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Deep Learning(RNN) based sparse channel estimation and hybrid precoding in mm-Wave massive MIMO systems and its comparison to Compressed sensing OMP, PSO, DGMP performance

I will be posting base papers with results, need to enhance all the results of base paper using Deep learning with RNN recursive neural networks(novel approach) and comparing it to state of art results and OMP, DGMP, PSO

[login to view URL] its IEEE paper with novelty and reproduce results enhanced and better than state of art

2. provide the prepared paper along with Turnitin report (less than 10% similarity) compulsory

Recursive neural network can learn channel structure and estimate channel from a large number of

training data. Channel estimation and hybrid precoding are considered for multi-user millimeter wave massive multi-input multi output system. A deep learning compressed sensing channel estimation using RNN scheme has to be proposed. The channel estimation neural network for the DLCS scheme is trained offline using simulated environments to predict the beamspace channel amplitude. Then the channel is reconstructed based on the obtained indices of dominant beamspace channel entries. A deep learning quantized phase (DLQP) hybrid precoder design method is developed after channel estimation. The training hybrid precoding neural network for the DLQP method is obtained offline considering the approximate phase quantization. Then the deployment hybrid precoding neural network (DHPNN) is obtained by replacing the approximate phase quantization with ideal phase quantization and the output of the DHPNN is the analog precoding vector.

Finally, the analog precoding matrix is obtained by stacking the analog precoding vectors and the digital precoding matrix is calculated by zero-forcing. Simulation results demonstrate that

the DLCS channel estimation scheme outperforms the existing schemes in terms of the normalized mean-squared error and the spectral efficiency, while the DLQP hybrid precoder design

method has better spectral efficiency performance than other methods with low phase shifter resolution.

Software : Matlab with neuralnetwork toolbox can be used or Python

Performance parameters :NMSE vs SNR, Spectral Efficiency vs SNR, Comparisons of channel estimation performance in terms of the number of time slots for channel training for different schemes,Comparisons of spectral efficiency in terms of the number of time slots for channel training for different channel estimation schemes(OMP, DGMP,PSO).

Need Neural network person strong in mm-wave channel estimation and Matlab simulations with nntool box

Навыки: Алгоритмы, Matlab and Mathematica, Электротехника, Беспроводные технологии, Техника

О работодателе:
( 1 отзыв ) Hyderabad, India

ID проекта: #25748422

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