Efficient Channel Estimation and Beamforming in Massive MIMO

Title: - Deep Learning-Based Channel Estimation and Beamforming Architecture for Massive MIMO System
-----------------------------------------------------------------------------------------------------------------------
Implementation Plan:
---------------------------------

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

============================================================
Software Requirements:
-----------------------------------

1. Tool: Matlab-R2020a (or and above version).
2. OS: Windows-10 (64-bit)
============================================================

Note:-
--------
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

\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\
#EfficientChannelEstimation
#Beamforming
#MassiveMIMO
#WirelessCommunication
#5GTechnology
#AntennaArray
#SignalProcessing
#WirelessNetworking
#SpectralEfficiency
#MIMOtechnolog
--------------------------------------------------------------------------------------------------------------------

Our organization offers a comprehensive range of services to support Research
Endeavors, including Topic Selection, Research Proposal ,Development, Code
and Simulation Assistance, Paper Writing, Paper Publishing, as well as Synopsis
and Thesis writing.
These services are designed to facilitate the research process and ensure the
successful completion of Research Projects.

For complete Research Support contact us through:

E-mail us at : [email protected]

Visit us at : https://phdprojects.org/

call us at : +91 98946 59122.

Тэги:

#EfficientChannelEstimation #Beamforming #MassiveMIMO #WirelessCommunication #5GTechnology #AntennaArray #SignalProcessing #WirelessNetworking #SpectralEfficiency #MIMOtechnolog

Скачать видео