Reproduce “Pilot-Efficient Channel Estimation for 6G MU-mMIMO Using LS Initialization and GAN-Augmented CNN Refinement” on OpenResearch
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About this paper
Pooya Hejazi, Masoud Sabaei, Ali Hajmahmoudi
The authors combine a least-squares (LS) pilot estimate with a convolutional neural network (CNN) refinement. In simulated outdoor DeepMIMO data, the modeled uplink has a 32-antenna base station and 20 users. At 0 dB signal-to-noise ratio, normalized mean squared error is approximately −14.2 dB versus −6.0 dB for LS/MMSE, an approximately 8 dB improvement over classical estimators in this setting.
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