Maximizing Mimo Spectral Efficiency Using Linear Discriminant Analysis (Lda) And Drl With Non-Linear Analysis

Abstract

Massive MIMO (Multiple-Input Multiple-Output) technology considerably increases spectral efficiency and network capacity in modern wireless communication systems. While combining Linear Discriminant Analysis (LDA) and Deep Reinforcement Learning (DRL) could help to further increase spectrum efficiency, integrating these approaches with non-linear analysis remains an area of current research since both techniques have shown great power for optimizing MIMO performance. Even with improvements in MIMO technology, complex channel characteristics and non-linear interference make improving spectral efficiency a challenging choreography. Conventional optimization techniques find it challenging to adapt to dynamic environments and non-linearities, so they are limited in real-world applications. By merging LDA and DRL with non-linear analysis, this work proposes a new technique optimizing MIMO spectrum efficiency. By means of feature extraction and dimensionality reduction, LDA enhances dimensionality reduction and signal processing thereby avoiding interference. Designed especially for adaptive learning and decision-making, DRL maximizes beamforming and resource allocation in real-time. Non-linear analysis helps to control difficult channel conditions and raise resistance against interference. The proposed method was evaluated on a standard MIMO testbed with 64 antennas and 16 users. Under various channel conditions the spectral efficiency increased from 4.2 bps/Hz to 5.5 bps/Hz, so demonstrating the efficacy of the proposed strategy in increasing MIMO performance.