S.N. Trushkov1, V.V. Kuptsov2, O.A. Shmonin3, K.A. Ponur4

1-4 Lobachevsky State University of Nizhny Novgorod (Nizhny Novgorod, Russia)

1 trushkovsn@gmail.com; 2 vitaliy.kuptsov.nn@yandex.ru; 3 olgsh6@yandex.ru; 4 ponur0kirill@gmail.com

This paper considers the problem of the pilot overhead reduction in Orthogonal Frequency Division Multiplexing (OFDM) communication systems with multi-port antenna arrays for massive Multiple-Input Multiple-Output (massive MIMO) operation. The efficient usage of OFDM-based systems requires the accurate channel estimation between transmitter and receiver. Conventionally it is performed using Pilot Aided Channel Estimation (PACE) when pilot (reference) signals are transmitted simultaneously with data. These signals are known at the transmitter and the receiver side. In case of multi-port antenna systems, the channel estimation must be performed for each antenna port to obtain the maximum gain in system capacity. It means that if the number of antenna ports is large the pilot overhead becomes huge and must be reduced. Two algorithms for full channel matrix recovery using channel measurements for incomplete set of active antenna ports in two-dimensional arrays were proposed. The pilot decimation is obtained by the usage of the high-resolution map that contains a priori information about the propagation channel between the transmitter and the receiver. This information consists of scattering environment and some preliminary performed measurements of full channel matrix. It is assumed that information from high-resolution map can be unambiguously matched with the receiver location. The first algorithm is based on harmonic-based model where channel coefficient is represented in a basis of harmonic signals determined by paths’ directions. It is assumed that information about channel paths is quite accurately known from the high-resolution map. The second algorithm is based on Eigen-value decomposition of channel correlation matrix that is calculated using historical channel measurements from the high-resolution map. Via numerical simulations the performance of channel recovering algorithms was estimated. It is shown that proposed algorithms provide significant pilot overhead reduction without losses in channel estimation accuracy. In addition, we analyzed complexity of proposed algorithms and considered possible ways for high-resolution map construction.

Trushkov S.N., Kuptsov V.V., Shmonin O.A., Ponur K.A. Recovering of full channel matrix based on channel measurements over incomplete set of antenna ports using high-resolution map. Radiotekhnika. 2025. V. 89. № 6. P. 90−103. DOI: https://doi.org/10.18127/j00338486-202506-09 (In Russian)

1. Fazel K., Fettweis G. Multi-carrier and spread spectrum systems. Wiley. 2003.
2. Baykal B. Blind channel estimation via combining autocorrelation and blind phase estimation. IEEE Transactions on Circuits and Systems I: Regular Papers. 2004. V. 51. № 6. P. 1125-1131.
3. Zhang W., Gao F., Yin Q. Blind channel estimation for MIMO-OFDM systems with low order signal constellation. IEEE Communication Letters. 2015. V. 19. № 3. P. 499-502.
4. Sutar M.B., Patil V.S. LS and MMSE estimation with different fading channels for OFDM system. 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA). Coimbatore/ India. 2017. P. 740-745.
5. Sterba J., Kocur D. Pilot symbol aided channel estimation for OFDM system in frequency selective Rayleigh fading channel. 2009 19th International Conference Radioelektronika. Bratislava. Slovakia. 2009. P. 77-80.
6. Zourob M., Rao R. Lower-complexity Wiener filtering for UE-RS channel estimation in LTE DL system. 2017 International Symposium on Wireless Systems and Networks (ISWSN). Lahore. Pakistan. 2017. P. 1-5.
7. Auer G. 3D MIMO-OFDM channel estimation. IEEE Transactions on Communications. 2012. V. 60. № 4. P. 972-985.
8. Lee B.M., Kim Y. Beam grouping based RS resource reuse and de-contamination in large scale MIMO systems. Appl. Sci. 2017.
9. Adhikary A., Nam J., Ahn J.Y., Caire G. Joint spatial division and multiplexing - the large-scale array regime. IEEE Transactions on Information Theory. 2013. V. 59. № 10. P. 6441-6463.
10. Dong P., Zhang H., Li G.Y. Machine learning prediction based CSI acquisition for FDD massive MIMO downlink. 2018 IEEE Global Communications Conference (GLOBECOM). Abu Dhabi. 2018. P. 1-6.
11. Zhang Z., Zhang J., Zhang Y., Yu L., Liu G. Ai-based time-, frequency-, and space-domain channel extrapolation for 6G: opportunities and challenges. IEEE Vehicular Technology Magazine. 2023. V. 18. № 1. P. 29-39.
12. Yang Y., Zhang S., Gao F., Xu C., Ma J., Dobre O.A. Deep learning based antenna selection for channel extrapolation in FDD massive MIMO. 2020 International Conference on Wireless Communications and Signal Processing (WCSP). Nanjing. 2020. P. 182-187.
13. Choi H., Choi J. Downlink extrapolation for FDD multiple antenna systems through neural network using extracted uplink path gains. IEEE Access. 2020. V. 8. P. 67100-67111.
14. Han Y., Hsu T.H., Wen C.K., Wong K.K., Jin S. Efficient downlink channel reconstruction for FDD multi-antenna systems. IEEE Transactions on Wireless Communications. 2019. V. 18. № 6. P. 3161-3176.
15. Han Y., Jin S., Li. X., Wen C.K., Quek T.Q.S. Multi-domain channel extrapolation for FDD massive MIMO systems. IEEE Transactions on Communications. 2021. V. 69. № 12. P. 8534-8550.
16. Godara L.C. Smart antennas. CRC Press. 2004.
17. Stoica P., Moses R. Spectral analysis of signals. N.J.: Prentice Hall Inc. 2005.
18. Shmidt R.O. Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propagat. 1986. V. 34. P. 276-280.
19. Jaeckel S., Raschkowski L., Borner K.B., Thiele L., Burkhardt F., Eberlein E. QuaDRiGa - quasi deterministic radio channel generator, user manual and documentation. Fraunhofer Henrich Hertz Institue, Tech Rep. v2.0.0. 2017. Quadriga-manual-ref.
20. Jaeckel S., Raschkowski L., Borner K.B., Thiele L. QuaDRiGa: A 3-D multi-cell channel model with time evolution for enabling virtual field trials. IEEE Trans. Antennas Propagat. 2014. T. 62. P. 3242-3256.