Modeling of co-channel interference in bluetooth low energy based on measurement data

The intense use of the 2.4 GHz ISM band by several wireless technologies has resulted in increased co-channel interference between networks operating in this frequency band. The aim of this paper is to investigate modeling techniques of co-channel interference affecting bluetooth low energy devices. The models are derived from recorded interference. Two types of models are introduced: a time domain model utilizing IQ data as reference and a spectrum-based model in which the reference signal is captured in frequency domain by a real-time spectrum analyzer. The recorded interference is also used as a reference to analyze the accuracy of proposed models. The proposed IQ-based model shows a degradation in performance for the environments with dominant bluetooth interfering signals. The frequency-based model not only addresses this problem, but also results in a huge data decimation in recording the interference.

[1]  J. Lago-Fernández,et al.  DVB-T Modelling impulsive interference in — statistical analysis , test waveforms and receiver performance , 1998 .

[2]  Kapil Gulati.  Radio frequency interference modeling and mitigation in wireless receivers , 2011 .

[3]  Ivan Howitt,et al.  Bluetooth performance in the presence of 802.11b WLAN , 2002, IEEE Trans. Veh. Technol..

[4]  Markku Renfors,et al.  Optimized FFT and filter bank based spectrum sensing for Bluetooth signal , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[5]  Kay Römer,et al.  Improving the Timeliness of Bluetooth Low Energy in Noisy RF Environments , 2019, EWSN.

[6]  Davide Dardari,et al.  Simple and accurate models for error probability evaluation of IEEE802.11 DS-SS physical interface in the presence of Bluetooth interference , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.

[7]  Konstantin Mikhaylov,et al.  Performance Evaluation of Bluetooth Low Energy Technology Under Interference , 2018, BODYNETS.

[8]  Kees Slump,et al.  Bluetooth demodulation algorithms and their performance , 2002 .

[9]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[10]  Eduardo Casilari-Pérez,et al.  Analytical and empirical evaluation of the impact of Gaussian noise on the modulations employed by Bluetooth Enhanced Data Rates , 2012, EURASIP Journal on Wireless Communications and Networking.

[11]  I. Howitt,et al.  Empirical study of IEEE 802.15.4 mutual interference issues , 2007, Proceedings 2007 IEEE SoutheastCon.

[12]  Theodore S. Rappaport,et al.  Measurements and Models of Radio Frequency Impulsive Noise for Indoor Wireless Communications , 1993, IEEE J. Sel. Areas Commun..

[13]  Majid Nabi,et al.  A Scalable and Fast Model for Performance Analysis of IEEE 802.15.4 TSCH Networks , 2019, 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC).

[14]  A. Stephens,et al.  Wi-Fi (802.11b) and Bluetooth: enabling coexistence , 2001, IEEE Netw..

[15]  Kapil Gulati,et al.  Statistical Modeling of Co-Channel Interference , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[16]  Mikko Valkama,et al.  Efficient Energy Detection Methods for Spectrum Sensing Under Non-Flat Spectral Characteristics , 2015, IEEE Journal on Selected Areas in Communications.

[17]  Luc Devroye,et al.  Sample-based non-uniform random variate generation , 1986, WSC '86.

[18]  Kihong Kim,et al.  Interference Mitigation in Wireless Communications , 2005 .