Transfer Learning Empowered Power Allocation in Holographic MIMO-enabled Wireless Network

The upcoming 6G wireless communication networks are anticipated to offer extensive mobile connectivity, faster data services with reduced power consumption, and seamless integration among different technologies for providing effective beamforming. To accomplish these aims, a transfer learning empowered AI framework is proposed to allocate the power for serving the users under the coverage areas of the corresponding holographic MIMOs (HMIMOs) by activating the required number of grids from the respective HMIMOs. An optimization problem is formulated with the goal of maximizing the utility function for achievable rate, which in turn maximizes the signal-to-interference-plus-noise ratio (SINR), and achievable rate of the users. The HMIMO that serves the highest number of users is considered as the parent HMIMO and the rest of the HMIMOs are regarded as the child HMIMOs. A Transformer-based AI framework is utilized for allocating the power to the users under the coverage areas of the parent HMIMO and transfers the knowledge of the trained model to the child HMIMOs which requires lower learning cost to allocate power to the corresponding users within the coverage areas of the child HMIMOs. Finally, simulation results show that the proposed AI framework empowered by transfer learning surpasses the baseline methods such as gated recurrent unit and long short-term memory, achieving power savings ranging from 28.14% to 38.92% and achievable rate enhancements from 16.58 bps/Hz to 16.84 bps/Hz.