Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Implementation of Spectrum-Sensing for Cognitive Radio Using USRP with GNU Radio and a Cloud Server

  • Thien, Huynh Thanh (School of Electrical Engineering, University of Ulsan) ;
  • Tendeng, Rene (School of Electrical Engineering, University of Ulsan) ;
  • Vu-Van, Hiep (School of Electrical Engineering, University of Ulsan) ;
  • Koo, Insoo (School of Electrical Engineering, University of Ulsan)
  • Received : 2017.10.16
  • Accepted : 2018.03.22
  • Published : 2018.03.31
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Abstract

In cognitive radio (CR), spectrum sensing is an essential function since secondary users (SUs) must determine whether the primary user (PU) is utilizing the channel or not, and furthermore, SUs opportunistically access the licensed channel when the PU is absent. In this paper, spectrum sensing is implemented by energy detection, and a software-defined radio testbed is built to evaluate sensing performance by energy detection in a real environment. In particular, the testbed was built based on the GNU's Not Unix (GNU) Radio software platform and Universal Software Radio Peripheral National Instruments 2900 devices. More specifically, a new block of energy detection is developed by using an out-of-tree module from GNU Radio. To successfully integrate CR into the cloud computing paradigm, we also implement cloud computing-based spectrum sensing by utilizing a cloud server with ThingSpeak, such that we can store, process, and share the sensing information more efficiently in a centralized way in the cloud server.

Keywords

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Fig. 1. ThingSpeak with a cloud interface.

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Fig. 2. Schematic diagram of the system.

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Fig. 3. PU transmitter and SU receiver with USRP hardware and GNU

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Fig. 4. The PU transmitter with OFDM modulation in the GNU Radio Companion (GRC).

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Fig. 5. The SU receiver with energy detection and data uploading function blocks in the GNU Radio Companion (GRC).

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Fig. 6. The Detect upload function block.

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Fig. 7. Transmitted power at the PU transmitter.

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Fig. 8. The received signal power at center frequency fc = 2.48 GHz when

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Fig. 9. The received signal power at center frequency fc = 2.48 GHz when

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Fig. 10. The probability of detection according to the number of sensing

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Fig. 11. The probabilities of detection and false alarm based on SNR.

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Fig. 12. The probability of detection uploaded via ThingSpeak to the cloud.

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Fig. 13. The spectrum sensing data received from the cloud by the end

Table 1. Parameters for the transmitter and receiver

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References

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