# IMNet: A Learning Based Detector for Index Modulation Aided MIMO-OFDM Systems

arXiv: https://arxiv.org/abs/1911.04133 · HTML: https://mlsanity.com/paper/1911.04133/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering](https://mlsanity.com/paper/2103.03390/) (2021)

## Abstract (arXiv)

Index modulation (IM) brings the reduction of power consumption and complexity of the transmitter to classical multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. However, due to the introduction of IM, the complexity of the detector at receiver is greatly increased. Furthermore, the detector also requires the channel state information at receiver, which leads to high system overhead. To tackle these challenges, in this paper, we introduce deep learning (DL) in designing a non-iterative detector. Specifically, based on the structural sparsity of the transmitted signal in IM aided MIMO-OFDM systems, we first formulate the detection process as a sparse reconstruction problem. Then, a DL based detector called IMNet, which combines two subnets with the traditional least square method, is designed to recover the transmitted signal. To the best of our knowledge, this is the first attempt that designs the DL based detector for IM aided systems. Finally, to verify the adaptability and robustness of IMNet, simulations are carried out with consideration of correlated MIMO channels. The simulation results demonstrate that the proposed IMNet outperforms existing algorithms in terms of bit error rate and computational complexity under various scenarios.