Improving NB-IoT performance in weak coverage areas with CBSTO polar coding and LMMSE channel estimation

Abstract

The Narrowband Internet of Things (NB-IoT) technology is considered as an attractive option for IoT applications due to its ability in offering better connectivity in weak coverage areas. However, current research on NB-IoT systems primarily focuses on improving its distribution network, while neglecting the importance of channel condition estimation. To overcome these shortcomings, this article proposes a joint channel coding as well as channel estimation approach that aims to enhance the reliability and efficiency of data transmission over wireless channels. The proposed concatenation technique incorporates both polar coded data transfer and channel estimation procedures, which increases the system's reliability and efficiency even under immoral radio coverage conditions. The system utilizes the crossover boosted sooty tern optimization (CBSTO) based polar coding algorithm to design polar code parameters, resulting in improved error correction performance. Additionally, DNN-based linear minimum mean square error (LMMSE) channel estimations are performed at receiver side to ensure accurate channel equalization and decoding of received data bits. The simulation experiment conducted to validate the proposed NB-IoT system demonstrates its improved error correction performance and high throughput compared to other methods. The proposed scheme could help enhance the efficiency and reliability of NB-IoT systems, particularly in weak coverage areas. This paper highlights the importance of considering channel condition estimation in the development of NB-IoT systems, which could have significant implications for the future of IoT applications.