Digital Modulation Augmentations with Adaptive Modulation and Coding and AI in Cellular Networks.

DOI: https://doi.org/10.33650/jeecom.v8i2.16405
Authors

(1) * Uzonna Gabriel Anamonye   (Delta state University Abraka)  
        Nigeria
(2)  Olisemedua John Okonkwo   (Delta state University Abraka)  
        Nigeria
(3)  Gabriel Ilori Efenedo   (Delta state University Abraka)  
        Nigeria
(4)  Emmanuel Ewere Obuseh   (Delta state University Abraka)  
        Nigeria
(*) Corresponding Author

Abstract


A user closer to a base station can operate using a higher order of quadrature  modulation, such as 64-QAM, for its communication link, however once the channel’s condition vary such as distance or noise, the throughput and spectral efficiency drops and results in unreliable and inefficient link. The study aimed to implement digital modulation techniques, for efficient data transmission in cellular networks. So the efficacy of Adaptive Modulation and Coding (AMC) and the prospect of Artificial Intelligence (AI) were considered as solution. Simulation was carried out in MATLAB R2025a and compared the performance of the various digital modulation techniques in their SNR and BER characteristics, in presence of Additive White Gaussian Noise. Also adaptive modulation and deep learning were applied to ascertain their effectiveness. The results showed that AMC augmented approaches gave the best BER/SNR performances, followed by M-QAM, QPSK, and BPSK in that order under fixed SNR situations. The use of Convolution Neural Network (CNN) gave 100% prediction for BPSK, 95% for QPSK, 90% for 16-QAM and 85% for the 64-QAM. So the confusion matrix favoured all the modulation schemes and mostly the lower order ones.


Keywords

Digital modulation; Adaptive modulation and coding; Convolutional neural network; Bit error rate; Signal to noise ratio



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References


Supriya, K. Udaya, K. Shonoy, “Analysis of BPSK, QPSK, 16-QAM and 64- QAM Over Wireless Networks Using NS-2”, volume 1 no. 6, International Journal on Applications in Electrical and Electronics Engineering, pp. 13-16, 2015.

R.D. Prameela and N. Humaira, “Performance Evaluation of Digital Modulation Schemes BPSK, QPSK, and QAM”, volume 3, no. 2, International Journal of Engineering and Techniques, pp. 71-74, 2017.

. S. Ekta, P.D. Christopher, C.D. Ravinesh, D.C. Brad and S. Sancho, “A comprehensive systematic literature review on artificial intelligence for error correction and modulation schemes in next-generation satellite communications”, volume 58, no. 321, Springer Nature (Artificial Intelligence Review), pp. 1-67, 2025.

Lou F., “Understanding Mordern Digital Modulation Techniques. Electronic Design. Available online at https://www.electronic design.com.

. S.K. Chandra, B. Tarun, B. D. Harshitha, V. Dileep and D.S.G. Ruthwik, “Performance Analysis of BPSK, QPSK & QAM over AWGN Channel”, volume 17, no. 3, IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), pp. 32-37, 2022.

. Y. Liang, A comprehensive view intoMPSK modulation classification, volume 36, Applied and Computational Engineering, pp. 22-27, 2024.

. Y. Chai Y., “Performance Analysis of QAM Modulation and Demodulation, volume 134, Highlights in Science, Engineering and Technology”, pp. 114-125, 2025.

. B. Suchi, R. Yue, N. Sven and C. Reng, “Real-Time Adaptive Modulation Schemes for Underwater Acoustic OFDM Communication Sensors”, Accessed on 22, December 2025 at https://doi.org/10.3390/s22093436.

. A. Ali and N.M. Khan, “Adaptive Coding and Modulation in a direct A2G Communication Link for Inflight Broadband Connectivity”, vol. 17, Art. No. 102968, Physical Communication, (Elsevier), 2026.

. K. Rohit, S. Aparna, A. Pankaj, and G. Akhil, “A Brief Survey on Adaptive Modulation and Coding Over Time Varying Fading Channel”, volume 1, International Journal of Computer Applications, PP. 16-19, 2015.

. S. Dhananjay, K.K. Amit, and Y. Mala, “Performance of Digital Modulation Techniques on Basis of Bit Error Rate and Signal to Noise Ratio”, volume 8 no. 10, International Journal of Engineering Research & Technology (IJERT). ENCADEMS-2020 Conference Proceedings, PP. 171-174, 2020.

. S. Peng, S. Sun,and Y.D. Yao, “A Survey of Modulation ClassificationUsing Deep Learning: Signal Representation and Data Processing, vol. 33 ” IEEE Transactions on Neural Networks and Learning Systems, pp. 7020-7038, 2022.

. S. Nasir, S.A. Sheikh, and F>M> Malik, “Automatic Modulation Classification Using Convolutional Neural Network and Support Vector Vector Machine,” Digital Signal Processing, vol. 164, Art. No. 105249, 2025.doi:10.1016/j.dsp.2025.105249.

. J. Xu, C. Luo, G. Parr and Y. Luo, “A Spatiotemporal Multi-Channel Learning Framework for Automatic Modulation Recognition,” IEE Wireless Comminications Letters, 2020.

. U. G. Anamonye , K. O. Ogbeide , L.E. Omoze, “Quality of Service Experience for Cellular Data Network in Residential Clutter, vol. 2, Journal of Engineering Research Innovation and Scientific Development, no. 3, pp. 32-39, 2024.

. V. Oweh , G.I. Efenedo , U.G. Anamonye , E. E. “Obuseh Comparative Analysis of Signal Propagation Models Using Radial Basis Function Neural Networks for Urban Wireless Network Planning in Effurun, Delta State, Nigeria,” vol. 4, Journal of Engineering Research Innovation and Scientific Development, No. 1, 2026.

. B. Priyanka, J. Aadi, Manveen, K. Richita, and C. Rishab, “BER of Various Modulation Techniques Under Atmospheric Turbulences”, volume 10, no. 5, International Journal of Engineering and Advanced Technology (IJEAT), pp. 302-307, 2021.


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10.33650/jeecom.v8i2.16405


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Copyright (c) 2026 Uzonna Gabriel Anamonye, Olisemedua John Okonkwo, Gabriel Ilori Efenedo, Emmanuel Ewere Obuseh

 
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Journal of Electrical Engineering and Computer (JEECOM)
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