Adaptive echo state network based-channel prediction algorithm for the internet of things based on the IEEE 802.11ah standard

Yongbo Sui and Hui Gao ()
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Yongbo Sui: Nanjing University of Posts and Telecommunications
Hui Gao: Nanjing University of Posts and Telecommunications

Telecommunication Systems: Modelling, Analysis, Design and Management, 2022, vol. 81, issue 4, No 1, 503-526

Abstract: Abstract The IEEE 802.11ah standard is designed to overcome the limited transmission range in conventional wireless local area network (WLAN) technologies and connect hundreds and thousands of sensors in the Internet of Things (IoT). Therefore, as a promising communication technology, it will play an important role in the IoT in the future. In this paper, to support the adaptive transmissions in the IoT, we focus on the channel prediction issue based on the IEEE 802.11ah standard. In particular, we introduce an adaptive echo state network (AESN) to predict the channel state information (CSI) in the orthogonal frequency division multiplexing (OFDM) systems of the IEEE 802.11ah standard. The AESN includes a basic echo state network (ESN) model and an adaptive elastic network. Thereinto, the adaptive elastic network is utilized to estimate the output weight matrix in the ESN. Therefore, the AESN produces the well sparse output weight matrix, enjoys the oracle property, and offers the excellent prediction performance. In the simulation section, an outdoor scenario with the Rayleigh channel and an indoor scenario with the Nakagami-m channel are considered based on the OFDM systems of the IEEE 802.11ah technology. In the simulations, we firstly use the recurrence quantification analysis (RQA) to explore the local predictability of the CSI samples. Then, the AESN is further evaluated in the one-step prediction, the multistep prediction and the robustness test. The results indicate that by using the AESN, we can effectively predict the CSI to support the adaptive transmissions in the IoT.

Keywords: Internet of things; IEEE 802.11ah standard; Channel prediction; Echo state network; OFDM systems (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11235-022-00934-x

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