Deep Learning-Based End-to-End Design for OFDM Systems With Hardware Impairments

Cheng Yu Wu, Yu Kai Lin, Chun Kuan Wu, Chia Han Lee*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Orthogonal frequency-division multiplexing (OFDM) is a key technology for cellular and Wi-Fi systems, but its performance may be degraded by hardware impairments. Existing works focus mostly on single hardware impairment in OFDM systems, without considering the joint effect of hardware impairments on the entire system. In this paper, hardware impairments including nonlinear power amplification, clipping, in-phase/quadrature-phase (IQ) imbalance, phase noise, carrier frequency offset, and sampling clock offset in OFDM systems are simultaneously considered. We propose end-to-end deep learning-based designs, which jointly optimize transmitter and receiver, to effectively mitigate the performance loss due to hardware impairments. For single-antenna systems and 2× 2 multiple-input and multiple-output (MIMO) systems, the proposed design featuring the dense layer neural network (DLNN) significantly outperforms traditional impairment-mitigating methods under both the additive white Gaussian noise (AWGN) channel and the Rayleigh fading channel. Meanwhile, the complexity of the proposed scheme is six times smaller. For 2× 4 MIMO systems, the proposed design featuring the residual dense convolution dense neural network (ResNet-DCDNN) outperforms the traditional methods by a large margin. Additionally, transfer learning is applied to effectively address the issue of time-varying impairment levels.

Original languageEnglish
Pages (from-to)2468-2482
Number of pages15
JournalIEEE Open Journal of the Communications Society
StatePublished - 2023


  • Deep learning
  • end-to-end design
  • hardware impairments
  • multiple antennas
  • orthogonal frequency-division multiplexing (OFDM)
  • transfer learning


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