Transformer-based Communication Resource Allocation for Holographic Beamforming: A Distributed Artificial Intelligence Framework

The next-generation wireless communication systems are expected to meet the increasing demand for higher data services with nominal power for beamforming. Therefore, distributed holographic MIMOs (HMIMOs)-based resource allocation approach for beamforming improves the channel capacity of the users under the corresponding coverage areas of the distributed HMIMOs which in turn enhances the signal-to-interference-noise ratio (SINR) of the intended users. An optimization problem is formulated to maximize the channel capacity considering the users in the coverage areas of the distributed HMIMOs and the corresponding SINRs of the users. A distributed artificial intelligence (DAI) framework is proposed for solving the formulated NP-hard problem. Therefore, a Transformer-based DAI framework is utilized to allocate the communication resources such as power to the respective users for beamforming under the coverage areas of the distributed HMIMOs. Finally, experimental results demonstrate that power is allocated efficiently to the corresponding users for beamforming that is placed under the coverage areas of the distributed HMIMOs that denote the effectiveness of our proposed DAI framework.