Channel Estimated Modulation Techniques for Wireless Communication Systems: Review

Year : 2025 | Volume : 15 | Issue : 02 | Page : 1 10
    By

    Sarika Sudhakar Apshankar,

  • Chandani Sharma,

  1. Research Scholar, Department of Electrical Engineering, Shri Venkateshwara University, Gajraula, Uttar Pradesh, India
  2. Assistant Professor, Department of Electrical Engineering, Shri Venkateshwara University, Gajraula, Uttar Pradesh, India

Abstract

The high spectrum and energy capabilities of Multiple Input Multiple Output systems make them a propitious technology for 5th-generation wireless communication systems. The acquisition of channel information is crucial for utilizing the potential gains of the multi-modal systems. Numerous studies have established channel estimation techniques and are still in research which faced several challenges with downlink-uplink overheads, complexities, and pilot contamination. This work discusses the insights obtained from the reviewed literature which are mainly focused on the channel estimation types, modulation schemes, the advantages, and the generalized summary. The survey also interprets the limitations that need to be overcome for the accurate determination of wireless channel data. Moreover, the survey analyzed the various performance metrics employed for evaluating the channel estimation performance in the related works. The conventional methods still exhibit implications including latency, channel parameters, pilot contamination, and time-varying channels which need to be improved in further research for acquiring channel information. The study provides good insights for developing advanced and robust modulation techniques for conducting effective channel estimation in wireless systems.

Keywords: Channel estimation, multiple input multiple output, channel state information, pilot signals, orthogonal frequency division multiplexing

[This article belongs to Trends in Opto-electro & Optical Communication ]

How to cite this article:
Sarika Sudhakar Apshankar, Chandani Sharma. Channel Estimated Modulation Techniques for Wireless Communication Systems: Review. Trends in Opto-electro & Optical Communication. 2025; 15(02):1-10.
How to cite this URL:
Sarika Sudhakar Apshankar, Chandani Sharma. Channel Estimated Modulation Techniques for Wireless Communication Systems: Review. Trends in Opto-electro & Optical Communication. 2025; 15(02):1-10. Available from: https://journals.stmjournals.com/toeoc/article=2025/view=215288


References

  1.  Jiang P, Wen CK, Jin S, Li GY. Dual CNN-based channel estimation for MIMO-OFDM systems. IEEE Trans Commun. 2021 Jun 3; 69(9): 5859–72.
  2.  Kang JM, Chun CJ, Kim IM. Deep learning based channel estimation for MIMO systems with received SNR feedback. IEEE Access. 2020 Jul 2; 8: 121162–81.
  3. Kang XF, Liu ZH, Yao M. Deep learning for joint pilot design and channel estimation in MIMO- OFDM systems. Sensors. 2022 May 31; 22(11): 4188.
  4. Mashhadi MB, Gündüz D. Pruning the pilots: Deep learning-based pilot design and channel estimation for MIMO-OFDM systems. IEEE Trans Wirel Commun. 2021 Apr 21; 20(10): 6315–28.
  5. Van Chien T, Ngo HQ, Chatzinotas S, Di Renzo M, Ottersten B. Reconfigurable intelligent surface- assisted cell-free massive MIMO systems over spatially-correlated channels. IEEE Trans Wirel Commun. 2021 Dec 29; 21(7): 5106–28.
  6.  Hosney M, Selmy HA, Srivastava A, Elsayed KM. Interference mitigation using angular diversity receiver with efficient channel estimation in MIMO VLC. IEEE Access. 2020 Mar 16; 8: 54060–73.
  7. Ghermezcheshmeh M, Zlatanov N. Parametric channel estimation for LoS dominated holographic massive MIMO systems. IEEE Access. 2023 May 8; 11: 44711–24.
  8.  Zhang R, Cheng L, Zhang W, Guan X, Cai Y, Wu W, Zhang R. Channel estimation for movable- antenna MIMO systems via tensor decomposition. IEEE Wirel Commun Lett. 2024 Aug 29; 13(11): 3089–3093.
  9.  Yi X, Zhong C. Deep learning for joint channel estimation and signal detection in OFDM systems. IEEE Commun Lett. 2020 Aug 5; 24(12): 2780–4.
  10.  Essai Ali MH, Alraddady F, Al-Thunaibat MA, Elnazer S. Machine Learning-Based Channel State Estimators for 5G Wireless Communication Systems. CMES-Comput Model Eng Sci. 2023 Apr 1; 135(1): 755–778.
  11. Guo J, Chen T, Jin S, Li GY, Wang X, Hou X. Deep learning for joint channel estimation and feedback in massive MIMO systems. Digit Commun Netw. 2024 Feb 1; 10(1): 83–93.
  12. Khan I, Cheffena M, Hasan MM. Data aided channel estimation for MIMO-OFDM wireless systems using reliable carriers. IEEE Access. 2023 Apr 24; 11: 47836–47.
  13. Jeong S, Farhang A, Perović NS, Flanagan MF. Joint CFO and channel estimation for RIS-aided multi-user massive MIMO systems. IEEE Trans Veh Technol. 2023 Apr 18; 72(9): 11800–13.
  14.  Xu Y, Wang B, Song E, Shi Q, Chang TH. Low-complexity channel estimation for massive MIMO systems with decentralized baseband processing. IEEE Trans Signal Process. 2023 Jul 27; 71: 2728–43.
  15.  Lei H, Zhang J, Xiao H, Zhang X, Ai B, Ng DW. Channel estimation for XL-MIMO systems with polar-domain multi-scale residual dense network. IEEE Trans Veh Technol. 2023 Sep 1; 73(1): 1479–84.
  16.  Lu Y, Dai L. Near-field channel estimation in mixed LoS/NLoS environments for extremely large- scale MIMO systems. IEEE Trans Commun. 2023 Mar 22; 71(6): 3694–707.
  17.  Gomes PR, de Araújo GT, Sokal B, de Almeida AL, Makki B, Fodor G. Channel estimation in RIS- assisted MIMO systems operating under imperfections. IEEE Trans Veh Technol. 2023 May 26; 72(11): 14200–13.
  18.  Yang T, Maly J, Dirksen S, Caire G. Plug-in channel estimation with dithered quantized signals in spatially non-stationary massive MIMO systems. IEEE Trans Commun. 2023 Sep 22; 72(1): 387–402.
  19.  Huang C, Xu J, Xu W, You X, Yuen C, Chen Y. Low-complexity channel estimation for extremely large-scale MIMO in near field. IEEE Wirel Commun Lett. 2023 Dec 5; 13(3): 671–5.
  20.  Rahman MH, Sejan MA, Aziz MA, Tabassum R, Baik JI, Song HK. Deep learning based one bit- ADCs efficient channel estimation using fewer pilots overhead for massive MIMO system. IEEE Access. 2024 May 6; 12: 64823–64836.
  21.  Lee S, Sim D. Deep learning-based channel estimation method for MIMO systems in spatially correlated channels. IEEE Access. 2024 Jun 3; 12: 79082–79090.
  22.  Phan DK, Van Chien T. Deep learning-assisted channel estimation in frequency selective UWA communication systems. IEEE Access. 2023 Sep 1; 11: 96603–14.
  23.  Hassan HA, Mohamed MA, Shaaban MN, Ali MH, Omer OA. An efficient deep neural network channel state estimator for OFDM wireless systems. Wirel Netw. 2024 Apr; 30(3): 1441–51.
  24.  Wang R, Ren H, Pan C, Fang J, Dong M, Dobre OA. Channel estimation for RIS-aided mmWave massive MIMO system using few-bit ADCs. IEEE Commun Lett. 2023 Jan 30; 27(3): 961–5.
  25.  Wu Q, Zhou X, Wang C, Cao H. Variable pilot assisted channel estimation in MIMO-OFDM system with STBC and different modulation modes. EURASIP J Wirel Commun Netw. 2022 May 16; 2022(1): 45.

Regular Issue Subscription Review Article
Volume 15
Issue 02
Received 01/04/2025
Accepted 14/04/2025
Published 30/06/2025
Publication Time 90 Days


Login


My IP

PlumX Metrics