Superimposed Pilot-based Channel Estimation for RIS-Assisted IoT Systems Using Lightweight Networks

TL;DR

This paper proposes a superimposed pilot-based channel estimation method for RIS-assisted IoT systems, utilizing lightweight neural networks to enhance efficiency and accuracy while reducing computational complexity and delay.

Contribution

It introduces a novel superimposed pilot scheme combined with neural networks at the base station for efficient channel estimation in RIS-assisted IoT systems.

Findings

Reduced computational complexity and delay.

Improved NMSE and BER performance.

Maintained accuracy with fewer training data.

Abstract

Conventional channel estimation (CE) for Internet of Things (IoT) systems encounters challenges such as low spectral efficiency, high energy consumption, and blocked propagation paths. Although superimposed pilot-based CE schemes and the reconfigurable intelligent surface (RIS) could partially tackle these challenges, limited researches have been done for a systematic solution. In this paper, a superimposed pilot-based CE with the reconfigurable intelligent surface (RIS)-assisted mode is proposed and further enhanced the performance by networks. Specifically, at the user equipment (UE), the pilot for CE is superimposed on the uplink user data to improve the spectral efficiency and energy consumption for IoT systems, and two lightweight networks at the base station (BS) alleviate the computational complexity and processing delay for the CE and symbol detection (SD). These dedicated networks are developed in a cooperation manner. That is, the conventional methods are employed to perform initial feature extraction, and the developed neural networks (NNs) are oriented to learn along with the extracted features. With the assistance of the extracted initial feature, the number of training data for network training is reduced. Simulation results show that, the computational complexity and processing delay are decreased without sacrificing the accuracy of CE and SD, and the normalized mean square error (NMSE) and bit error rate (BER) performance at the BS are improved against the parameter variance.