1.5k citations · 2.2k across the 19 of their papers we have counts for
4 papers · 1 filter
Deep Active Learning Approach to Adaptive Beamforming for mmWave Initial Alignment
Foad Sohrabi, Zhilin Chen, Wei Yu
This paper proposes a deep learning approach to the adaptive and sequential beamforming design problem for the initial access phase in a mmWave environment with a single-path chann…
Deep Learning for Channel Sensing and Hybrid Precoding in TDD Massive MIMO OFDM Systems
Kareem M. Attiah, Foad Sohrabi, Wei Yu
This paper proposes a deep learning approach to channel sensing and downlink hybrid beamforming for massive multiple-input multiple-output systems operating in the time division du…
Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO
Foad Sohrabi, Kareem M. Attiah, Wei Yu
This paper shows that deep neural network (DNN) can be used for efficient and distributed channel estimation, quantization, feedback, and downlink multiuser precoding for a frequen…
Phase Transition Analysis for Covariance Based Massive Random Access with Massive MIMO
Zhilin Chen, Foad Sohrabi, Ya-Feng Liu +1
This paper considers a massive random access problem in which a large number of sporadically active devices wish to communicate with a base station (BS) equipped with massive multi…