Design And Implementation Of Deep Learning Based Mimo Systems For Wireless Networks

Abstract

Present Day Communication Systems Are Facing Some Critical Issues Which Are Increased Number Of Users, The Amount Of Bandwidth Availability To Be Used By The Users And The Need For Ever Increasing Data Rates. The Major Concern Regarding All The Problems Is The High Capacity Expectation From Wireless Channels. However, Wireless Channels Are Often Random In Nature With Frequency Selective Nature At The Basest. The Limitation In The Bandwidth Support By Any Channel Makes The Data Rate Support To Be Limited. In This Paper, A Deep Neural Network Assisted Massive MIMO System Has Been Designed And Has Been Employed To Commonly Existing Diverse Channel Conditions. To Increase The Spectral Efficiency And Simultaneously Reduce The BER Of The System, The Maximum Ratio Comining (MRC) Approach Has Been Used Along With MMSE And ZFE Equalization Techniques. The Proposed System Has Been Simulated On Matlab. The Performance Of The System Has Been Evaluated In Terms Of The Bit Error Rate And Spectral Efficiency Of The System.

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