# A Transformer–LSTM Hybrid Detector for OFDM-IM Signal Detection

## TL;DR

The paper introduces a new deep learning detector for OFDM-IM systems that combines Transformers and LSTMs to improve signal detection accuracy.

## Contribution

A novel hybrid Transformer–LSTM detector is proposed for OFDM-IM systems, reformulating signal detection as a sequence prediction task.

## Key findings

- The FullTrans-IM detector outperforms conventional methods in bit error rate performance under Rayleigh fading channels.
- The sequence prediction approach improves detection accuracy and robustness by learning channel characteristics and modulation patterns.

## Abstract

This paper addresses the signal detection problem in orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems using deep learning (DL) techniques. In particular, a DL-based detector termed FullTrans-IM is proposed, which integrates the Transformer architecture with long short-term memory (LSTM) networks. Unlike conventional methods that treat signal detection as a classification task, the proposed approach reformulates it as a sequence prediction problem by exploiting the sequence modeling capability of the Transformer’s decoder rather than relying solely on the encoder. This formulation enables the detector to effectively learn channel characteristics and modulation patterns, thereby improving detection accuracy and robustness. Simulation results demonstrate that the proposed FullTrans-IM detector achieves superior bit error rate (BER) performance compared with conventional methods such as zero-forcing (ZF) and existing DL-based detectors under Rayleigh fading channels.