47 citations · 49 across the 6 of their papers we have counts for
9 papers
Connections Between Quadratic Transform for Fractional Programming and Schur Complement
Kaiming Shen, Kareem M. Attiah, Yannan Chen +1
This paper shows that there are intimate connections between the quadratic transform technique for solving fractional programming (FP) problems and the Schur-complement technique i…
Active MIMO Sensing With Exploration-Exploitation Tradeoff
Nadim Ghaddar, Kareem M. Attiah, Wei Yu
This paper develops an active sensing framework for designing the transmit and receive beamformers of a multiple-input multiple-output (MIMO) radar system. In the proposed techniqu…
Uplink-Downlink Duality for Beamforming in Integrated Sensing and Communications
Kareem M. Attiah, Wei Yu
This paper considers the beamforming and power optimization problem for a class of integrated sensing and communications (ISAC) problems that utilize the communication signals simu…
How Many Simultaneous Beamformers are Needed for Integrated Sensing and Communications?
Kareem M. Attiah, Wei Yu
Consider a downlink integrated sensing and communications (ISAC) system in which a base station employs linear beamforming to communicate to users, while simultaneously uses se…
RIS-Assisted Joint Sensing and Communications via Fractionally Constrained Fractional Programming
Yiming Liu, Kareem M. Attiah, Wei Yu
This paper studies an uplink dual-functional sensing and communication system aided by a reconfigurable intelligent surface (RIS), whose reflection pattern is configured to trade-o…
Coded Downlink Massive Random Access and a Finite de Finetti Theorem
Ryan Song, Kareem M. Attiah, Wei Yu
This paper considers a massive connectivity setting in which a base-station (BS) aims to communicate sources to a randomly activated subset of users, among a…
Beamforming Design for Integrated Sensing and Communications Using Uplink-Downlink Duality
Kareem M. Attiah, Wei Yu
This paper presents a novel optimization framework for beamforming design in integrated sensing and communication systems where a base station seeks to minimize the Bayesian Cramér…
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…