GWO Based Optimal Channel Estimation Technique for Large Scale Mimo in LTE Network

Abstract

The Wireless Systems Are Employed With More Number Of Antennas For Fulfilling The Demand For High Data Rates. In Telecommunication, Lte-A (Long Term EvolutionAdvanced) Is A Well-Known Technology Intended For Wireless Broadband Communication Aimed At Data Terminals And Mobile Devices. Multiple Input Multiple Output (Mimo) Technology Is Used By Lte Which Is Also Known As Fourth Generation Mobile Networks To Attain Very High Data Rates In Downlink And Uplink Channels. Though The Mimo Systems In Massive Mimo Are Provided By Multiple Antennas, The Design Of The Appropriate Non-Erroneous Detection Algorithm Is A Major Challenge. Also, With The Increase In Quantity Of Antennas, The System's Spectral Efficiency Begins To Degrade. So As To Deal With This Issue, A Proper Algorithm Has To Be Utilized For Channel Estimation. The Bio Inspired Algorithms Have Shown Potential In Handling These Issues In Communication And Signal Processing. So, Grey Wolf Optimization (Gwo) Algorithm Is Used In This Approach To Estimate The Most Optimal Communication Channel. Also, The Spectral Efficiency And Data Capacity Are Enhanced Using The Presented Approach. The Proposed Approach’s Performance Is Compared With The Existing Approaches. The Simulation Result Exposes That The Presented Channel Estimation Approach Is Far Better Than Existing Channel Estimation Approaches In Performance Metrics Such As Bit Error Rate, Minimum Delay, Papr, Spectral Efficiency, Uplink Throughput, Downlink Throughput And Mean-Squared-Error

Intelligent channel estimation in millimeter wave massive MIMO communication system using hybrid deep learning with heuristic improvement

“…The proposed “channel estimation in mmWave massive MIMO communication system” was “implemented in MATLAB 2020a using data Deep MIMO. Here, the performance of the proposed model was compared over the conventional models” in terms of several measures like NMSE and spectral efficiency over other heuristic‐algorithms like Dragonfly Algorithm (DA), 46 “Deer Hunting Optimization Algorithm (DHOA), 47 Gray Wolf Optimization (GWO)” 48 and HHO‐D‐LSTM 42 and other channel estimation models like Convolutional Neural Network (CNN), 49 DNN, 39 LSTM 41 and D‐LSTM 39 , 41 . The simulation constraints for designing the mmWave massive MIMO communication system are given in Table 2.…”

“…Evaluation of the computational analysis using uplink and downlink The computational complexity of the designed methodAlgorithmTime complexityOE-HHO O [iter+(N*N)] O [iter+(N + N)]-HHO T A B L E 7Evaluation of the statistical analysis using uplink and downlink Measures DA-D-LSTM46 DHOA-D-LSTM47 GWO-D-LSTM48 HHO-D-LSTM42 …”