Energy detector based TOA estimation for MMW systems using machine learning

Abstract60 GHz millimeter wave signals can provide precise time and multipath resolution and so have great potential for accurate time of arrival (TOA) and range estimation. To improve TOA estimation, a new energy detector based threshold selection algorithm which employs a neural network is proposed. The minimum slope, kurtosis, and skewness of the received energy block values are used to determine the normalized thresholds for different signal-to-noise ratios (SNRs). The effects of the channel and integration period are evaluated. Performance results are presented which show that the proposed approach provides better precision and is more robust than other solutions over a wide range of SNRs for the CM1.1 and CM2.1 channel models in the IEEE 802.15.3c standard.

[1]  Mohd Wazir Mustafa,et al.  New algorithm for detection and fault classification on parallel transmission line using DWT and BPNN based on Clarke's transformation , 2015, Neurocomputing.

[2]  M. Raja,et al.  Design of bio-inspired computational intelligence technique for solving steady thin film flow of Johnson–Segalman fluid on vertical cylinder for drainage problems , 2016 .

[3]  Mohsen Guizani,et al.  5G wireless backhaul networks: challenges and research advances , 2014, IEEE Network.

[4]  Dongfeng Yuan,et al.  Front-End Narrowband Interference Mitigation for DS-UWB Receiver , 2013, IEEE Transactions on Wireless Communications.

[5]  Francesco Camastra,et al.  Machine Learning for Audio, Image and Video Analysis - Theory and Applications , 2007, Advanced Information and Knowledge Processing.

[6]  Wei-Chang Liu,et al.  All-Digital Synchronization for SC/OFDM Mode of IEEE 802.15.3c and IEEE 802.11ad , 2015, IEEE Transactions on Circuits and Systems I: Regular Papers.

[7]  Minghua Chen,et al.  Beyond 2.5Gb/s photonic generation and wireless transmission of different pulse modulation formats for a high speed impulse radio UWB over fiber system , 2011, 2011 Optical Fiber Communication Conference and Exposition and the National Fiber Optic Engineers Conference.

[8]  Cheng-Xiang Wang,et al.  Spatial Spectrum and Energy Efficiency of Random Cellular Networks , 2015, IEEE Transactions on Communications.

[9]  Hirofumi Taki,et al.  Remote heartbeat monitoring from human soles using 60-GHz ultra-wideband radar , 2015, IEICE Electron. Express.

[10]  Yi Zhu,et al.  Analytical and comparative investigation of 60 GHz wireless channels , 2012, ISWPC 2012 proceedings.

[11]  Ismail Güvenç,et al.  Threshold selection for UWB TOA estimation based on kurtosis analysis , 2005, IEEE Communications Letters.

[12]  Anshul Tyagi,et al.  A Survey on Various Coherent and Non-coherent IR-UWB Receivers , 2014, Wirel. Pers. Commun..

[13]  Hao Zhang,et al.  Remotely-sensed TOA interpretation of synthetic UWB based on neural networks , 2012, EURASIP J. Adv. Signal Process..

[14]  Julien Sarrazin,et al.  TDOA estimation method using 60 GHz OFDM spectrum , 2015 .

[15]  Hsie-Chia Chang,et al.  A 7.92 Gb/s 437.2 mW Stochastic LDPC Decoder Chip for IEEE 802.15.3c Applications , 2015, IEEE Transactions on Circuits and Systems I: Regular Papers.

[16]  N. C. Karmakar,et al.  On the Detection of Frequency-Spectra-Based Chipless RFID Using UWB Impulsed Interrogation , 2012, IEEE Transactions on Microwave Theory and Techniques.

[17]  Branimir R. Vojcic,et al.  Ultra wide band wireless communications: A tutorial , 2003, Journal of Communications and Networks.

[18]  M. Zhadobov,et al.  On-Body Propagation at 60 GHz , 2013, IEEE Transactions on Antennas and Propagation.

[19]  Norman C. Beaulieu,et al.  Novel Adaptive Nonlinear Receivers for UWB Multiple Access Communications , 2013, IEEE Transactions on Wireless Communications.

[20]  Hao Zhang,et al.  Threshold Selection for Ultra-Wideband TOA Estimation Based on Skewness Analysis , 2011, UIC.

[21]  Zhang Lei,et al.  A fully integrated 60GHz four channel CMOS receiver with 7GHz ultra-wide bandwidth for IEEE 802.11ad standard , 2014, China Communications.

[22]  S. Yong,et al.  TG3c channel modeling sub-committee final report , 2007 .

[23]  Manuel Rosa-Zurera,et al.  Artificial Neural Network-Based Clutter Reduction Systems for Ship Size Estimation in Maritime Radars , 2010, EURASIP J. Adv. Signal Process..

[24]  Joongheon Kim,et al.  Fast and Low-Power Link Setup for IEEE 802.15.3c Multi-Gigabit/s Wireless Sensor Networks , 2014, IEEE Communications Letters.

[25]  Markku Renfors,et al.  Pilot-Based Synchronization and Equalization in Filter Bank Multicarrier Communications , 2010, EURASIP J. Adv. Signal Process..

[26]  Yao Sun,et al.  A Numerical Approach to Solving an Inverse Heat Conduction Problem Using the Levenberg-Marquardt Algorithm , 2014, Inverse Heat Conduction and Heat Exchangers.

[27]  Hao Zhang,et al.  Pulse waveforms for 60 GHz M-ary pulse position modulation communication systems , 2013, IET Commun..

[28]  M. García Sánchez,et al.  A Simple Model for Average Reradiation Patterns of Single Trees Based on Weighted Regression at 60 GHz , 2015, IEEE Transactions on Antennas and Propagation.

[29]  Jian Song,et al.  A survey of single and multi-hop link schedulers for mmWave wireless systems , 2015, Ad Hoc Networks.