Channel estimation in 5G using deep learning

What is the meaning of practical channel estimation and perfect channel estimation in Channel estimation in 5G using deep learning project ?

[Thien Hieu]

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 ^^

Answer 1

[Shekar N]

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

Answer 2

[Prabha]

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:]