Power Optimization and Deep Learning for Channel Estimation of Active IRS-Aided IoT

TL;DR

This paper investigates channel estimation in active IRS-aided IoT networks, deriving LS estimators, optimizing power allocation, and proposing CNN-based algorithms to enhance estimation accuracy, validated by simulations.

Contribution

It introduces CNN-based direct and cascaded channel estimation algorithms and derives optimal power allocation strategies for improved channel estimation in active IRS IoT systems.

Findings

Optimal power allocation minimizes Sum-MSE.

CNN-based algorithms outperform LS and MMSE baselines.

Simulation confirms effectiveness of proposed methods.

Abstract

In this paper, channel estimation of an active intelligent reflecting surface (IRS) aided uplink Internet of Things (IoT) network is investigated. Firstly, the least square (LS) estimators for the direct channel and the cascaded channel are presented, respectively. The corresponding mean square errors (MSE) of channel estimators are derived. Subsequently, in order to evaluate the influence of adjusting the transmit power at the IoT devices or the reflected power at the active IRS on Sum-MSE performance, two situations are considered. In the first case, under the total power sum constraint of the IoT devices and active IRS, the closed-form expression of the optimal power allocation factor is derived. In the second case, when the transmit power at the IoT devices is fixed, there exists an optimal reflective power at active IRS. To further improve the estimation performance, the convolutional neural network (CNN)-based direct channel estimation (CDCE) algorithm and the CNN-based cascaded channel estimation (CCCE) algorithm are designed. Finally, simulation results demonstrate the existence of an optimal power allocation strategy that minimizes the Sum-MSE, and further validate the superiority of the proposed CDCE / CCCE algorithms over their respective traditional LS and minimum mean square error (MMSE) baselines.