Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications

Optical wireless communication (OWC) is a promising technology for future wireless communications due to its potential for cost-effective network deployment and high data rate. There are several implementation issues in OWC that have not been encountered in radio frequency wireless communications. First, practical OWC transmitters need illumination control on color, intensity, luminance, and so on, which poses complicated modulation design challenges. Furthermore, signal-dependent properties of optical channels raise nontrivial challenges in both modulation and demodulation of the optical signals. To tackle such difficulties, deep learning (DL) technologies can be applied for optical wireless transceiver design. This article addresses recent efforts on DL-based OWC system designs. A DL framework for emerging image sensor communication is proposed, and its feasibility is verified by simulation. Finally, technical challenges and implementation issues for the DL-based optical wireless technology are discussed.

[1]  Deva K. Borah,et al.  A Single-Input Multiple-Output Optical System for Mobile Communication: Modeling and Validation , 2014, IEEE Photonics Technology Letters.

[2]  Murat Uysal,et al.  Survey on Free Space Optical Communication: A Communication Theory Perspective , 2014, IEEE Communications Surveys & Tutorials.

[3]  Patric R. J. Östergård,et al.  Classification of Binary Constant Weight Codes , 2010, IEEE Transactions on Information Theory.

[4]  Jiaheng Wang,et al.  Constellation Design Enhancement for Color-Shift Keying Modulation of Quadrichromatic LEDs in Visible Light Communications , 2017, Journal of Lightwave Technology.

[5]  Inkyu Lee,et al.  Deep learning based transceiver design for multi-colored VLC systems. , 2018, Optics express.

[6]  Takaya Yamazato,et al.  Technical Issues on IEEE 802.15.7m Image Sensor Communication Standardization , 2018, IEEE Communications Magazine.

[7]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[8]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[9]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[10]  Shoji Kawahito,et al.  Image-sensor-based visible light communication for automotive applications , 2014, IEEE Communications Magazine.

[11]  Xiao Ma,et al.  A Spectral-Efficient Transmission Scheme for Dimmable Visible Light Communication Systems , 2017, Journal of Lightwave Technology.

[12]  Inkyu Lee,et al.  Binary signaling design for visible light communication: a deep learning framework. , 2018, Optics express.

[13]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

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

[15]  Chang-Jun Ahn,et al.  Mobile Phone Camera-Based Indoor Visible Light Communications With Rotation Compensation , 2016, IEEE Photonics Journal.