ELM‐based timing synchronization for OFDM systems by exploiting computer‐aided training strategy

Due to the implementation bottleneck of training data collection in realistic wireless communications systems, supervised learning-based timing synchronization (TS) is challenged by the incompleteness of training data. To tackle this bottleneck, we extend the computer-aided approach, with which the local device can generate the training data instead of generating learning labels from the received samples collected in realistic systems, and then construct an extreme learning machine (ELM)-based TS network in orthogonal frequency division multiplexing (OFDM) systems. Specifically, by leveraging the rough information of channel impulse responses (CIRs), i.e., root-mean-square (r.m.s) delay, we propose the loose constraint-based and flexible constraint-based training strategies for the learning-label design against the maximum multi-path delay. The underlying mechanism is to improve the completeness of multi-path delays that may appear in the realistic wireless channels and thus increase the statistical efficiency of the designed TS learner. By this means, the proposed ELM-based TS network can alleviate the degradation of generalization performance. Numerical results reveal the robustness and generalization of the proposed scheme against varying parameters.

[1]  Javier Rodríguez-Fernández,et al.  Joint Synchronization and Compressive Channel Estimation for Frequency-Selective Hybrid mmWave MIMO Systems , 2022, IEEE Transactions on Wireless Communications.

[2]  Tingting Yang,et al.  Blind Channel Codes Recognition via Deep Learning , 2021, IEEE Journal on Selected Areas in Communications.

[3]  Chaojin Qing,et al.  Label Design-based ELM Network for Timing Synchronization in OFDM Systems with Nonlinear Distortion , 2021, 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall).

[4]  Pushpa Singh,et al.  Supervised and Unsupervised Machine Learning based Review on Diabetes Care , 2021, 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS).

[5]  H. Vincent Poor,et al.  Learning to Decode Protograph LDPC Codes , 2021, IEEE Journal on Selected Areas in Communications.

[6]  Harish Viswanathan,et al.  Toward a 6G AI-Native Air Interface , 2020, IEEE Communications Magazine.

[7]  F. Gao,et al.  DeepIoT: Deep Learning Based Symbol Detection for Spatially Undersampled Internet of Things , 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference.

[8]  Saif Khan Mohammed,et al.  OTFS Based Random Access Preamble Transmission for High Mobility Scenarios , 2020, IEEE Transactions on Vehicular Technology.

[9]  Jeffrey G. Andrews,et al.  DeepWiPHY: Deep Learning-Based Receiver Design and Dataset for IEEE 802.11ax Systems , 2020, IEEE Transactions on Wireless Communications.

[10]  Syed Ali Raza Zaidi,et al.  Fine Timing and Frequency Synchronization for MIMO-OFDM: An Extreme Learning Approach , 2020, IEEE Transactions on Cognitive Communications and Networking.

[11]  D. Vukobratović,et al.  Deep Learning-Based Packet Detection and Carrier Frequency Offset Estimation in IEEE 802.11ah , 2020, IEEE Access.

[12]  Chaojin Qing,et al.  ELM-Based Frame Synchronization in Burst-Mode Communication Systems With Nonlinear Distortion , 2020, IEEE Wireless Communications Letters.

[13]  Chaojin Qing,et al.  Deep Learning for CSI Feedback Based on Superimposed Coding , 2019, IEEE Access.

[14]  Pingzhi Fan,et al.  Channel Estimation for Orthogonal Time Frequency Space (OTFS) Massive MIMO , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[15]  Santiago Grijalva,et al.  An “On The Fly” Framework for Efficiently Generating Synthetic Big Data Sets , 2019, 2019 IEEE International Conference on Big Data (Big Data).

[16]  Zhuo Sun,et al.  Deep Learning-based Frame and Timing Synchronization for End-to-End Communications , 2019, Journal of Physics: Conference Series.

[17]  Jeng-Kuang Hwang,et al.  Efficient Detection and Synchronization of Superimposed NB-IoT NPRACH Preambles , 2019, IEEE Internet of Things Journal.

[18]  Fumiyuki Adachi,et al.  Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding , 2019, IEEE Transactions on Vehicular Technology.

[19]  Shuguang Cui,et al.  Multi-Antenna Channel Interpolation via Tucker Decomposed Extreme Learning Machine , 2018, IEEE Transactions on Vehicular Technology.

[20]  Guan Gui,et al.  Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System , 2018, IEEE Transactions on Vehicular Technology.

[21]  Jiri Blumenstein,et al.  Coarse Time Synchronization Utilizing Symmetric Properties of Zadoff–Chu Sequences , 2018, IEEE Communications Letters.

[22]  T. Charles Clancy,et al.  Over-the-Air Deep Learning Based Radio Signal Classification , 2017, IEEE Journal of Selected Topics in Signal Processing.

[23]  Geoffrey Ye Li,et al.  Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems , 2017, IEEE Wireless Communications Letters.

[24]  Kiran Karra,et al.  Learning approximate neural estimators for wireless channel state information , 2017, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).

[25]  Stephan ten Brink,et al.  Deep Learning Based Communication Over the Air , 2017, IEEE Journal of Selected Topics in Signal Processing.

[26]  Liang Gu,et al.  5G Field Trials: OFDM-Based Waveforms and Mixed Numerologies , 2017, IEEE Journal on Selected Areas in Communications.

[27]  Mahrokh G. Shayesteh,et al.  Novel Coarse Timing Synchronization Methods in OFDM Systems Using Fourth-Order Statistics , 2015, IEEE Transactions on Vehicular Technology.

[28]  Xiaoli Ma,et al.  Timing and Frequency Synchronization for OFDM Downlink Transmissions Using Zadoff-Chu Sequences , 2015, IEEE Transactions on Wireless Communications.

[29]  Linh Ngo,et al.  Synthetic data generation for the internet of things , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[30]  Mark C. Reed,et al.  Training symbol based coarse timing synchronization in OFDM systems , 2009, IEEE Transactions on Wireless Communications.

[31]  H.S. Jamadagni,et al.  Realizing a flexible constraint length Viterbi decoder for software radio on a de Bruijn interconnection network , 2008, 2008 International Symposium on System-on-Chip.

[32]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[33]  Mohamed-Slim Alouini,et al.  Effect of imperfect phase and timing synchronization on the bit-error rate performance of PSK modulations , 2005, IEEE Transactions on Communications.

[34]  Khaled Ben Letaief,et al.  A robust timing and frequency synchronization for OFDM systems , 2003, IEEE Trans. Wirel. Commun..

[35]  Daesik Hong,et al.  A novel timing estimation method for OFDM systems , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.

[36]  Zhigang Cao,et al.  Timing recovery for OFDM transmission , 2000, IEEE Journal on Selected Areas in Communications.

[37]  Donald C. Cox,et al.  Robust frequency and timing synchronization for OFDM , 1997, IEEE Trans. Commun..