Published March 29, 2023 | Version 01
Journal article Open

Improved Channel Estimation and Removal of Fading for MIMO-OFDM Systems

Creators

  • 1. E.G.S. Pillay Engineering College, Nagapattinam, India.

Contributors

Contact person:

  • 1. E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India.

Description

Many inference algorithms develop the time-domain connection of the direct by employ a Kalman filter base on a first-order (or sometimes second-order) estimate of the time-varying channel amid a norm based on link toning (CM), or on the Minimization of Asymptotic Variance (MAV). To decrease the complexity of the high-dimensional RW-KF for combined opinion of the multi-path multifaceted amplitudes, we suggest using an inferior dimensional RW-KF that estimate the compound amplitude of each path separately. We demonstrate that this amounts to a simplification of the joint multi-path Kalman increase formulation through the Woodbury’s identities. Hence, this innovative algorithm consists of a superposition of self-determining single-path single-carrier KFs, which be optimized in our earlier studies. This examination allow us to settle in the optimization to the authentic multi-path multi-carrier scenario, to afford logical formulas for the mean-square error presentation and the best tuning of the future estimator in a straight line as a function of the physical parameter of the canal (Doppler frequency, signal-to-noise-ratio, power delay profile). These logical formula are known for the first-, second-, and third-order RW models used in the KF. The future per-path KF is exposed to be as well-organized as the accurate KF (i.e., the joint multi-path KF), and outperforms the autoregressive-model-based KFs future in the literature.

Files

7478.pdf

Files (341.2 kB)

Name Size Download all
md5:d429dd40a0721751312a5b08527a4732
341.2 kB Preview Download

Additional details

References

  • [1] Y. Zhao and A. Huang (1997). A novel channel estimation method for OFDM mobile communication systems based on pilot signals and transform domain processing. In Proc. IEEE 47th Veh. Technol. Conf., 3: 2089–2093.
  • [2] M.-H. Hsieh and C.-H. Wei (1998). Channel estimation for OFDM systems based on comb-type pilot arrangement in frequency selective fading channels. IEEE Trans. Consum. Electron., 44(1): 217–225.
  • [3] S. Coleri, M. Ergen, A. Puri, and A. Bahai (2002). Channel estimation techniques based on pilot arrangement in OFDM systems. IEEE Trans. Broadcast., 48(3): 223–229.
  • [4] C. Komninakis, C. Fragouli, A. Sayed, and R. Wesel (2002). Multi-input multi-output fading channel tracking and equalization using Kalman estimation. IEEE Trans. Signal Process., 50(5): 1065–1076.
  • [5] D. Nathangashree, L. Ramachandran, S. Senthilkumar & R. Lakshmirekha (2016). PLC based smart monitoring system for photovoltaic panel using GSM technology. International Journal of Advanced Research in Electronics and Communication Engineering, 5(2): 251–255.
  • [6] S. Suganya, R. Sinduja, T. Sowmiya & S. Senthilkumar (2014). Street Light Glow on Detecting Vehicle Movement Using Sensor. International J. for Advance Research in Engineering and Technology, ICIRET–2014.
  • [7] W. Chen and R. Zhang (2004). Kalman-filter channel estimator for OFDM systems in time and frequency- selective fading environment. In Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), May 2004, 4: 377–380.
  • [8] A. Asuvaran & S. Senthilkumar (2014). Low delay error correction codes to correct stuck-at defects and soft errors. 2014 International Conference on Advances in Engineering and Technology (ICAET). doi: 10.1109/icaet. 2014.7105257.
  • [9] T. Al-Naffouri (2007). An EM-based forward-backward Kalman filter for the estimation of time-variant channels in OFDM. IEEE Trans. Signal Process., 55(7): 3924–3930.
  • [10] H. Hijazi and L. Ros (2010). Joint data QR-detection and Kalman estimation for OFDM time-varying Rayleigh channel complex gains. IEEE Trans. Commun., 58(1): 170–178.
  • [11] E. P. Simon, L. Ros, H. Hijazi, and M. Ghogho (2012). Joint carrier frequency offset and channel estimation for OFDM systems via the EM algorithm in the presence of very high mobility. IEEE Trans. Signal Process., 60(2): 754–765.
  • [12] S. Hwang, J. Um, M. Song, C. Kim, H. Park, and Y. Kim (2008). Design and verification of IEEE 802.22 WRAN physical layer. In Int. Conf. Cognit. Radio Oriented Wireless Netw Commun., Singapore.
  • [13] S. Senthilkumar, L. Ramachandran, R. S. Aarthi (2014). Pick and place of Robotic Vehicle by using an Arm based Solar tracking system. International Journal of Advanced Engineering Research and Science, 1(7): 39–43.
  • [14] M. Sohail and T. Al-Naffouri (2010). An EM based frequency domain channel estimation algorithm for multi-access OFDM systems. Elsevier Signal Process., 90(5): 1562–1572.
  • [15] Akbar Ali, R. Harish Kumar, R. Dheenathalayan, N. Prasanth, V. Parthasaradi, S. Senthilkumar, T. Senthil Kumar (2023). Audio Streaming Using Li-FI Communication. Irish Interdisciplinary Journal of Science & Research, 7(1): 1–7. doi: https://doi.org/10.46759/IIJSR.2023.7101.
  • [16] Senthilkumar Selvaraj (2013). Semi-Analytical Solution for Soliton Propagation in Colloidal Suspension. International Journal of Engineering and Technology, 5(2), Apr–May 2013.