Last week our postdoc Dr. Asmaa Abdallah presented her work on Deep Learning Based Frequency-Selective Channel Estimation for Hybrid mmWave MIMO Systems in the Research conference: Extreme Bandwidth Communication: From mmWave, THz to Optical Bands.
Her work is about channel estimations for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. These systems typically employ hybrid mixed signal processing to avoid expensive hardware and high training overheads. However, the lack of fully digital beamforming at mmWave bands imposes additional challenges in channel estimation. Prior art on hybrid architectures has mainly focused on greedy optimization algorithms to estimate frequency-flat narrowband mmWave channels, despite the fact that in practice, the large bandwidth associated with mmWave channels results in frequency-selective channels. In her work, a frequency-selective wideband mmWave system is considered and two deep learning (DL) compressive sensing (CS) based algorithms are proposed for channel estimation. The proposed algorithms learn critical apriori information from training data to provide highly accurate channel estimates with low training overhead. In the first approach, a DL-CS based algorithm simultaneously estimates the channel supports in the frequency domain, which are then used for channel reconstruction. The second approach exploits the estimated supports to apply a low-complexity multi-resolution fine-tuning method to further enhance the estimation performance.
This work has been done in collaboration with Prof. Mohammad Mansour from the American University of Beirut.
You can access a full version of the poster in this link.
All the members of CCSL congratulate Dr. Asmaa Abdallah and celebrate her accomplishments.
References:
Abdallah, A., Celik, A., Mansour, M. M., & Eltawil, A. M. (2021). Deep Learning Based Frequency-Selective Channel Estimation for Hybrid mmWave MIMO Systems. IEEE Transactions on Wireless Communications, 1–1.
Handle 10754/668446 DOI 10.1109/TWC.2021.3124202 Altmetrics
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