IMRecoNet: Learn to Detect in Index Modulation Aided MIMO Systems with Complex Valued Neural Networks

TL;DR

This paper introduces IMRecoNet, a novel complex-valued neural network-based detector for index modulation MIMO systems, effectively addressing detection complexity and channel estimation challenges with improved accuracy.

Contribution

It is the first to apply complex-valued neural networks to IM-MIMO detection, formulating detection as sparse reconstruction and demonstrating robustness under practical conditions.

Findings

Outperforms existing algorithms in antenna recognition accuracy.

Achieves lower bit error rates in simulations.

Demonstrates robustness with imperfect channel information.

Abstract

Index modulation (IM) reduces the power consumption and hardware cost of the multiple-input multiple-output (MIMO) system by activating part of the antennas for data transmission. However, IM significantly increases the complexity of the receiver and needs accurate channel estimation to guarantee its performance. To tackle these challenges, in this paper, we design a deep learning (DL) based detector for the IM aided MIMO (IM-MIMO) systems. We first formulate the detection process as a sparse reconstruction problem by utilizing the inherent attributes of IM. Then, based on greedy strategy, we design a DL based detector, called IMRecoNet, to realize this sparse reconstruction process. Different from the general neural networks, we introduce complex value operations to adapt the complex signals in communication systems. To the best of our knowledge, this is the first attempt that introduce complex valued neural network to the design of detector for the IM-MIMO systems. Finally, to verify the adaptability and robustness of the proposed detector, simulations are carried out with consideration of inaccurate channel state information (CSI) and correlated MIMO channels. The simulation results demonstrate that the proposed detector outperforms existing algorithms in terms of antenna recognition accuracy and bit error rate under various scenarios.