Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
194 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

AFDM Channel Estimation in Multi-Scale Multi-Lag Channels (2405.02660v2)

Published 4 May 2024 in cs.IT, eess.SP, and math.IT

Abstract: Affine Frequency Division Multiplexing (AFDM) is a brand new chirp-based multi-carrier (MC) waveform for high mobility communications, with promising advantages over Orthogonal Frequency Division Multiplexing (OFDM) and other MC waveforms. Existing AFDM research focuses on wireless communication at high carrier frequency (CF), which typically considers only Doppler frequency shift (DFS) as a result of mobility, while ignoring the accompanied Doppler time scaling (DTS) on waveform. However, for underwater acoustic (UWA) communication at much lower CF and propagating at speed of sound, the DTS effect could not be ignored and poses significant challenges for channel estimation. This paper analyzes the channel frequency response (CFR) of AFDM under multi-scale multi-lag (MSML) channels, where each propagating path could have different delay and DFS/DTS. Based on the newly derived input-output formula and its characteristics, two new channel estimation methods are proposed, i.e., AFDM with iterative multi-index (AFDM-IMI) estimation under low to moderate DTS, and AFDM with orthogonal matching pursuit (AFDM-OMP) estimation under high DTS. Numerical results confirm the effectiveness of the proposed methods against the original AFDM channel estimation method. Moreover, the resulted AFDM system outperforms OFDM as well as Orthogonal Chirp Division Multiplexing (OCDM) in terms of channel estimation accuracy and bit error rate (BER), which is consistent with our theoretical analysis based on CFR overlap probability (COP), mutual incoherent property (MIP) and channel diversity gain under MSML channels.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (18)
  1. A. Bemani, N. Ksairi, and M. Kountouris, “AFDM: A full diversity next generation waveform for high mobility communications,” in Proc. IEEE Int. Conf. Commun. Workshops (ICC Workshops), Jun. 2021, pp. 1–6.
  2. R. Hadani, S. Rakib, M. Tsatsanis, A. Monk, A. J. Goldsmith, A. F. Molisch, and R. Calderbank, “Orthogonal time frequency space modulation,” in Proc. IEEE Wireless Commun. Net. Conf. (WCNC), Mar. 2017, pp. 1–6.
  3. X. Ouyang and J. Zhao, “Orthogonal chirp division multiplexing,” IEEE Trans. Commun., vol. 64, no. 9, pp. 3946–3957, Sep. 2016.
  4. A. Bemani, N. Ksairi, and M. Kountouris, “Affine frequency division multiplexing for next generation wireless communications,” IEEE Trans. Wireless Commun., vol. 22, no. 11, Nov. 2023.
  5. V. Savaux, “DFT-based modulation and demodulation for affine frequency division multiplexing,” Jul. 2023. [Online]. Available: http://dx.doi.org/10.36227/techrxiv.23804055.v1
  6. Y. Tang, A. Zhang, M. Wen, Q. Wang, F. Ji, and J. Wen, “Time and frequency offset estimation and intercarrier interference cancellation for AFDM systems,” 2023. [Online]. Available: https://arxiv.org/abs/2310.07141
  7. H. Yin and Y. Tang, “Pilot aided channel estimation for AFDM in doubly dispersive channels,” in Proc. IEEE Int. Conf. Commun. in China (ICCC), 2022, pp. 308–313.
  8. W. Benzine, A. Bemani, N. Ksairi, and D. Slock, “Affine frequency division multiplexing for communications on sparse time-varying channels,” 2023. [Online]. Available: https://arxiv.org/abs/2306.07765
  9. Y. Ni, Z. Wang, P. Yuan, and Q. Huang, “An AFDM-based integrated sensing and communications,” 2022. [Online]. Available: https://arxiv.org/abs/2208.13430
  10. A. Bemani, N. Ksairi, and M. Kountouris, “Integrated sensing and communications with affine frequency division multiplexing,” IEEE Wireless Commun. Lett., p. 1–1, Feb. 2024.
  11. W. Xu et al., “Marine information technology: the best is yet to come,” Front. Inf. Technol. Electron. Eng., vol. 19, no. 8, pp. 947–950, Aug. 2018.
  12. W. Li and J. C. Preisig, “Estimation of rapidly time-varying sparse channels,” IEEE J. Ocean. Eng, vol. 32, no. 4, pp. 927–939, Oct. 2007.
  13. C. R. Berger, S. Zhou, J. C. Preisig, and P. Willett, “Sparse channel estimation for multicarrier underwater acoustic communication: From subspace methods to compressed sensing,” IEEE Trans. Signal Proccessing, vol. 58, no. 3, pp. 1708–1721, Mar. 2010.
  14. T. Xu, Z. Tang, G. Leus, and U. Mitra, “Multi-rate block transmission over wideband multi-scale multi-lag channels,” IEEE Trans. Signal Proccessing, vol. 61, no. 4, pp. 964–979, Feb. 2013.
  15. Y. Zhao, H. Yu, G. Wei, F. Ji, and F. Chen, “Parameter estimation of wideband underwater acoustic multipath channels based on fractional fourier transform,” IEEE Trans. Signal Proccessing, vol. 64, no. 20, pp. 5396–5408, Oct. 2016.
  16. S. Beygi and U. Mitra, “Multi-scale multi-lag channel estimation using low rank approximation for OFDM,” IEEE Trans. Signal Proccessing, vol. 63, no. 18, pp. 4744–4755, Sep. 2015.
  17. J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Trans. Infomation Theory, vol. 53, no. 12, pp. 4655–4666, Dec. 2007.
  18. T. T. Cai and L. Wang, “Orthogonal matching pursuit for sparse signal recovery with noise,” IEEE Trans. Infomation Theory, vol. 57, no. 7, pp. 4680–4688, Jul. 2011.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now