A Stacked Autoencoder-based Decode-and-Forward Relay Networks with I/Q Imbalance

Ankit Gupta*, Mathini Sellathurai, Tharmalingam Ratnarajah

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

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Abstract

We propose a stacked autoencoder (AE) that stacks a novel bit-wise denoising AE and a bit-wise AE for decode and forward (DF) relay network impacted by the I/Q imbalance (IQI) at all the nodes. Within the stacked AE framework, we propose block-coded modulation (BCM) and differential-BCM (d-BCM) designs depending on the availability of the channel state information (CSI) knowledge. Moreover, IQI estimation increases feedback overhead, thus, we design the stacked AE without utilizing the IQI parameters information that can generalize well on varying levels of IQI and signal-to-noise ratio, completely removing the IQI estimation overhead. By extensive evaluation, we show that the proposed stacked AE framework can remove the deteriorating impact of IQI performing similar to ideal relay networks without IQI.

Original languageEnglish
Article number4
JournalCEUR Workshop Proceedings
Volume3189
Publication statusPublished - 17 Aug 2022
Event1st International Workshop on Artificial Intelligence in Beyond 5G and 6G Wireless Networks 2022 - Padova, Italy
Duration: 21 Jul 2022 → …

Keywords

  • Autoencoder
  • block coded modulation
  • decode-and-forward
  • deep learning
  • I/Q imbalance
  • relay networks

ASJC Scopus subject areas

  • Computer Science(all)

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