied to reduce the data offloading throughput to the host’s personal computer (PC). Benchmarking of the platform was performed to demonstrate sustainable long duration dual channel acquisition. Indoor range measurements with the synchronous operation of the testbed show a decimeter-level accuracy.
Cherif Diouf; Gerard J. M. Janssen; Han Dun; Tarik Kazaz; Christian C. J. M. Tiberius. A USRP-Based Testbed for Wideband Ranging and Positioning Signal Acquisition. IEEE Transactions on Instrumentation and Measurement 2021, 70, 1 -15.
AMA StyleCherif Diouf, Gerard J. M. Janssen, Han Dun, Tarik Kazaz, Christian C. J. M. Tiberius. A USRP-Based Testbed for Wideband Ranging and Positioning Signal Acquisition. IEEE Transactions on Instrumentation and Measurement. 2021; 70 ():1-15.
Chicago/Turabian StyleCherif Diouf; Gerard J. M. Janssen; Han Dun; Tarik Kazaz; Christian C. J. M. Tiberius. 2021. "A USRP-Based Testbed for Wideband Ranging and Positioning Signal Acquisition." IEEE Transactions on Instrumentation and Measurement 70, no. : 1-15.
This paper presents end-to-end learning from spectrum data-an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural networks. End-to-end learning allows to: 1) automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments and 2) train wireless signal classifiers in one end-to-end step which eliminates the need for complex multi-stage machine learning processing pipelines. The purpose of this paper is to present the conceptual framework of end-to-end learning for spectrum monitoring and systematically introduce a generic methodology to easily design and implement wireless signal classifiers. Furthermore, we investigate the importance of the choice of wireless data representation to various spectrum monitoring tasks. In particular, two case studies are elaborated: 1) modulation recognition and 2) wireless technology interference detection. For each case study three convolutional neural networks are evaluated for the following wireless signal representations: temporal IQ data, the amplitude/phase representation, and the frequency domain representation. From our analysis, we prove that the wireless data representation impacts the accuracy depending on the specifics and similarities of the wireless signals that need to be differentiated, with different data representations resulting in accuracy variations of up to 29%. Experimental results show that using the amplitude/phase representation for recognizing modulation formats can lead to performance improvements up to 2% and 12% for medium to high SNR compared to IQ and frequency domain data, respectively. For the task of detecting interference, frequency domain representation outperformed amplitude/phase and IQ data representation up to 20%.
Merima Kulin; Tarik Kazaz; Ingrid Moerman; Eli De Poorter. End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications. IEEE Access 2018, 6, 18484 -18501.
AMA StyleMerima Kulin, Tarik Kazaz, Ingrid Moerman, Eli De Poorter. End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications. IEEE Access. 2018; 6 ():18484-18501.
Chicago/Turabian StyleMerima Kulin; Tarik Kazaz; Ingrid Moerman; Eli De Poorter. 2018. "End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications." IEEE Access 6, no. : 18484-18501.
Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant. Identifying concurrent technologies is an important step towards efficient spectrum sharing. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals’ modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI’s probability distribution. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. Experimental evaluation indicates that more than 92% accuracy is achieved with the appropriate configuration. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access.
Wei Liu; Merima Kulin; Tarik Kazaz; Adnan Shahid; Ingrid Moerman; Eli De Poorter. Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices. Sensors 2017, 17, 2081 .
AMA StyleWei Liu, Merima Kulin, Tarik Kazaz, Adnan Shahid, Ingrid Moerman, Eli De Poorter. Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices. Sensors. 2017; 17 (9):2081.
Chicago/Turabian StyleWei Liu; Merima Kulin; Tarik Kazaz; Adnan Shahid; Ingrid Moerman; Eli De Poorter. 2017. "Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices." Sensors 17, no. 9: 2081.
The future 5G wireless infrastructure will support any-to-any connectivity between densely deployed smart objects that form the emerging paradigm known as the Internet of Everything (IoE). Compared to traditional wireless networks that enable communication between devices using a single technology, 5G networks will need to support seamless connectivity between heterogeneous wireless objects and IoE networks. To tackle the complexity and versatility of future IoE networks, 5G will need to guarantee optimal usage of both spectrum and energy resources and further support technology-agnostic connectivity between objects. One way to realize this is to combine intelligent network control with adaptive software defined air interfaces. In this paper, a flexible and compact platform is proposed for on-the-fly composition of low-power adaptive air interfaces, based on hardware/software co-processing. Compared to traditional Software Defined Radio (SDR) systems that perform computationally-intensive signal processing algorithms in software, consume significantly power and have a large form factor, the proposed platform uses modern hybrid FPGA technology combined with novel ideas such as RF Network-on-Chip (RFNoC) and partial reconfiguration. The resulting system enables composition of reconfigurable air interfaces based on hardware/software co-processing on a single chip, allowing high processing throughput, at a smaller form factor and reduced power consumption.
Tarik Kazaz; Christophe Van Praet; Merima Kulin; Pieter Willemen; Ingrid Moerman. Hardware Accelerated SDR Platform for Adaptive Air Interfaces. 2017, 1 .
AMA StyleTarik Kazaz, Christophe Van Praet, Merima Kulin, Pieter Willemen, Ingrid Moerman. Hardware Accelerated SDR Platform for Adaptive Air Interfaces. . 2017; ():1.
Chicago/Turabian StyleTarik Kazaz; Christophe Van Praet; Merima Kulin; Pieter Willemen; Ingrid Moerman. 2017. "Hardware Accelerated SDR Platform for Adaptive Air Interfaces." , no. : 1.
VoIP (Voice over Internet) provides delivery of voice information over unsecured IP-based networks like the Internet. VoIP data, signaling and voice, needs to be secured in such an environment. Security mechanisms take their toll on VoIP system performance. SIP is dominant signaling protocol for VoIP. This paper measures relative decrease in VoIP performance of system with secured SIP signaling over one without it. It compares SIP with authentication enabled over three transport protocols: UDP, TCP and TLS. Peak throughput of concurrent calls, registration request delay, session request delay, SIP server CPU and RAM usage are measured. Testbed environment consists of Asterisk IP private branch exchange (PBX) as a part of Elastix server, several SIP user agents and SIPp traffic generator. Test results show that performance of SIP over TLS based signaling is four times lower than the SIP signaling over UDP in most metrics.
Merima Kulin; Tarik Kazaz; Sasa Mrdovic. SIP server security with TLS: Relative performance evaluation. 2012 IX International Symposium on Telecommunications (BIHTEL) 2012, 1 -6.
AMA StyleMerima Kulin, Tarik Kazaz, Sasa Mrdovic. SIP server security with TLS: Relative performance evaluation. 2012 IX International Symposium on Telecommunications (BIHTEL). 2012; ():1-6.
Chicago/Turabian StyleMerima Kulin; Tarik Kazaz; Sasa Mrdovic. 2012. "SIP server security with TLS: Relative performance evaluation." 2012 IX International Symposium on Telecommunications (BIHTEL) , no. : 1-6.