Autoencoder based communication solution enhanced performance of Body Area networks
Keywords:
WBAN, Deep Learning, AutoencoderAbstract
In recent times, the application of WBANs (Wireless Body Area Networks) has been growing significantly in many areas. However, such networks usually require high reliability of communication in the condition of highly mobile sensor nodes with strong signal attenuation under multipath transmission. Autoencoder technology has the ability to automatically encode and decode signals with high accuracy because the parameters at the receiver and transmitter are optimized synchronously through training. In this paper, we propose to use autoencoders for the transceivers in order to improve the performance of communication in WBANs.
References
J. Wang et al., “Body Area Communications: Channel modeling, Communication Systems, and EMC,” John Wiley & Sons Singapore Pte. Ltd, 2013.
K. Y. Yazdandoost et al., "Channel model for body area network (BAN)," IEEE Standards Assoc., Piscataway, NJ, USA, Tech., 2010.
J. Zhang et al., "Stability of Narrowband Dynamic Body Area Channel," IEEE Antennas and Wireless Propagation Letters, vol. 8 (2009), pp. 53-56,.
D. Miniutti et al., "Narrowband channel characterization for body area network," IEEE P802 (2008), pp. 15-08.
D. B. Smith et al., "Propagation Models for Body-Area Networks: A Survey and New Outlook," IEEE Antennas and Propagation Magazine, vol. 55, no. 5 (2013), pp. 97-117.
L. C. Tran et al., "Comprehensive Performance Analysis of Fully Cooperative Communication in WBANs," IEEE Access, vol. 4 (2016), pp. 8737-8756.
N. Dinh et al., "Controlling sequence length of DS-IR-UWB to enhance performance of multi-WBAN systems," Journal of Electrical Engineering, vol. 69 (2018), pp. 373-378.
D. Quan et al., "Performance Analysis Method for IEEE 802.15.6 Based WBANs with Adaptive BCH Code Rates," Wireless Personal Communications, vol. 94 (2017).
T. L. Chung et al., "Exact error performance analysis of binary space-time block coded cooperative communications systems in Rayleigh fading channels," in 2015 International Conference on Advanced Technologies for Communications (ATC) (2015), pp. 567-571. Nghiên cứu khoa học công nghệ
Bùi T. T. Tâm and T. X. Nam, "Học máy và khả năng ứng dụng trong các hệ thống thông tin vô tuyến," Tạp chí Nghiên cứu KH&CN quân sự, pp. pp. 11-23, 2020.
J. Jagannath et al., "Machine learning for wireless communications in the Internet of Things: A comprehensive survey," Ad Hoc Networks, vol. 93 (20119), p. 101913.
Y. B. Ian Goodfellow et al., “Deep Learning,” (2016).
F. Ullah et al., "Future of Big Data and Deep Learning for Wireless Body Area Networks," in Deep Learning: Convergence to Big Data Analytics,” Springer Singapore (2019), pp. 53-77.
T. O’Shea et al., "An Introduction to Deep Learning for the Physical Layer," IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4 (2017), pp. 563-575.
Y. Lu et al., "Deep Autoencoder Learning for Relay-Assisted Cooperative Communication Systems," IEEE Transactions on Communications, vol. 68, no. 9 (2020), pp. 5471-5488.
D. Kingma et al., "Adam: A Method for Stochastic Optimization," International Conference on Learning Representations (2014).
