WiFi leakage detection in LTE downlink for in-device interference avoidance

WiFi leakage detection in LTE downlink for in-device interference avoidance

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To allow users to access networks ubiquitously, more user equipments contain multiple radio transceivers. Because of the proximity of transmit and receive frequencies of in-device wireless technologies, the transmitter (TX) of one in-device wireless technology can interfere with the reception of another technology within the same device, and the spurious emissions of the aggressor TX may result in unacceptable interference at the victim receiver. Hence, it is crucial to detect the in-device interference and adapt strategies to avoid harm. Here, we consider one scenario in 3GPP discussions: the co-existence of Long-Term Evolution (LTE) with WiFi. Different from traditional spectrum sensing which detects signal in the background of Gaussian noise, the WiFi leakage detection in this study aims to detect WiFi leakage in the presence of both LTE signal and noise. Furthermore, most of the WiFi signal features are lost in the WiFi leakage because of the LTE RX filtering which makes the WiFi leakage problem very challenging. By identifying unique features of the WiFi leakage, we investigate this problem thoroughly and propose a suite of detection algorithms, including energy-ratio-based, entropy ratio-based and cyclostationary analysis-based algorithms. Simulations under different scenarios are presented to illustrate the effectiveness and efficiency of the proposed techniques.


    1. 1)
      • 1. Baghel, S., Ingale, M., Goyal, G.: ‘Coexistence possibilities of LTE with ISM technologies and GNSS’. National Conf. on Communications (NCC), January 2011, pp. 15.
    2. 2)
      • 2. ‘Evolved universal terrestrial radio access (E-UTRA); study on signalling and procedure for interference avoidance for in-device coexistence’. 3GPP TR 36.816, 2011.
    3. 3)
    4. 4)
    5. 5)
    6. 6)
      • 6. ‘Evolved universal terrestrial radio access (E-UTRA); physical channels and modulation’, 3GPP TS 36.211, 2009.
    7. 7)
      • 7. ‘Supplement to IEEE Standard for Information Technology–Telecommunications and Information Exchange between Systems – Local and Metropolitan Area Networks – Specific Requirements. Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: High-Speed Physical Layer in the 5 GHz Band’, IEEE Std 802.11a, 1999.
    8. 8)
      • 8. Shi, X., de Francisco, R.: ‘Adaptive spectrum sensing for cognitive radios: an experimental approach’. IEEE Wireless Communications and Networking Conf. (WCNC), March 2011, pp. 14081413.
    9. 9)
      • 9. Chen, X., Nagaraj, S.: ‘Entropy based spectrum sensing in cognitive radio’. Wireless Telecommunications Symp. (WTS) 2008, April 2008, pp. 5761.
    10. 10)
    11. 11)
      • 11. T.M., C., J.A., T.: ‘Elements of information theory’ (John Wiley and Sons, 1991), pp. 228229.
    12. 12)
      • 12. Grimmett, G.R., Stirzaker, D.R.: ‘Probability and random processes’ (Oxford University Press, 2001, 3rd edn.).
    13. 13)
    14. 14)
    15. 15)

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