Channel estimation in 5G using deep learning

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Abhiram Anilkumar
Abhiram Anilkumar 2022-3-13
评论： Thien Hieu 2024-7-16
What is the meaning of practical channel estimation and perfect channel estimation in Channel estimation in 5G using deep learning project ?
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Thien Hieu
Thien Hieu 2024-7-16
hi, I also had the same question, and I went into the code and saw something like virtual pilot interpolation, you can check papers related to it, I'm also reading them now ^^

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回答（2 个）

Shekar N
Shekar N 2022-12-29
Hi Abhiram,
I understand that you would like to know about ‘Perfect’ and ‘Practical’ channel estimation referred in the example ‘Deep Learning Data Synthesis for 5G Channel Estimation’
Perfect channel estimation returns the actual channel (H). The function ‘nrPerfectChannelEstimate’ first reconstructs the channel impulse response from the channel path gains ‘pathGains’ and the path filter impulse response ‘pathFilters’. The function then performs orthogonal frequency division multiplexing (OFDM) demodulation. Carrier specifies the parameters for the OFDM demodulation.
Refer the link for more information on perfect channel estimation
https://www.mathworks.com/help/5g/ref/nrperfectchannelestimate.html?s_tid=doc_ta
Practical channel estimation ‘nrChannelEstimate’ performs channel estimation on the received resource grid ‘rxGrid’ by using a reference resource grid containing reference symbols ‘refSym’ at locations ‘refInd’.
Refer the link for more information on practical channel estimation
https://www.mathworks.com/help/5g/ref/nrchannelestimate.html
I hope the above information resolves your query
Prabha A G
Prabha A G 2023-11-2
import numpy as np
# Define the parameters
num_samples = 1000
snr_db = 10 # Signal-to-noise ratio in dB
# Generate random complex symbols for transmitted signals
transmitted_symbols = np.random.randn(num_samples) + 1j * np.random.randn(num_samples)
# Generate random complex channel coefficients
channel_coefficients = np.random.randn(num_samples) + 1j * np.random.randn(num_samples)
# Apply the channel and add AWGN to create received signals
noise_power = 10 ** (-snr_db / 10)
received_signals = channel_coefficients * transmitted_symbols + np.sqrt(noise_power) * (np.random.randn(num_samples) + 1j * np.random.randn(num_samples))
# The channel coefficients are our labels
labels = channel_coefficients
# Split the data into training and testing sets (you can use more advanced splitting methods)
train_samples = 800
X_train, X_test = received_signals[:train_samples], received_signals[train_samples:]
y_train, y_test = labels[:train_samples], labels[train_samples:]

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