A.O. Chilingarov1, A.L. Gelgor2

1 JSC “Concern “Oceanpribor” (St. Petersburg, Russia)

2 Peter the Great St. Petersburg Polytechnic University (St. Petersburg, Russia)

1 chilingarov.ao@gmail.com; 2agelgor@spbstu.ru

The problem formulation. The adaptive modulation is a powerful tool for improving noise immunity and/or data transmission rate in static channels with frequency-selective fading. To deploy it i n communication system subject to non-stationary fading and wit h low data rate capabilities, it is essential to minimize the amount of channel state information (CSI) transmitted over the feedback link.

Objective. To enhance the efficiency of adaptive modulation applying in low-speed non-stationary communication channels by red ucing the amount of transmitted channel parameter information.

Results. This work proposes an algorithm for quantizing the cha nnel response in dB scale prior to its transmission over the fe edback link. Simulation modeling for a static channel demonstrated a g ain of 3.7 to 5 dB from using ad aptive modulation. This gain is achieved when quantizing the channel transfer function level in the feedback link into 16 bins. Verification of the adaptive m odulation operation feasibility for a real dynamic underwater acoustic channel was performed. It is shown that in this case, 16 quantization bins are also sufficient to achieve a result almost indistinguishabl e from the case without quantization. Applying adaptive modulat ion in the real dynamic channel, considering the feedback transmission delay and using 16-bin channel estimate quantization provides the gain from 0.2 to 4.5 dB. In the same channel, in the absence of feedback transmission delay and without channel estimate quant ization, the gain from applying adaptive modulation ranged from 1.1 to 6.4 dB.

Practical Significance. The study confirms the efficacy of adaptive modulation for low- data-rate, non-stationary channels. As a practical application, the developed feedback quantization algorithm enables extend-range communication in systems such as underwate r acoustic modems, all while preserving the target data throughput

Chilingarov A.O., Gelgor A.L. Adaptive modulation in low-speed non-stationary channels with frequency-selective fading. Radiotekhnika. 2026. V. 90. № 3. P. 126−138. DOI: https://doi.org/10.18127/j00338486-202603-11 (In Russian)

1. Böcherer G., Schulte P. and Steiner F. Probabilistic shaping and forward error correction for fiber-optic communication systems. Journal of Lightwave Technology. Jan. 2019. V. 37. № 2. P. 230−244.
2. Perez J., Ibañez J. Adaptive mod ulation and power in wireless c ommunication systems with delay constraints. IEEE Statistical S ignal Processing Workshop (SSP). 2011. P. 73−76.
3. Oh J.-h., Lim J.-t. Two-step channel estimation scheme for OFDM systems over fast Rayleigh fading channels. IEEE Communication s Letters. June 2010. V. 14. № 6. P. 545−547.
4. Qarabaqi P., Stojanovic M. Statistical characterization and com putationally efficient modeling of a class of underwater acoust ic communication channels. IEEE Journal of Oceanic Engineering. Oct. 2013. V. 38. № 4. Р. 701−717.
5. Kovzel' D.G. Apparatura akusticheskoj svyazi dlya kontrolya rab oty avtonomnoj gidroakusticheskoj donnoj stancii na shel'fe. Ak usticheskij zhurnal. 2019. T. 65. № 5. S. 619−629 (in Russian).
6. Puzko D., Batov Y., Gelgor A., Dolgikh D. QAM constellations wi th fractional entropy to gain in margin maximization for freque ncy selective channels. IEEE Communications Letters. May 2023. V. 27. № 5. P. 1457−1461.
7. Lei D., Bo L., Hao S., Ziyu M., Yuji L., Yani Q. Design and simulation of baseband coaxial cable communication system based on OFDMtechnology. IEEE 6th International Conference on Computer and Communication Engineering Technology (CCET). Beijing. China. 2023. P. 217−222.
8. Wang Y., Tao J., Ma L., Jiang M., Chen W. Joint timing and freq uency synchronization for OFDM underwater acoustic communicatio ns. IEEE/CIC International Conference on Communications in China. 2021. P. 272−277.
9. Zhao Y., Wan L., Chen Y., Cheng E., Xu F., Liang L. Power alloc ation for non-coherent multi-carrier FSK underwater acoustic communication systems with uneven transmission source level. 2022 14th International Conference on Signal Processing Systems (ICSPS). Jiangsu. China. 2022. P. 61−622. 1
10. Huang X., Liu Y. Capacity criter ion-based power allocation for OFDM cooperative underwater acoustic communications with limite d feedback. Oceans, Hampton Roads. VA. USA. 2012. P. 1−4. 1
11. Kranc V.Z. Gidroakusticheskie kommunikacionnye sistemy (osnovy razrabotki). SPb: SPbGETU «LETI». 2024 (in Russian). 1
12. Radosevic A., Ahmed R.T., Duman M., Proakis J.G., Stojanovic M. Adaptive OFDM modulation for underwater acoustic communications: design considerations and experimental results. IEEE Journal of Oceanic Engineering. April 2014. V. 39. № 2. P. 357−370. 1
13. Huang X., Liu Y. Capacity criter ion-based power allocation for OFDM cooperative underwater acoustic communications with limite d feedback. 2012 Oceans. Hampton Roads. VA. USA. 2012. Р. 1−4. 1
14. Papandreou N., Antonakopoulos T. Bit and power allocation in co nstrained multicarrier systems: The single-user case. EURASIP J. Adv. Signal Process. Dec. 2007. V. 2008. № 1. P. 1–14. 1
15. Porter M.B., Ocean Acoustics Library, Bellhop, http://oalib.hlsresearch.com/AcousticsToolbox/. 1
16. Farzamnia A., Hlaing N.W., Haldar M.K., Rahebi J. Channel estimation for sparse channel OFDM systems using least square and minimum mean square error techniques. 2017 International Conference on Engineering and Technology (ICET). Antalya. Turkey. 2017. P. 1−5. 1
17. Chilingarov A.O., Gel'gor A.L. Algoritm chastotnoj sinhronizaci i dlya sistem gidroakusticheskoj svyazi, ispol'zuyushchih signa ly s OFDM. Radiotekhnika. 2025. T. 89. № 3. S. 109−120. DOI: https://doi.org/10.18127/j00338486-202503-10 (in Russian) 1
18. Jensen F.B., Kuperman W.A., Porter M.B., Schmidt H. Computational Ocean Acoustics 2nd ed. Springer New York. NY. 2011.