Joint Transceiver Optimization for Wireless Communication PHY Using Neural Network

Deep learning has a wide application in the area of natural language processing and image processing due to its strong ability of generalization. In this paper, we propose a novel neural network structure for jointly optimizing the transmitter and receiver in communication physical layer under fading channels. We build up a convolutional autoencoder to simultaneously conduct the role of modulation, equalization, and demodulation. The proposed system is able to design different mapping scheme from input bit sequences of arbitrary length to constellation symbols according to different channel environments. The simulation results show that the performance of neural network-based system is superior to traditional modulation and equalization methods in terms of time complexity and bit error rate under fading channels. The proposed system can also be combined with other coding techniques to further improve the performance. Furthermore, the proposed system network is more robust to channel variation than traditional communication methods.

[1]  Jacob Ziv,et al.  On functionals satisfying a data-processing theorem , 1973, IEEE Trans. Inf. Theory.

[2]  Stephan ten Brink,et al.  Scaling Deep Learning-Based Decoding of Polar Codes via Partitioning , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[3]  Cong Shen,et al.  An Iterative BP-CNN Architecture for Channel Decoding , 2017, IEEE Journal of Selected Topics in Signal Processing.

[4]  Yair Be'ery,et al.  Learning to decode linear codes using deep learning , 2016, 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[5]  A. Lee Swindlehurst,et al.  A vector-perturbation technique for near-capacity multiantenna multiuser communication-part I: channel inversion and regularization , 2005, IEEE Transactions on Communications.

[6]  Ephraim Zehavi,et al.  8-PSK trellis codes for a Rayleigh channel , 1992, IEEE Trans. Commun..

[7]  Yann LeCun,et al.  Generalization and network design strategies , 1989 .

[8]  Theodore S. Rappaport,et al.  Millimeter Wave Mobile Communications for 5G Cellular: It Will Work! , 2013, IEEE Access.

[9]  Namyoon Lee,et al.  MIMO systems with low-resolution ADCs: Linear coding approach , 2016, 2017 IEEE International Conference on Communications (ICC).

[10]  Jakob Hoydis,et al.  An Introduction to Deep Learning for the Physical Layer , 2017, IEEE Transactions on Cognitive Communications and Networking.

[11]  Sreeram Kannan,et al.  Communication Algorithms via Deep Learning , 2018, ICLR.

[12]  Cheng-Xiang Wang,et al.  5G Ultra-Dense Cellular Networks , 2015, IEEE Wireless Communications.

[13]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[14]  Namyoon Lee,et al.  Blind detection for MIMO systems with low-resolution ADCs using supervised learning , 2016, 2017 IEEE International Conference on Communications (ICC).

[15]  Ami Wiesel,et al.  Deep MIMO detection , 2017, 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[16]  Peter F. Driessen,et al.  On the capacity formula for multiple input-multiple output wireless channels: a geometric interpretation , 1999, 1999 IEEE International Conference on Communications (Cat. No. 99CH36311).

[17]  Timothy J. O'Shea,et al.  Deep Learning Based MIMO Communications , 2017, ArXiv.

[18]  Andrea J. Goldsmith,et al.  Detection Algorithms for Communication Systems Using Deep Learning , 2017, ArXiv.

[19]  I. Daubechies,et al.  PAINLESS NONORTHOGONAL EXPANSIONS , 1986 .

[20]  Sudharman K. Jayaweera,et al.  A Survey on Machine-Learning Techniques in Cognitive Radios , 2013, IEEE Communications Surveys & Tutorials.

[21]  Simeon Furrer,et al.  Adaptive bit loading for wireless OFDM systems , 2001, 12th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications. PIMRC 2001. Proceedings (Cat. No.01TH8598).

[22]  Kiran Karra,et al.  Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention , 2016, 2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[23]  B. Floch,et al.  Coded orthogonal frequency division multiplex , 1995 .

[24]  Giulio Colavolpe,et al.  On reliable communications over channels impaired by bursty impulse noise , 2008, ISPLC 2008.

[25]  Tao Jiang,et al.  Deep learning for wireless physical layer: Opportunities and challenges , 2017, China Communications.

[26]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

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

[28]  Stephan ten Brink,et al.  On deep learning-based channel decoding , 2017, 2017 51st Annual Conference on Information Sciences and Systems (CISS).

[29]  Enrico Paolini,et al.  Low-Density Parity-Check (LDPC) Codes , 2013 .

[30]  P. Viswanath,et al.  Fundamentals of Wireless Communication: The wireless channel , 2005 .

[31]  Mohamed Ibnkahla,et al.  Applications of neural networks to digital communications - a survey , 2000, Signal Process..