activity
20112021
most citedMIMO OFDM Dual-Function Radar-Communication Under Error Rate and Beampattern Constraints

119 citations · 137 across the 8 of their papers we have counts for

collaborators

8 papers

eess.SP2021★ 119 cited

MIMO OFDM Dual-Function Radar-Communication Under Error Rate and Beampattern Constraints

Jeremy Johnston, Luca Venturino, Emanuele Grossi +2

In this work we consider a multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system, which senses multiple spatial directions and serves multiple users…

cs.IT2021

Reconfigurable-intelligent-surface-assisted Downlink Transmission Design via Bayesian Optimization

Dong Wang, Xiaodong Wang, Fanggang Wang

This paper investigates the transmission design in the reconfigurable-intelligent-surface (RIS)-assisted downlink system. The channel state information (CSI) is usually difficult t…

stat.ML2017★ 4 cited

Scaled Nuclear Norm Minimization for Low-Rank Tensor Completion

Morteza Ashraphijuo, Xiaodong Wang

Minimizing the nuclear norm of a matrix has been shown to be very efficient in reconstructing a low-rank sampled matrix. Furthermore, minimizing the sum of nuclear norms of matrici…

cs.LG2017★ 1 cited

Rank Determination for Low-Rank Data Completion

Morteza Ashraphijuo, Xiaodong Wang, Vaneet Aggarwal

Recently, fundamental conditions on the sampling patterns have been obtained for finite completability of low-rank matrices or tensors given the corresponding ranks. In this paper,…

eess.SY2017★ 2 cited

Improved NN-JPDAF for Joint Multiple Target Tracking and Feature Extraction

Le Zheng, Xiaodong Wang

Feature aided tracking can often yield improved tracking performance over the standard multiple target tracking (MTT) algorithms with only kinematic measurements. However, in many…

cs.LG2017★ 5 cited

Characterization of Deterministic and Probabilistic Sampling Patterns for Finite Completability of Low Tensor-Train Rank Tensor

Morteza Ashraphijuo, Xiaodong Wang

In this paper, we analyze the fundamental conditions for low-rank tensor completion given the separation or tensor-train (TT) rank, i.e., ranks of unfoldings. We exploit the algebr…