Mathematical model of the functioning of a mobile radio communication system built on software-defined means

Hryhorii Radzivilov, Dmytro Pavliuk
Abstract

The purpose of the study was to develop a generalised mathematical model of the functioning of multi-antenna radio communication systems under the influence of random and intentional interference and to analytically describe the influence of interference-noise and non-stationary factors on the security and reliability of information transmission. The work used a matrix channel model, space-time block coding, orthonormal representation of signals in a common signal space and statistical modelling of noise and interference in the coordinates of the basis. The results of the study showed that under quasi-stationarity of the channel on the interval of block transmission and orthogonality of the space-time structure, an inverse channel operator was formed. Under such conditions, linear recovery of the transmitted symbol vector and coherent summation of the useful component were ensured with statistical independence of noise components at the decoder output. The desired signal, fluctuating noise, and intentional interference had a consistent coordinate structure in the same orthonormal signal space. As a result, the quality of each antenna channel was determined by the energy balance between the desired energy and the spectral parameters of noise and interference, which determined the distance of signal points from the regions of false decision. The constellation degradation was described by the composition of geometric transformations (rotation, scaling, quadrature deformation) and stochastic perturbations (phase jitter, interference, additive noise), and the parameters of these distortions were identified by the mathematical expectation, dispersion, and covariance of the coordinates of the received symbols. The temporal nonstationarity of the channel was determined by the Doppler shift and the correlation structure of the transmission coefficient, and the elliptical geometric scattering model related the delays of multipath components to the spatial configuration of the scatterers. The practical significance of the results lay in the possibility of using the proposed model for analysis and optimisation of multi-antenna radio systems on software-defined devices in conditions of noise, interference and non-stationary channel. Its application contributed to the justification of modulation and coding parameters, as well as to increasing the reliability and interference immunity of radio communications

Keywords

space-time coding; additive noise; intentional interference; MIMO; software-defined radio; Doppler effect

Suggested citation
Radzivilov, H., & Pavliuk, D. (2026). Mathematical model of the functioning of a mobile radio communication system built on software-defined means. Machinery & Energetics, 17(1), 55-72. https://10.31548/machinery/1.2026.55
References
  1. Abdallah, M.A., Nassr, M., Anbar, M., & Alasadi, H.A.A. (2023). BER improvement in 5G MU MIMO-OFDM systems using channel coding techniques. Pure and Applied Optics, 56(1), article number 51121. doi: 10.7149/OPA.56.1.51121.
  2. Ahmed, M., Wahid, A., Laique, S.S., Khan, W.U., Ihsan, A., Xu, F., Chatzinotas, S., & Han, Z. (2023). A survey on STAR-RIS: Use cases, recent advances, and future research challenges. IEEE Internet of Things Journal, 10(16), 14689-14711. doi: 10.1109/JIOT.2023.3279357.
  3. Alqahtani, A.S., Pandiaraj, S., Alshmrany, S., Almalki, A.J., Prabhu, S., & Kumar, U.A. (2024). Enhancing MIMO-OFDM channel estimation in 5G and beyond with conditional self-attention generative adversarial networks. Wireless Networks, 30(3), 1719-1736. doi: 10.1007/s11276-023-03615-y.
  4. Amadid, J., Belhabib, A., Khabba, A., Zeroual, A., & Hassani, M.M. (2022). On channel estimation and spectral efficiency for cell‐free massive MIMO with multi‐antenna access points considering spatially correlated channels. Transactions on Emerging Telecommunications Technologies, 33(5), article number e4438. doi: 10.1002/ett.4438.
  5. Balamurugan, K., & Janakiraman, N. (2025). DVB-RCS2 satellite link optimization for efficient data transmission using cascaded coding approach in MIMO-OFDM system. Wireless Personal Communications, 144(3), 435-462. doi: 10.1007/s11277-025-11856-7.
  6. Chodisetti, L.S.S.P.K., Donga, M., Tella, P.V., Rao, K.P., Chandra, K.R., Budumuru, P.R., & Rao, C.V. (2023). Equalization based soft output data detection for massive MU-MIMO-OFDM using coordinate descent. In P. Pareek, N. Gupta & M.J.C.S. Reis (Eds.), 4th EAI international conference: Cognitive computing and cyber physical systems (pp. 173-184). Cham: Springer. doi: 10.1007/978-3-031-48891-7_14.
  7. de la Fuente, A., Interdonato, G., & Araniti, G. (2022). User subgrouping and power control for multicast massive MIMO over spatially correlated channels. IEEE Transactions on Broadcasting, 68(4), 834-847. doi: 10.1109/TBC.2022.3190990.
  8. Du, P., Zhang, C., Jing, Y., Fang, C., Zhang, Z., & Huang, Y. (2026). Jamming detection and channel estimation for spatially correlated beamspace massive MIMO. IEEE Transactions on Wireless Communications, 25, 3910-327doi: 10.1109/TWC.2025.3607091.
  9. Ge, L., Qi, C., Guo, Y., Qian, L., Tong, J., & Wei, P. (2022). Classification weighted deep neural network based channel equalization for massive MIMO-OFDM systems. Radioengineering, 31(3), 346-356. doi: 10.13164/re.2022.0346.
  10. Harkat, H., Monteiro, P., Gameiro, A., Guiomar, F., & Ahmed, H.F.T. (2022). A survey on MIMO-OFDM systems: Review of recent trends. Signals, 3(2), 359-395. doi: 10.3390/signals3020023.
  11. Iklodiya, H., & Bhanwar, M.S. (2025). Noma based communication in 5G scheme on nonlinear real signal SVM OFDM systemInternational Journal of Research & Technology, 13(3), 365-377.
  12. Islam, M., Islam, A., & Ahmed, F. (2025). A DNN-based 5G MIMO system adopting a mix of tactics. Discover Electronics, 2(1), article number 15. doi: 10.1007/s44291-025-00055-0.
  13. Kamga, G.N., Xia, M., & Aïssa, S. (2017). Spectral-efficiency analysis of regular- and large-scale (Massive) MIMO with a comprehensive channel model. IEEE Transactions on Vehicular Technology, 66(6), 4984-4996. doi: 10.1109/TVT.2016.2620489.
  14. Khudov, H., Diakonov, O., Kuchuk, N., Maliuha, V., Furmanov, K., Mylashenko, I., Olshevskyi, Y., Stetsiv, S., Solomonenko, Y., & Yuzova, I. (2021). Method for determining coordinates of airborne objects by radars with additional use of ADS-B receivers. Eastern-European Journal of Enterprise Technologies, 4(9(112)), 54-64. doi: 10.15587/1729-4061.2021.238407.
  15. Khudov, H., Kostianets, O., Kovalenko, O., Maslenko, O., & Solomonenko, Y. (2023). Using Software-Defined radio receivers for determining the coordinates of low-visible aerial objects. Eastern-European Journal of Enterprise Technologies, 4(9(124)), 61-73. doi: 10.15587/1729-4061.2023.286466.
  16. Kotb, M.M.E., Mohamed, M.R.A.H., Fahmy, A.Y.H.A., & Mohra, A.S.S.S. (2025). Fine carrier frequency offset estimation for OFDM and MIMO-OFDM systems: A comparative study. Scientific Reports, 15, article number 15622. doi: 10.1038/s41598-025-98233-3.
  17. Kumar, A., & Nanthaamornphong, A. (2025). Signal detection of massive MIMO-OTFS using DNN algorithm with diverse channel state estimation. Physics Open, 25, article number 100332. doi: 10.1016/j.physo.2025.100332.
  18. Kumar, M.P., Summaq, A., Chinnadurai, S., Kumaravelu, V.B., Selvaprabhu, P., Imoize, A.L., & Jaiswal, G. (2025). An overview of channel modeling and propagation measurements in IRS‐based wireless communication systems. In A.L. Imoize, V.B. Kumaravelu & D.-T. Do (Eds.), Reconfigurable intelligent surfaces for 6G and beyond wireless networks (pp. 435-473). Hoboken: John Wiley &Sons. doi: 10.1002/9781394250141.ch13.
  19. Ma, Y., Yang, L., & Zheng, X. (2018). A geometry-based non-stationary MIMO channel model for vehicular communications. China Communications, 15(7), 30-38. doi: 10.1109/CC.2018.8424580.
  20. Pande, M., Kulkarni, A.J., & Shastri, A.S. (2025). Multiple input multiple output schemes in 3G, 4G, and 5G networks. In Optimization methods in mobile communication systems: A machine-generated literature overview (pp. 129-220). Singapore: Springer. doi: 10.1007/978-981-95-1810-4_3.
  21. Papazafeiropoulos, A., Tran, L.-N., Abdullah, Z., Kourtessis, P., & Chatzinotas, S. (2024). Achievable rate of a STAR-RIS assisted massive MIMO system under spatially-correlated channels. IEEE Transactions on Wireless Communications, 23(2), 1550-1564. doi: 10.1109/TWC.2023.3290325.
  22. Reddy, G.K., & Sheeba, G.M. (2024). Enhancing PAPR performance in MIMO-OFDM system using hybrid optimal MMSE-MLSE equalizers. Multimedia Tools and Applications, 83(10), 28993-29013. doi: 10.1007/s11042-023-16489-1.
  23. Riabukha, V.P., Semeniaka, A.V., Katiushyn, Ye.A., & Atamanskiy, D.V. (2022). Comparative experimental investigations of adaptive and non-adaptive MTI systems in pulse radars of various applications and wave ranges. Radioelectronics and Communications Systems, 65, 165-176. doi: 10.3103/S073527272204001X.
  24. Ribeiro, L., Leinonen, M., Al-Tous, H., Tirkkonen, O., & Juntti, M. (2022). Channel charting aided pilot reuse for massive MIMO systems with spatially correlated channels. IEEE Open Journal of the Communications Society, 3, 2390-2406. doi: 10.1109/OJCOMS.2022.3225054.
  25. Shankar, R. (2023). Bi‐directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL‐C model with Rayleigh fading distribution. International Journal of Communication Systems, 36(16), article number e5585. doi: 10.1002/dac.5585.
  26. Shi, E., Zhang, J., He, R., Jiao, H., Wang, Z., Ai, B., & Ng, D.W.K. (2022). Spatially correlated reconfigurable intelligent surfaces-aided cell-free massive MIMO systems. IEEE Transactions on Vehicular Technology, 71(8), 9073-9077. doi: 10.1109/TVT.2022.3175459.
  27. Singh, A., & Saha, S. (2022). Machine/deep learning based estimation and detection in OFDM communication systems with various channel imperfections. Wireless Networks, 28(6), 2637-2650. doi: 10.1007/s11276-022-02994-y.
  28. Singh, Y. (2026). NI-USRP and ANN-based wireless real-time monitoring of liquid level using non-contact capacitive level sensor. IETE Technical Review, 43(1), 58-69. doi: 10.1080/02564602.2025.2589806.
  29. Sui, Z., Ngo, H.Q., Van Chien, T., Matthaiou, M., & Hanzo, L. (2025). RIS-assisted cell-free massive MIMO relying on reflection pattern modulation. IEEE Transactions on Communications, 73(2), 968-982. doi: 10.1109/TCOMM.2024.3446589.
  30. Xiao, J., Wang, J., Wang, Z., Wang, J., Xie, W., & Liu, Y. (2024). Multi-task learning for near/far field channel estimation in STAR-RIS networks. IEEE Transactions on Communications, 72(10), 6344-6359. doi: 10.1109/TCOMM.2024.3402619.
  31. Yang, M., Zhang, S., Shao, S., Guo, C., & Tang, W. (2017). Statistical modeling of the high altitude platform dual-polarized MIMO propagation channel. China Communications, 14(3), 43-54. doi: 10.1109/CC.2017.7897321.
  32. Ye, N., An, J., & Yu, J. (2021). Deep-learning-enhanced NOMA transceiver design for massive MTC: Challenges, state of the art, and future directions. IEEE Wireless Communications, 28(4), 66-73. doi: 10.1109/MWC.001.2000472.
  33. Zhao, L., Wang, H., Chen, J., & Meng, X. (2024). Multi-array visible-light optical generalized spatial multiplexing – multiple input multiple-output system with pearson coefficient-based antenna selection. Photonics, 11(1), article number 67. doi: 10.3390/photonics11010067.