Investigation of diversity reception methods in radio channels with fading

Modern digital radio communication systems impose stringent requirements on energy and spectral efficiency under the influence of various types of interference, particularly in challenging radio wave propagation conditions. Consequently, the investigation of existing methods for operating in radio channels with fading, as well as the development of new approaches to address this challenge, remains highly relevant. The primary objective of this study is to investigate diversity reception techniques aimed at enhancing signal robustness against fading. The study examines approaches to combining known diversity methods and proposes a new modified spatial reception method. The methodology employed includes a comparative analysis of various combinations of spatial diversity reception techniques within an adaptive feedback system, based on simulations conducted in the MATLAB environment to evaluate the impact of different fading types on data transmission in a channel with feedback. The novelty of this work lies in the proposed diversity method, which involves signal combining through optimal summation in diversity reception, performed only on a selected subset of receiving antennas. This subset is determined based on channel state estimation results, as summing signals from all receiving antennas is deemed unnecessary and significantly increases complexity when the received signal quality is already high. The results demonstrate that the proposed solution offers advantages over the conventional optimal summation method by reducing computational complexity, as signal summation is limited to a portion of the receiving antennas rather than all of them. The proposed solution is particularly suitable for applications requiring simultaneous optimization of both energy efficiency and spectral efficiency in digital radio systems. Its relevance becomes especially pronounced under degraded reception conditions caused by environmental factors inducing severe fading effects.

1. Tedeev Y.R. Methods of Increasing the Noise Immunity of Digital Radio Communication Systems. In: Perspektivnye tekhnologii v sredstvakh peredachi informatsii – PTSPI'2021: materialy 14-oi mezhdunarodnoi nauchno-tekhnicheskoi konferentsii, 06–07 October 2021, Vladimir, Russia. Vladimir: Vladimir State University named after Alexander and Nikolay Stoletovs; 2021. P. 366–370. (In Russ.).

2. Al Tahar I.A., Nasir S.A.X. Algorithm for Modeling Fast Interference Fading. Proektirovanie i tekhnologiya ehlektronnykh sredstv. 2021;(2):52–54. (In Russ.).

3. Samoylov A.G. Simulator of Interference Fading of Signals in Multipath Radio Channels. Proektirovanie i tekhnologiya ehlektronnykh sredstv. 2023;(2):21–24. (In Russ.).

4. Novikov M.A., Skorohodov E.A., Vishnyakov D.Y., Bryncev E.A. Analysis of Algorithms for Spatial Separation of Signals Under Uncorrelated Multipath Fading. In: Radiotekhnicheskie sistemy: materialy mezhdunarodnoi molodezhnoi nauchno-prakticheskoi konferentsii, 26 April 2019, Yaroslavl, Russia. Yaroslavl: P.G. Demidov Yaroslavl State University; 2020. P. 126–135. (In Russ.).

5. Keti F. Diversity Techniques in Wireless Communication Systems: A Review. European Journal of Theoretical and Applied Sciences. 2023;1(6):431–440. https://doi.org/10.59324/ejtas.2023.1(6).42

6. Hawaibam S., Singh A.D. Performance Comparison of L-SC and L-MRC over Various Fading Channels. In: ICDEC 2022: Proceedings of International Conference on Data, Electronics and Computing, 07–09 September 2022, Shillong, Meghalaya, India. Singapore: Springer; 2023. P. 467–477. https://doi.org/10.1007/978-981-99-1509-5_43

7. Saleh H.H., Hasson S.T. Improving Communication Reliability in Vehicular Networks Using Diversity Techniques. Journal of Computational and Theoretical Nanoscience. 2019;16(3):838–844. https://doi.org/10.1166/jctn.2019.7963

8. Samoylov S.A., Al Tahar I.A., Tsapalova A.S. Modeling Algorithms for Processing Diverse Signals. In: Sovremennye tekhnologii v nauke i obrazovanii – STNO-2022: sbornik trudov V Mezhdunarodnogo nauchno-tekhnicheskogo foruma: Volume 1, 02–04 March 2022, Ryazan, Russia. Ryazan: Ryazan State Radio Engineering University; 2022. P. 177–182. (In Russ.).

9. Alcaraz López O.L., Alves H., Latva-aho M. Joint Power Control and Rate Allocation Enabling Ultra-Reliability and Energy Efficiency in SIMO Wireless Networks. IEEE Transactions on Communications. 2019;67(8):5768–5782. https://doi.org/10.1109/TCOMM.2019.2914682

10. Feng X., Tian F., Wang J., et al. A Survey on Maximum Ratio Combination: Applications, Evaluation and Future Directions. Electronics. 2024;13(15). https://doi.org/10.3390/electronics13153087

11. Chudnov A.M., Makarov S.B., Kirik D.I. Estimation of the Efficiency of Control of the Correcting Ability of the Code When Transmitting Messages over a Non-Stationary Channel. Radioengineering. 2020;84(12):102–111. (In Russ.). https://doi.org/10.18127/j00338486-202012(24)-10

12. Brodsky M.S., Zvonarev V.V., Popov A.S. Methodology for Calculating the Number of Repetitions in the Radio Channel of Feedback Message Transmission Systems Operating Under the Influence of Multiplicative Interference. Izvestiya Instituta inzhenernoi fiziki. 2022;(2):40–42. (In Russ.).

13. Kochetova I.V., Levenets A.V. Simulation of Data Transmission System with Adaptive Selection of an Error-Correcting Code Based on Communication Channel State Assessment. Information Science and Control Systems. 2020;(4):17–24. (In Russ.).

14. Vladimirov S., Ostapchuk R., Skakunov I. Methods of Organizing of Adaptive Error Correcting Coding. Herald of SPbSUT. 2024;2(1). (In Russ.). URL: https://vestnik-sut.ru/2024-1/C02.pdf

15. Genga Yu., Oyerinde O., Versfeld J. Symbol-Level Iterative Information Set Decoding of RS Codes. Alexandria Engineering Journal. 2023;73:1–10. https://doi.org/10.1016/j.aej.2023.04.034

16. Raviv T., Schwartz A., Be'ery Ya. Deep Ensemble of Weighted Viterbi Decoders for Tail-Biting Convolutional Codes. Entropy. 2021;23(1). https://doi.org/10.3390/e23010093

17. Kumar D.D., Selvakumari R.Sh. Performance Analysis of Min-Sum Based LDPC Decoder Architecture for 5G New Radio Standards. Materials Today: Proceedings. 2022;62:4965–4972. https://doi.org/10.1016/j.matpr.2022.03.693

18. Healy C.T., Al Rawi A., Tsimenidis Ch.C. LDPC-Coded Modulation Performance Analysis and System Design. IEEE Access. 2020;8:212166–212176. https://doi.org/10.1109/ACCESS.2020.3038082

19. Hebbar S.A., Mishra R.K., Ankireddy S.K., Makkuva A.V., Kim H., Viswanath P. TinyTurbo: Efficient Turbo Decoders on Edge. In: 2022 IEEE International Symposium on Information Theory (ISIT), 26 June – 01 July 2022, Espoo, Finland. IEEE; 2022. P. 2797–2802. https://doi.org/10.1109/ISIT50566.2022.9834589

20. Xu J., Yuan Yi. Channel Coding in 5G New Radio. Boca Raton: CRC Press; 2022. 330 p. https://doi.org/10.1201/9781003336174

21. Fam K.K., Glushankov E., Vu T.D. Research on Adaptive Digital Communication Lines with Decision Feedback. Telecom IT. 2024;12(3):52–67. (In Russ.). https://doi.org/10.31854/2307-1303-2024-12-3-52-67