Modeling and Simulink of Channel Estimation and Beamforming Architecture for Massive MIMO System
Implementation Plan:
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Step 1: Initially we design the network; it consists of 100- User Equipment (UEs), 4- Base stations (BS) and 1- MIMO-BS.
Step 2: Next we perform the Analysis of Channel Quality process, In this process the context of assessing channel quality in wireless communication systems using Doppler-Sparse Channel Assessment (DSCA).
Step 3: Next the channel estimation, the Deep Recurrent Channel Estimation Network (DR-CEN) is an advanced channel estimation method tailored for MIMO (Multiple Input, Multiple Output) systems.
Step 4: Next we perform Antenna Selection: the completion of the channel estimate stage, we concentrate on beamforming process optimization. In this optimization, we employ a hybrid approach wherein, for every training cycle, we build both the transmit beamformer (precoder) and the receive beamformer (combiner).
4.1: Beamforming: For beamforming we use the Deep Learning Methods based Hybrid Beamforming (DLM-HB).
4.2: Channel Selection: In the context of optimizing wireless channel selection, Reinforcement Learning with Deep Networks (RL-DQN) is used.
Step 5: FOV-Selective Receiver: A FOV-Selective Receiver focuses on signals from a specific angular range while minimizing interference from other directions. Optimizing antenna parameters to ensure it effectively captures signals within the specified angular range.
Step 6: Data Transmission: Data transmission involves encoding and transmitting data from a sender to a receiver over a communication channel, where the sender modulates the data.
Step 7: Next we perform Spectral efficiency improvement is an inherent feature of MIMO systems due to their ability to simultaneously transmit multiple data streams. In this step we used Alamouti Space-Time Block Coding (Alamouti STBC).
Step 8: User Scheduling, User scheduling in MIMO takes into consideration the available spatial resources, aiming to allocate them efficiently.
Step 9: The proposed approach is validated using several parameters such as,
9.1: SNR with NMSE
9.2: SNR with MSE
9.3: SNR Vs Spectral Efficiency
9.4: Pilot overhead Vs NMSE
9.5: SNR Vs Processing Time
9.6: SNR vs. Bit Error Rate
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Software Requirements:
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1. Tool: Matlab-R2020a (or and above version).
2. OS: Windows-10 (64-bit)
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Note:-
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We perform the EXISTING process based on the Reference 2 Title: A family of deep learning architectures for channel estimation and hybrid beamforming in multi-carrier mm-wave massive MIMO
