AI-empowered Channel Estimation for Block-based Active IRS-enhanced Hybrid-field IoT Network

TL;DR

This paper introduces a novel channel estimation approach for active IRS-assisted IoT networks, employing sub-blocking, optimal power allocation, and a lightweight deep learning algorithm to improve accuracy and reduce complexity.

Contribution

It proposes a sub-blocking strategy for active IRS, derives optimal power allocation, and develops a CAEformer deep learning model for efficient channel estimation in hybrid-field IoT systems.

Findings

The sub-blocking strategy simplifies NF channel modeling.

The CAEformer outperforms traditional estimation schemes.

Optimal power allocation reduces power consumption while maintaining accuracy.

Abstract

In this paper, channel estimation (CE) for uplink hybrid-field communications involving multiple Internet of Things (IoT) devices assisted by an active intelligent reflecting surface (IRS) is investigated. Firstly, to reduce the complexity of near-field (NF) channel modeling and estimation between IoT devices and active IRS, a sub-blocking strategy for active IRS is proposed. Specifically, the entire active IRS is divided into multiple smaller sub-blocks, so that IoT devices are located in the far-field (FF) region of each sub block, while also being located in the NF region of the entire active IRS. This strategy significantly simplifies the channel model and reduces the parameter estimation dimension by decoupling the high-dimensional NF channel parameter space into low dimensional FF sub channels. Subsequently, the relationship between channel approximation error and CE error with respect to the number of sub blocks is derived, and the optimal number of sub blocks is solved based on the criterion of minimizing the total error. In addition, considering that the amplification capability of active IRS requires power consumption, a closed-form expression for the optimal power allocation factor is derived. To further reduce the pilot overhead, a lightweight CE algorithm based on convolutional autoencoder (CAE) and multi-head attention mechanism, called CAEformer, is designed. The Cramer-Rao lower bound is derived to evaluate the proposed algorithm's performance. Finally, simulation results demonstrate the proposed CAEformer network significantly outperforms the conventional least square and minimum mean square error scheme in terms of estimation accuracy.