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.

IEEE 802.11ah 标准旨在克服传统无线局域网 (WLAN) 技术的有限传输范围，并连接物联网 (IoT) 中的成百上千个传感器。因此，作为一种很有前途的通信技术，它将在未来的物联网中发挥重要作用。在本文中，为了支持物联网中的自适应传输，我们关注基于 IEEE 802.11ah 标准的信道预测问题。特别是，我们引入了自适应回波状态网络 (AESN) 来预测 IEEE 802.11ah 标准的正交频分复用 (OFDM) 系统中的信道状态信息 (CSI)。AESN 包括一个基本的回声状态网络 (ESN) 模型和一个自适应弹性网络。其中，自适应弹性网络用于估计 ESN 中的输出权重矩阵。因此，AESN 产生了良好的稀疏输出权重矩阵，享有预言机属性，并提供了优异的预测性能。在模拟部分，使用瑞利通道的室外场景和使用 Nakagami- 的室内场景m信道是基于 IEEE 802.11ah 技术的 OFDM 系统考虑的。在模拟中，我们首先使用递归量化分析（RQA）来探索 CSI 样本的局部可预测性。然后，在一步预测、多步预测和鲁棒性测试中进一步评估AESN。结果表明，通过使用AESN，我们可以有效地预测CSI，以支持物联网中的自适应传输。