Dynamic spectrum allocation in 6G MIMO systems using adaptive deep multiagent reinforcement learning with coordinate attention

Abstract

The Millimeter-wave massive Multiple-Input Multiple-Output (MIMO) is a core mechanism for the Sixth-Generation (6G) wireless communication networks. By including numerous antennas in the compact model of advanced smartphones, the MIMO enhances the network capacity and the Spectral Efficiency (SE). The growth of 6G technology is significant for the future and provided evolutionary and revolutionary solutions. The resource allocation in the MIMO-based wireless networks is selected for various users, aiming to optimize the network resource distribution. But the high increase in the antennas and users poses complexities for the resource allocation and interference suppression for the MIMO systems. In this research, an advanced Deep Reinforcement Learning (DRL)-based approach is proposed for efficient dynamic spectrum allocation in 6G MIMO systems. To perform spectrum allocation in 6G MIMO systems, an Adaptive Deep Multiagent Reinforcement Learning with Co-ordinate Attention (ADMRL-CA) model is developed. The DMRL mechanism is capable of handling varying traffic and channel conditions. The incorporation of the CA mechanism enhances the policy learning process for accurate spectrum allocation. The parameters of the ADMRL-CA are fine-tuned using the Flying workers phase Modified Termite Queen Algorithm (FMTQA). Finally, the model performance is analyzed with various existing models. The SE of the recommended FMTQA-ADMRL-CA is increased by 4.44% of DRL, 6.66% of SAC, 2.77% of DDPG and 10.88% of DMRL-CA when system uses as 32 nd batch size. Hence, it is guaranteed that the recommended FMTQA-ADMRL-CA can allocate the spectrum efficiently and robustly in 6G MIMO systems than the existing methods.