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Transmit beamforming for physical-layer multicasting
This paper considers the problem of downlink transmit beamforming for wireless transmission and downstream precoding for digital subscriber wireline transmission, in the context of common information
Quality of Service and Max-Min Fair Transmit Beamforming to Multiple Cochannel Multicast Groups
It is shown that Lagrangian relaxation coupled with suitable randomization/cochannel multicast power control yield computationally efficient high-quality approximate solutions.
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
A joint DR and K-means clustering approach in which DR is accomplished via learning a deep neural network (DNN) while exploiting theDeep neural network's ability to approximate any nonlinear function is proposed.
Blind PARAFAC receivers for DS-CDMA systems
This paper links the direct-sequence code-division multiple access (DS-CDMA) multiuser separation-equalization-detection problem to the parallel factor (PARAFAC) model, which is an analysis tool
Tensor Decomposition for Signal Processing and Machine Learning
The material covered includes tensor rank and rank decomposition; basic tensor factorization models and their relationships and properties; broad coverage of algorithms ranging from alternating optimization to stochastic gradient; statistical performance analysis; and applications ranging from source separation to collaborative filtering, mixture and topic modeling, classification, and multilinear subspace learning.
Parallel factor analysis in sensor array processing
This link facilitates the derivation of powerful identifiability results for MI-SAP, shows that the uniqueness of single- and multiple-invariance ESPRIT stems from uniqueness of low-rank decomposition of three-way arrays, and allows tapping on the available expertise for fitting the PARAFAC model.
SPLATT: Efficient and Parallel Sparse Tensor-Matrix Multiplication
Multi-dimensional arrays, or tensors, are increasingly found in fields such as signal processing and recommender systems. Real-world tensors can be enormous in size and often very sparse. There is a
Convex approximation techniques for joint multiuser downlink beamforming and admission control
This work advocates a cross-layer approach to joint multiuser transmit beamforming and admission control, aiming to maximize the number of users that can be served at their desired QoS.
Consensus-ADMM for General Quadratically Constrained Quadratic Programming
The core components are carefully designed to make the overall algorithm more scalable, including efficient methods for solving QCQP-1, memory efficient implementation, parallel/distributed implementation, and smart initialization.
Khatri-Rao space-time codes
A novel linear block coding scheme based on the Khatri-Rao matrix product is proposed, which is shown to have numerous desirable properties, including guaranteed unique linear decodability, built-in blind channel identifiability, and efficient near-maximum likelihood decoding.