Joint Parallel Tabu Search Algorithm-Based Look-Up Table Design and Deep Learning-Based Signal Detection for OFDM-AIM

YILDIRIM M.

IEEE Wireless Communications Letters, vol.13, no.2, pp.575-579, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 13 Issue: 2
  • Publication Date: 2024
  • Doi Number: 10.1109/lwc.2023.3342933
  • Journal Name: IEEE Wireless Communications Letters
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Page Numbers: pp.575-579
  • Keywords: deep learning (DL), deep neural network (DNN), long short-term memory (LSTM), Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), parallel tabu search algorithm, recurrent neural network (RNN)
  • Yozgat Bozok University Affiliated: Yes

Abstract

This letter first proposes a parallel Tabu Search (PTS) algorithm-based look-up table for orthogonal frequency division multiplexing with all index modulation (OFDM-AIM) called PTS-AIM. In the conventional OFDM-AIM (referred to as AIM in this letter), each subcarrier within a subblock is mapped to the same quadrature amplitude modulation (QAM) constellation points, while in the PTS-AIM, the PTS algorithm searches the optimum constellation point for each subcarrier to improve the bit error rate (BER) performance. Secondly, a deep learning (DL)-based signal detector termed as DeepAIM is proposed, which combines long short-term memory (LSTM) algorithm and deep neural network (DNN). Finally, a novel architecture, PTS-DeepAIM, is designed, where PTS-AIM and DeepAIM schemes are considered together. The simulation results show that the proposed PTS-DeepAIM outperforms AIM in BER performance and computation time, thanks to the benefit of the unified PTS-based look-up table design and DL-based signal detection architecture.