LAUSR

Massive Internet of Things (IoT) is an important application scenario for the next-generation mobile communication systems. The channel estimation is crucial for the performance of massive wireless access in IoT. However, when the number of terminals is large, the huge pilot overhead may seriously increase the burden of the wireless access system. Therefore, reducing the pilot overhead while ensuring the accuracy of channel estimation is an important challenge for massive wireless access systems. In this article, we propose a massive wireless access mechanism, which greatly reduces the pilot overhead and improve the energy efficiency (EE) of terminals. Based on the self-similarity of fractal channels, the channel-state information (CSI) of a subset of terminals is sampled to estimate the CSI of all terminals, thereby reducing the pilot overhead and solving the shortage of pilot resources in massive wireless access systems. Meanwhile, the optimal transmission power of terminals based on CSI can save the energy consumption of terminals in IoT. Compared with the traditional algorithm, simulation results indicate that the proposed massive wireless access mechanism improves the EE of terminal by 340% and reduces 70% of the pilot overhead.