Artificial Intelligence Framework for Target Oriented Integrated Sensing and Communication in Holographic MIMO

The future sixth-generation (6G) wireless communication networks are expected to provide massive connectivity with lower power requirements for generating the desired beamforming. Therefore, holographic MIMO assisted integrated sensing and communication framework is proposed that ensures lower power requirements to activate the minimum number of grids from the holographic grid array (HGA) for the effective beamforming. An optimization problem is formulated that maximizes the signal to noise-interference ratio (SNIR) of the users which in turn maximizes the utility function for sensing (UFS) considering the beampattern gains, distances, and sensing-communication loss. A novel artificial intelligence (AI) framework is proposed to solve the formulated problem which is a NP-hard problem. First, a variational autoencoder (VAE) based scheme is developed to solve the challenges of determining the exact location of the users and complete data distribution. Then, a sequential neural network-based mechanism is devised to allocate the communication resources to the heterogeneous users for the desired beamforming based on the results obtained from VAE. Finally, simulation results demonstrate that the proposed algorithms confirm 23% power savings compared to long short-term memory (LSTM) method to perform effective beamforming for serving the users.