Hong Huang, Decheng Wu, Qiangqiang Hu, Guanghua Yu, Jinhai Yang, Jianchen Zhu, Xue Liu, Dapeng Wu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

The beyond fifth-generation (B5G) cellular internet of things (IoT) network is required to support low-power and wide-coverage wireless access for a massive number of devices. However, the severe co-channel interference caused by massive access decreases the reliability and coverage. To solve this problem, we design a large intelligent reflecting surface (IRS) aided massive access framework, including channel estimation and information transmission. Furthermore, we analyze the performance of the proposed framework, and reveal the role of the large IRS. It is found that the large IRS is beneficial to improve the performance of edge-devices, and thus enhance the coverage. Moreover, we find that the refection coefficient should be carefully chosen according to channel conditions and system parameters to improve the performance.

In this article, we apply the large intelligent reflecting surface (IRS) technique in beyond fifth-generation (B5G) cellular Internet of Things (IoT) to satisfy the requirements of massive connectivity, low power, and wide coverage. First, we design a framework for the large IRS-aided B5G cellular IoT, including channel estimation, uplink data transmission, and downlink data transmission. Then, we analyze the performance of the proposed framework, and reveal the impacts of key parameters of the large IRS on the spectral efficiency. Next, we propose a low-complexity time-length allocation algorithm to minimize the total energy consumption of B5G cellular IoT. Finally, extensive simulation results validate the accuracy of the derived theoretical expressions and the effectiveness of the proposed algorithm.

In this paper, we investigate the issue of low-cost design of massive access for cellular internet of things (IoT) over spatially correlated Rician fading channels. Specifically, by exploiting a low-overhead transmission protocol, a base station (BS) equipped with a large-scale antenna array and low-resolution analog-to-digital converters (ADCs) is deployed to serve a massive number of IoT devices with low-complexity successive interference cancellation (SIC) receivers. We first analyze the impacts of the low-cost design on the system performance and derive closed-form expressions for uplink and downlink spectral efficiencies of the cellular IoT. Then, for alleviating the negative impacts of the low-cost design, we propose an algorithm allocating the time for channel estimation, uplink data transmission, and downlink data transmission in a data frame. Finally, extensive simulation results confirm the effectiveness of the proposed low-cost design for the cellular IoT.