AI Enhanced Resource Allocation in 6G MIMO-OFDM

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 3, pp. 246-255, March 2026.

https://doi.org/10.58482/ijersem.v2i3.31

This work is licensed under a Creative Commons Attribution 4.0 International License.

AI Enhanced Resource Allocation in 6G MIMO-OFDM

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K Bhaskar

Basha Lahari

E D Mahalakshmi

Panguru Mahesh

Gogula Naveen

Maddu Manoj

Department of ECE, Siddartha Institute of Science and Technology, Puttur, India.

Abstract: The anticipated capabilities of the sixth generation (6G) of wireless technology include the ability to serve exceptionally high data throughput, low latency, and intelligent resource allocation under changing conditions in the wireless medium. This research presents a 6G MIMO/Orthogonal Frequency Division Multiplexing (OFDM) system designed to meet these challenges using a pilot-assisted method for estimating the channel and implementing adaptive Non-Linear Minimum Mean Square Error (NL-MMSE) equalizers for interference mitigation through automatic allocation of resources (i.e., bandwidth and power). The proposed framework is also based on the principles of Convolutional Encoding, Random Interleaving, and Phase Shift Keying (PSK) modulation to enhance data reliability. Simulation results indicate that the proposed scheme produces significantly better performance than that achieved with conventional equal allocation and water-filling methods of resource allocation. The Bit Error Rate (BER) at a 10 dB Signal to Noise Ratio (SNR) for Binary Phase Shift Keying (BPSK) modulation reaches the order of 10−5, 10−5 at 10 dB SNR; and for higher-order modulations, trending toward reliable performance is achieved with increasing levels of SNR. The proposed framework achieves a channel capacity of approximately 19 bits/s/Hz at 30 dB SNR, with the latency reduced to ~0.05 and the energy efficiency improved to almost 18 bits/J. The results conclusively show that the proposed sixth generation of MIMO/OFDM technology provides superior spectral efficiency, enhanced robustness, and energy efficiency when compared to all previous generations of wireless communication. Thus, the technology developed in this research is a prime candidate for deployment in the future generation of wireless networks.

Keywords: 6G wireless communication, MIMO-OFDM, adaptive NL-MMSE equalization, AI-based resource allocation, channel estimation.

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This article is part of Volume 2, Issue 3 (March 2026)