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Channel Estimation using 5G Sounding Reference Signals: A Delay-Doppler Domain Approach

Danilo Lelin Li, Ramtin Rabiee, Arman Farhang

TL;DR

This paper demonstrates how to harness delay-Doppler (DD) domain processing within 5G NR frameworks to improve channel estimation and prediction in high-mobility scenarios without replacing OFDM-based waveforms. By mapping each 5G NR OFDM symbol to a DD grid using a DFT-s-OFDM receiver, the authors estimate the DD-domain channel from SRS pilots and then translate it back to the delay-time domain to enable prediction and equalization. A linear joint channel estimation and equalization method, augmented by a data-driven refinement that uses detected data as virtual pilots, yields substantial BER and NMSE gains over conventional frequency-domain estimation, and supports pilot-free data transmission after an initial SRS-based training window. The approach is designed to remain compatible with 5G NR standards and offers practical benefits for high-Doppler channels in 6G-era applications.

Abstract

Delay-Doppler multicarrier modulation (DDMC) techniques have been among the central topics of research for high-Doppler channels. However, a complete transition to DDMC-based waveforms is not yet practically feasible. This is because 5G NR based waveforms, orthogonal frequency division multiplexing (OFDM) and discrete Fourier transform-spread OFDM (DFT-s-OFDM), remain as the modulation schemes for the sixth-generation radio (6GR). Hence, in this paper, we demonstrate how we can still benefit from DD-domain processing in high-mobility scenarios using 5G NR sounding reference signals (SRSs). By considering a DFT-s-OFDM receiver, we transform each received OFDM symbol into the delay-Doppler (DD) domain, where the channel is then estimated. With this approach, we estimate the DD channel parameters, allowing us to predict the aged channel over OFDM symbols without pilots. To improve channel prediction, we propose a linear joint channel estimation and equalization technique, where we use the detected data in each OFDM symbol to sequentially update our channel estimates. Our simulation results show that the proposed technique significantly outperforms the conventional frequency-domain estimation technique in terms of bit error rate (BER) and normalized mean squared error (NMSE). Furthermore, we show that using only two slots with SRS for initial channel estimation, our method supports pilot-free detection for more than 25 subsequent OFDM symbols.

Channel Estimation using 5G Sounding Reference Signals: A Delay-Doppler Domain Approach

TL;DR

This paper demonstrates how to harness delay-Doppler (DD) domain processing within 5G NR frameworks to improve channel estimation and prediction in high-mobility scenarios without replacing OFDM-based waveforms. By mapping each 5G NR OFDM symbol to a DD grid using a DFT-s-OFDM receiver, the authors estimate the DD-domain channel from SRS pilots and then translate it back to the delay-time domain to enable prediction and equalization. A linear joint channel estimation and equalization method, augmented by a data-driven refinement that uses detected data as virtual pilots, yields substantial BER and NMSE gains over conventional frequency-domain estimation, and supports pilot-free data transmission after an initial SRS-based training window. The approach is designed to remain compatible with 5G NR standards and offers practical benefits for high-Doppler channels in 6G-era applications.

Abstract

Delay-Doppler multicarrier modulation (DDMC) techniques have been among the central topics of research for high-Doppler channels. However, a complete transition to DDMC-based waveforms is not yet practically feasible. This is because 5G NR based waveforms, orthogonal frequency division multiplexing (OFDM) and discrete Fourier transform-spread OFDM (DFT-s-OFDM), remain as the modulation schemes for the sixth-generation radio (6GR). Hence, in this paper, we demonstrate how we can still benefit from DD-domain processing in high-mobility scenarios using 5G NR sounding reference signals (SRSs). By considering a DFT-s-OFDM receiver, we transform each received OFDM symbol into the delay-Doppler (DD) domain, where the channel is then estimated. With this approach, we estimate the DD channel parameters, allowing us to predict the aged channel over OFDM symbols without pilots. To improve channel prediction, we propose a linear joint channel estimation and equalization technique, where we use the detected data in each OFDM symbol to sequentially update our channel estimates. Our simulation results show that the proposed technique significantly outperforms the conventional frequency-domain estimation technique in terms of bit error rate (BER) and normalized mean squared error (NMSE). Furthermore, we show that using only two slots with SRS for initial channel estimation, our method supports pilot-free detection for more than 25 subsequent OFDM symbols.
Paper Structure (10 sections, 30 equations, 6 figures, 1 algorithm)

This paper contains 10 sections, 30 equations, 6 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Time-Frequency to DD Domain
  4. Channel Estimation in DD Domain
  5. Channel Prediction
  6. MMSE-based Channel Equalization
  7. Proposed Data-driven Channel Estimation and Equalization Technique
  8. Numerical Results
  9. Conclusion
  10. Acknowledgment

Figures (6)

  • Figure 1: SRS slot structure.
  • Figure 2: NMSE as a function of SNR for v$=500$ km/h.
  • Figure 3: NMSE as a function of relative speed at SNR of 20 dB.
  • Figure 4: BER comparison between frequency-domain and DD domain channel estimation of data symbols without pilots.
  • Figure 5: Average BER performance for each transmitted slot.
  • ...and 1 more figures