Optimization of Channel Estimation Using ELMx-based in Massive MIMO

In communication channel estimation, the Least Square (LS) technique has long been a widely accepted and commonly used principle. This is because simple calculation method compared with other estimation methods. The Minimum Mean Squares Error (MMSE), which developed later, devised as next step goal to reduce error rate in system from conventional LS still higher rate. These estimations are very important modern systems, especially massive MIMO. Evaluating MIMO one of most researched debated topics today. essential technology overcome traditional performance barriers. better more accurate it is. paper investigated machine learning (ML) for estimation. ML based on Extreme Learning Machine (ELMx) group also implemented. estimations, known ELMx group, include Regularized (RELM) Outlier Robust (ORELM). Then, was MMSE. simulation results reveal that outperforms MMSE capacity bit Additionally, this proven complexity verified computational times. RELM less time consuming low suitable future use large systems.