1.5k citations · 2.2k across the 19 of their papers we have counts for
19 papers
End-to-End Deep Learning for TDD MIMO Systems in the 6G Upper Midbands
Juseong Park, Foad Sohrabi, Amitava Ghosh +1
This paper proposes and analyzes novel deep learning methods for downlink (DL) single-user multiple-input multiple-output (SU-MIMO) and multi-user MIMO (MU-MIMO) systems operating…
Active Sensing for Two-Sided Beam Alignment and Reflection Design Using Ping-Pong Pilots
Tao Jiang, Foad Sohrabi, Wei Yu
Beam alignment is an important task for millimeter-wave (mmWave) communication, because constructing aligned narrow beams both at the transmitter (Tx) and the receiver (Rx) is cruc…
Role of Deep Learning in Wireless Communications
Wei Yu, Foad Sohrabi, Tao Jiang
Traditional communication system design has always been based on the paradigm of first establishing a mathematical model of the communication channel, then designing and optimizing…
Learning Progressive Distributed Compression Strategies from Local Channel State Information
Foad Sohrabi, Tao Jiang, Wei Yu
This paper proposes a deep learning framework to design distributed compression strategies in which distributed agents need to compress high-dimensional observations of a source, t…
Active Sensing for Communications by Learning
Foad Sohrabi, Tao Jiang, Wei Cui +1
This paper proposes a deep learning approach to a class of active sensing problems in wireless communications in which an agent sequentially interacts with an environment over a pr…
Hybrid Analog and Digital Beamforming Design for Channel Estimation in Correlated Massive MIMO Systems
Javad Mirzaei, Shahram ShahbazPanahi, Foad Sohrabi +1
In this paper, we study the channel estimation problem in correlated massive multiple-input-multiple-output (MIMO) systems with a reduced number of radio-frequency (RF) chains. Imp…
Sparse Activity Detection in Multi-Cell Massive MIMO Exploiting Channel Large-Scale Fading
Zhilin Chen, Foad Sohrabi, Wei Yu
This paper studies the device activity detection problem in a multi-cell massive multiple-input multiple-output (MIMO) system, in which the active devices transmit signature sequen…
An Efficient Active Set Algorithm for Covariance Based Joint Data and Activity Detection for Massive Random Access with Massive MIMO
Ziyue Wang, Zhilin Chen, Ya-Feng Liu +2
This paper proposes a computationally efficient algorithm to solve the joint data and activity detection problem for massive random access with massive multiple-input multiple-outp…
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…
Multi-Cell Sparse Activity Detection for Massive Random Access: Massive MIMO versus Cooperative MIMO
Zhilin Chen, Foad Sohrabi, Wei Yu
This paper considers sparse device activity detection for cellular machine-type communications with non-orthogonal signatures using the approximate message passing algorithm. This…
Generalized Approximate Message Passing for Massive MIMO mmWave Channel Estimation with Laplacian Prior
Faouzi Bellili, Foad Sohrabi, Wei Yu
This paper tackles the problem of millimeter-Wave (mmWave) channel estimation in massive MIMO communication systems. A new Bayes-optimal channel estimator is derived using recent a…
One-Bit Precoding and Constellation Range Design for Massive MIMO with QAM Signaling
Foad Sohrabi, Ya-Feng Liu, Wei Yu
The use of low-resolution digital-to-analog converters (DACs) for transmit precoding provides crucial energy efficiency advantage for massive multiple-input multiple-output (MIMO)…
Sparse Activity Detection for Massive Connectivity
Zhilin Chen, Foad Sohrabi, Wei Yu
This paper considers the massive connectivity application in which a large number of potential devices communicate with a base-station (BS) in a sporadic fashion. The detection of…
Hybrid Analog and Digital Beamforming for mmWave OFDM Large-Scale Antenna Arrays
Foad Sohrabi, Wei Yu
Hybrid analog and digital beamforming is a promising candidate for large-scale mmWave MIMO systems because of its ability to significantly reduce the hardware complexity of the con…
Coordinate Update Algorithms for Robust Power Loading for the MU-MISO Downlink with Outage Constraints
Foad Sohrabi, Timothy N. Davidson
We consider the problem of power allocation for the single-cell multi-user (MU) multiple-input single-output (MISO) downlink with quality-of-service (QoS) constraints. The base sta…
Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays
Foad Sohrabi, Wei Yu
The potential of using of millimeter wave (mmWave) frequency for future wireless cellular communication systems has motivated the study of large-scale antenna arrays for achieving…