Comparative analysis of predictive synchronization and node selection processes for a distributed antenna system on measured data
DOI:
https://doi.org/10.34185/1562-9945-5-166-2026-25Keywords:
distributed antenna system, coherent data transmission, predictive synchronization, adaptive node selection, comparative analysis, Kalman filter, consensus, UAV, DICHASUS, DeepSense 6GAbstract
This paper presents a comparative analysis of predictive time, frequency, and phase synchronization and adaptive node selection in a distributed antenna system with peer-to-peer coordination. The system under consideration comprises 32 radio nodes that coherently form an information-bearing signal for a mobile receiver under conditions where synchronization estimates become outdated due to the motion of both the radio nodes and the receiver, instability of the radio-node reference oscillators, and delays in control information exchange. The study employs measured DICHASUS radio-channel characteristics for 32 antenna elements at a carrier frequency of 1.272 GHz, UAV trajectories from the DeepSense 6G dataset, and a model of frequency and phase instability of the radio-node reference oscillators, whose parameters are determined based on Allan deviation. To compare the considered methods, a mathematical model is developed that enables prediction of the phase state of each radio node, estimation of the probability of satisfying the prescribed phase tolerance, and calculation of the expected received power while accounting for the sign of the pairwise interference contribution between radio nodes. The obtained values are then used to select the cluster of radio nodes involved in coherent formation of the information-bearing signal at the receiver location. The performance evaluation shows that predictive synchronization increases the received information-signal power by 0.88–9.43 dB compared with reactive synchronization. Selecting the radio-node cluster based on the expected received power makes it possible to preserve the power level achieved by the full system while reducing the number of active radio nodes by 17.5–30.4%. The analysis further demonstrates that the entropy-based metric does not provide an additional increase in received power, whereas the reliability constraint based on the union bound leads to an excessive reduction in cluster size under dynamic scenarios.
References
1. Mudumbai, R., Brown, D. R., Madhow, U., & Poor, H. V. (2009). Distributed transmit beamforming: Challenges and recent progress. IEEE Communications Magazine, 47(2), 102–110. https://doi.org/10.1109/MCOM.2009.4785387
2. Nanzer, J. A., Mghabghab, S. R., Ellison, S. M., & Schlegel, A. (2021). Distributed phased arrays: Challenges and recent advances. IEEE Transactions on Microwave Theory and Tech-niques, 69(11), 4893–4907. https://doi.org/10.1109/TMTT.2021.3092401
3. Shandi, N., Merlo, J. M., & Nanzer, J. A. (2025). Distributed beamforming using decen-tralized time synchronization in a six-element array [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2505.13248
4. Dula, M. J., Shandi, N., & Nanzer, J. A. (2025). Decentralized localization of distributed antenna array elements using an evolutionary algorithm. IEEE Transactions on Microwave Theory and Techniques, 73(10), 8013–8023. https://doi.org/10.1109/TMTT.2025.3564522
5. Nguyen, K. D., Lechner, G., & Ahmad, I. (2025). Zero-feedback open-loop distributed transmit beamforming. Digital Signal Processing, 156, Article 104875. https://doi.org/10.1016/j.dsp.2024.104875
6. Ventura, G., Rossi, M., & Pegoraro, J. (2026). AsyMov: Integrated sensing and communi-cations with asynchronous moving devices. IEEE Transactions on Wireless Communications, 25, 11480–11496. https://doi.org/10.1109/TWC.2026.3653567
7. Jiang, Q., Jin, J., Wang, Q., Tang, Y., Cao, Y., Kuang, B., Dong, J., Lv, S., Wang, D., Huang, Y., Wang, J., & You, X. (2026). Experimental performance of bidirectional phase coherent transmission and sensing for mmWave cell-free massive MIMO systems with recip-rocity calibration [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2601.14648
8. Ganesan, U. K., Sarvendranath, R., & Larsson, E. G. (2023). BeamSync: Over-the-air syn-chronization for distributed massive MIMO systems [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2311.11070
9. Rashid, M., & Nanzer, J. A. (2023). High accuracy distributed Kalman filtering for syn-chronizing frequency and phase in distributed phased arrays. IEEE Signal Processing Letters, 30, 688–692. https://doi.org/10.1109/LSP.2023.3283333
10. Saiko, V. H., Zinchenko, M. O., Komarov, V. O., Solodovnyk, V. I., Kryvolapov, Ya. V., & Kryvolapov, H. Ya. (2025). Model intehrovanoho merezhevoho kompleksu zviazku z vy-korystanniam BPLA dlia pidvyshchennia efektyvnosti vyrishennia tsilovykh zavdan spetsial-noho pryznachennia [Model of an integrated network communication complex using UAVs to increase the efficiency of solving special-purpose tasks]. Vcheni zapysky TNU imeni V. I. Vernadskoho. Seriia: Tekhnichni nauky, 36(75)(2), 130–138. https://doi.org/10.32782/2663-5941/2025.2.1/20
11. Ngo, H. Q., Ashikhmin, A., Yang, H., Larsson, E. G., & Marzetta, T. L. (2017). Cell-free massive MIMO versus small cells. IEEE Transactions on Wireless Communications, 16(3), 1834–1850. https://doi.org/10.1109/TWC.2017.2655515
12. Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007). Consensus and cooperation in net-worked multi-agent systems. Proceedings of the IEEE, 95(1), 215–233. https://doi.org/10.1109/JPROC.2006.887293
13. Xiao, L., & Boyd, S. (2004). Fast linear iterations for distributed averaging. Systems & Control Letters, 53(1), 65–78. https://doi.org/10.1016/j.sysconle.2004.02.022
14. Allan, D. W. (1966). Statistics of atomic frequency standards. Proceedings of the IEEE, 54(2), 221–230. https://doi.org/10.1109/PROC.1966.4634
15. Björnson, E., Demir, Ö. T., & Sanguinetti, L. (2021). A primer on near-field beamform-ing for arrays and reconfigurable intelligent surfaces [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2110.06661
16. Euchner, F., Gauger, M., Dörner, S., & ten Brink, S. (2021). A distributed massive MIMO channel sounder for “Big CSI Data”-driven machine learning. In WSA 2021: 25th In-ternational ITG Workshop on Smart Antennas. https://doi.org/10.48550/arXiv.2206.15302
17. Euchner, F., & Gauger, M. (2022). CSI dataset DICHASUS-CF0x: Distributed antenna setup in industrial environment, day 1 [Data set]. DaRUS. https://doi.org/10.18419/darus-2854
18. Alkhateeb, A., Charan, G., Osman, T., Hredzak, A., Morais, J., Demirhan, U., & Srinivas, N. (2023). DeepSense 6G: A large-scale real-world multi-modal sensing and communication dataset. IEEE Communications Magazine, 61(9), 122–128. https://doi.org/10.1109/MCOM.006.2200730
19. Charan, G., Hredzak, A., Stoddard, C., Berrey, B., Seth, M., Nunez, H., & Alkhateeb, A. (2022). Towards real-world 6G drone communication: Position and camera aided beam pre-diction. In 2022 IEEE Global Communications Conference (GLOBECOM) (pp. 2951–2956). IEEE. https://doi.org/10.1109/GLOBECOM48099.2022.10000718
20. Bar-Shalom, Y., Li, X.-R., & Kirubarajan, T. (2001). Estimation with applications to tracking and navigation. Wiley.
21. Künsch, H. R. (1989). The jackknife and the bootstrap for general stationary observa-tions. The Annals of Statistics, 17(3), 1217–1241. https://doi.org/10.1214/aos/1176347265
22. Gneiting, T., & Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and esti-mation. Journal of the American Statistical Association, 102(477), 359–378. https://doi.org/10.1198/016214506000001437
Downloads
Published
Issue
Section
License
Copyright (c) 2026 System technologies
This work is licensed under a Creative Commons Attribution 4.0 International License.
