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The DCT Neuron for Estimation and Compensation of Amplitude Distortions in OFDM Systems

Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas

Abstract

We present a receiver-side framework for identifying amplitude distortions in frequency-selective OFDM channels. The core novelty is the use of the DCT Neuron, a compact adaptive processor based on the discrete cosine transform (DCT), to characterize the channel's nonlinear response, leveraging its properties for highly efficient estimation. Operating directly in the time domain, the method builds an accurate signal model and tracks channel variations adaptively, achieving reliable identification with as few as two OFDM symbols. The learned nonlinear response can then be exploited for predistortion and iterative decoding, enabling low-complexity, real-time adaptive compensation of complex responses in multicarrier systems.

The DCT Neuron for Estimation and Compensation of Amplitude Distortions in OFDM Systems

Abstract

We present a receiver-side framework for identifying amplitude distortions in frequency-selective OFDM channels. The core novelty is the use of the DCT Neuron, a compact adaptive processor based on the discrete cosine transform (DCT), to characterize the channel's nonlinear response, leveraging its properties for highly efficient estimation. Operating directly in the time domain, the method builds an accurate signal model and tracks channel variations adaptively, achieving reliable identification with as few as two OFDM symbols. The learned nonlinear response can then be exploited for predistortion and iterative decoding, enabling low-complexity, real-time adaptive compensation of complex responses in multicarrier systems.

Paper Structure

This paper contains 13 sections, 9 equations, 3 figures.

Table of Contents

  1. Introduction
  2. System model
  3. Nonlinear Channel Estimation
  4. Frequency-Selective Response
  5. Nonlinear Magnitude Distortion
  6. General Channel Estimation Algorithm
  7. Signal Detection
  8. Nonlinear Predistortion at the Transmitter
  9. Iterative-decoding
  10. Simulation Results
  11. Estimation of Nonlinear Frequency-Selective Channels
  12. Nonlinear Compensation in OFDM
  13. Conclusion

Figures (3)

  • Figure 1: Time-domain estimation of nonlinear frequency-selective channels for OFDM signals.
  • Figure 2: Considered nonlinear amplitude distortions and their estimated DCT responses at different SNR levels.
  • Figure 3: BER for nonlinear distortions and different decoding schemes when the estimation is performed at different SNR regimes.