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Compressed sensing (CS) based channel estimation methods can effectively acquire channel state information for Massive MIMO wireless powered communication networks. In order to solve the problem that the existing sparsity-based adaptive matching pursuit (SAMP) channel estimation algorithm is unstable under low signal to noise ratio (SNR), an optimized adaptive matching pursuit (OAMP) algorithm is proposed in this paper. First, the channel is pre-estimated. Next, the energy entropy-based order determination is raised to optimize the reconstruction performance of the algorithm. Then, a staged adaptive variable step size method is put forward to further promote the accuracy of channel estimation. Finally, theoretical analysis and simulation results demonstrate that the proposed OAMP algorithm improves the accuracy at the expense of a small amount of time complexity, does not require a priori knowledge of sparsity and its comprehensive performance is superior to other existing channel estimation algorithms.INDEX TERMS Massive MIMO, wireless powered communication networks, sparse channel estimation, sparsity-based adaptive matching pursuit, energy entropy-based order determination, staged adaptive variable step size.

As a promising wireless communication technology, the IEEE802.11ah standard is designed to connect various sensors in the Internet of Things (IoT) in future. It is important to investigate adaptive transmission in the IEEE802.11ah standard. However, exact channel state information (CSI) is required. Channel prediction is an available approach. Therefore, an adaptive elastic echo state network (AEESN) for channel prediction in the IEEE802.11ah standard-based orthogonal frequency division multiplexing (OFDM) system is introduced in this paper. The AEESN includes two key components, a basic echo state network and an adaptive elastic network. The latter is imported to overcome collinearity problems due to vast neurons in the former and to avoid ill-conditioned solutions when estimating output weights in the former. Moreover, the latter can produce sparse output weights, which reduces memory storage requirements. To evaluate system performances, 1MHz and 2MHz bandwidth cases with specified parameters are tested. One-step prediction, multi-step prediction and robustness are evaluated for various signal to noise ratios (SNRs). The results indicate that the AEESN not only offers satisfactory prediction performance, but also effectively avoids ill-conditioned solutions and produces sparse output weights. Therefore, it can assure adaptive IoT communication. INDEX TERMS IEEE802.11ah standard, OFDM system, channel prediction, echo state network, adaptive elastic network.

This paper presents a transmission tower tilt angle prognosis method based on solar-powered LoRa sensor node and sliding XGBoost predictor. The proposed LoRa sensor node, which consists of solar panel, LoRa radio frequency chip, super-capacitors, MCU, accelerometer, and gyroscope, can measure the initial tilt angle of the transmission tower and the angular rate of the transmission tower. Then, the measuring signals of transmission tower were wirelessly transmitted to the LoRa gateway and were processed online. First, the noise of the raw angular rate is reduced by using PCA (principal components analysis) method and the tilt angle of the transmission tower can be calculated by integrating the angular rate. Second, a sliding XGBoost predictor is proposed for tilt angle prognosis, which collects the training data and trains the regression model dynamically. Third, a novel parameter optimization algorithm named DCCPSO (double chain chaos particle swarm optimization) and its execution strategy are proposed to determine the values of hyper-parameters. Finally, the experimental system and the corresponding experimental results are demonstrated and discussed in detail, which shows that the proposed method is effective in transmission tower to tilt angel on-line prognosis.INDEX TERMS Transmission tower, tilt angle, LoRa, XGBoost, on-line prognosis.