Hybrid Deep Learning Approach for 6G MIMO Channel Estimation and Interference Alignment HetNet Environments

Abstract

Future 6G wireless networks are anticipated to support a variety of gadgets, including smartphones, tablets, smart home sensors, etc. One of the most significant problems that limits the operation of wireless networks as the number of connected devices rises is interference. With the advent of 6G wireless networks, new use cases and applications are emerging that adhere to tight standards for next-generation wireless communications. On TV, radio, or mobile phones, interference causes poor reception of the images or sounds. EM (Electromagnetic) waves are used as the transport medium in these communication systems. Therefore, recent research has focused on the potential of DL techniques in fulfilling these stringent requirements and addressing the drawbacks of existing modelbased methodologies. In 6G MIMO channel estimation with interference alignment, this research proposes a unique method based on a heterogeneous network and deep learning methods. HetNet-based multiuser propagation is used in this case to estimate the channel. A hybrid transfer convolutional network has been used to align the network's interference. We design an Orthogonal Frequency Division Multiplexing (OFDM) frame structure to illustrate the allocation of time-frequency resources to pilot signals for channel estimation. It is important to note that the proposed framework does not require information transmission between BSs and instead operates in a non-iterative and distributed manner based on local channel state information (CSI) at both BSs and users.