AI ASSISTED ADAPTIVE BEAMFORMING FOR ENERGY EFFICIENT 6G WIRELESS COMMUNICATION SYSTEMS

Abstract

The
sixth generation ( of wireless communication systems, envisioned for commercial
deployment around 2030 under the ITU R IMT 2030 framework (Recommendation ITU R M.2160,
Novembe r 2023), introduces unprecedented demands on spectral and energy efficiency. Conventional
model based beamforming approaches are increasingly inadequate for the ultra dense, heterogeneous,
and high mobility environments characteristic of 6G. This paper exa mines AI assisted adaptive
beamforming as a transformative paradigm for 6G, integrating deep reinforcement learning (
convolutional neural networks ( and long short term memory ( networks into the
beamforming pipeline for massive multiple i nput multiple output ( arrays, terahertz (
frequency channels, and reconfigurable intelligent surface ( assisted propagation environments.
A comparative analysis of AI based versus conventional beamforming is presented alongside a review
of I ndia's nascent 6G research policy landscape. Recommendations are provided for accelerating AI
beamforming integration in alignment with the IMT 2030 framework.