31 accepted papers
Given an undirected graph and a size parameter k, the Densest k-Subgraph (DkS) problem extracts the subgraph on k vertices with the largest number of induced edges. While DkS is NP--hard and difficult to approximate, penalty-based continuous relaxations of the problem have recently enjoyed practical
Multilayer matrix factorization (MMF) has recently emerged as a generalized model of, and potentially a more expressive approach than, the classic matrix factorization. This paper considers MMF under a probabilistic formulation, and our focus is on inference methods under variational inference. The…
Cardinality-constrained binary quadratic optimization appears in various applications such as finding a densest size-constrained subgraph from a graph. It is a challenging combinatorial problem, and in this paper we tackle the problem by a continuous optimization approach. Our method, called the ext…
Simplex component analysis (SCA) aims to estimate the vertices of the convex hull where data samples reside in. SCA finds various applications in signal processing, e.g., hyperspectral unmixing and noisy label learning. Recent works proposed to tackle SCA from a probabilistic viewpoint using variati…
The present work studies symbol-level precoding (SLP) for multiuser multi-input single-output (MISO) downlink channels and with imperfect channel state information (CSI) at the transmitter. SLP has gained significant interest in recent years because it is capable of enhancing symbol-level performanc…
Transmitting data using the phases on reconfigurable intelligent surfaces (RIS) is a promising solution for future energy-efficient communication systems. Recent work showed that a virtual phased massive multiuser multiple-input-multiple-out (MIMO) transmitter can be formed using only one active ant…
In this paper we develop a simple scheme for a coupled matrix factorization problem arising in the topic of hyperspectral super-resolution (HSR). HSR considers the problem of recovering a super-resolution image from a multispectral image and a hyperspectral image, which have lower spectral and spati…
Symbol-level precoding (SLP) has received tremendous interest in MIMO communications in recent years. In particular, much attention has been paid to the formulation and optimization aspects. In this paper we contribute to these aspects by drawing a connection between SLP and a seemingly unrelated to…
Unit-modulus constrained optimization is frequently encountered in many engineering problems. In our recent study for massive MIMO precoding, we devised a penalty method for unit-modulus optimization. In this paper, we revisit this penalty method in several other unit-modulus applications in signal…
In this paper we examine the application of variational inference (VI) to MIMO detection. Our study is motivated by the recent interest in applying machine learning concepts to signal processing. VI is an approach for providing friendly approximations of certain intractable posterior probabilities i…
Adopting one-bit analog-to-digital convertors (ADCs) for massive multiple-input multiple-output (MIMO) implementations has great potential in reducing the hardware cost and power consumption. However, distortions caused by quantization raise great challenges. In MIMO orthogonal frequency-division mo…
In federated learning, models are learned from users’ data that are held private in their edge devices, by aggregating them in the service provider’s “cloud” to obtain a global model. Such global model is of great commercial value in, e.g., improving the customers’ experience. In this paper we focus…
A family of computationally efficient DOA estimators under the partial relaxation framework has recently been proposed. In this framework, the manifold structure of the "interfering" signals is relaxed, and only the manifold structure of one desired signal is retained. This particular type of relaxa…
Massive MIMO using low-resolution digital-to-analog converters (DACs) at the base station (BS) is an attractive downlink approach for reducing hardware overhead and for reducing power consumption, but managing the large quantization noise effect is a challenge. Spatial Sigma-Delta (ΣΔ) modulation is…
This paper studies nonconvex quadratically constrained quadratic program (QCQP), which is known to be NP-hard in general. In the past decades, various approximate approaches have been developed to tackle the QCQP, including semidefinite relaxation (SDR), successive convex approximation (SCA), the va…
Hyperspectral unmixing (HU) is a problem of blindly identifying the underlying materials, in form of spectral signatures, in the captured hyperspectral image. HU has received tremendous interest in remote sensing, and fundamentally the problem can be regarded as solving a simplex-structured matrix f…
This paper considers symbol-level precoding (SLP) for the multiuser multiple-input single-output (MISO) downlink scenario. By exploiting symbol constellation information, SLP has the ability to achieve much better performance than traditional linear beamforming schemes. In this work, we propose an S…
This paper considers multiuser massive MIMO downlink transmission, where the base station (BS) employs a massive number of transmit antennas, each equipped with a low-resolution phase shifter, to simultaneously shape desired symbols at user side, after passing through the channels and receive beamfo…
Simplex-structured matrix factorization (SSMF) is a problem of recovering a basis matrix and the corresponding coefficient vectors from data, where the coefficient vectors are constrained to lie in the unit simplex. SSMF has attracted growing attention in recent years, with numerous applications suc…
Hyperspectral super-resolution (HSR) is a problem of recovering a high-spectral-spatial-resolution image from a multispectral measurement and a hyperspectral measurement, which have low spectral and spatial resolutions, respectively. We consider a low-rank structured matrix factorization formulation…
This work focuses on the problem of fusing a hyperspectral image (HSI) and a multispectral image (MSI) to produce a super-resolution image that admits high spatial and spectral resolutions. Existing algorithms are mostly based on joint low-rank factorization of the ma-tricized HSI and MSI. This fram…
Massive multiple-input multiple-output (MIMO) has the potential to substantially improve the spectral efficiency, robustness and coverage of mobile networks. However, such potential is limited by hardware cost and power consumption associated with a large number of RF chains. Recently, one-bit quant…
This paper considers symbol-level precoding (SLP) for multiuser multiple-input single-output (MISO) downlink. SLP is a nonlinear precoding scheme that utilizes symbol constellation structures. It has been shown that SLP can outperform the popular linear beamforming scheme. In this work we reveal a h…
This paper focuses on an average robust transmit beamforming optimization problem for the multiuser multiple-input-single-output (MISO) downlink scenario. In this problem, the channels are modeled as Gaussian variables with mean zero and with known covariance at the transmitter. The design criterion…
Consider a structured matrix factorizaton (SMF) whose coefficient vectors are constrained to lie in the unit simplex. This kind of simplex SMF (SSMF) has received growing attention and has found many applications such as hyperspectral unmixing in remote sensing, text mining in machine learning, and…
This paper considers practical deployment issues of a multi-user MIMO system with full-duplex (FD) base station and half-duplex (HD) user equipment. The aim is to select a set of uplink (UL) and downlink (DL) users at any instant that will provide a satisfactory performance in system resource alloca…
In this work, we consider the robust beamforming design for secondary downlink multicasting channels, where primary users are present with norm-bounded channel errors. In particular, the max-min-fair formulation is considered and the resulting design problem is a quadratically constrained quadratic…
This paper considers a special subclass of separable semidefinite programs (SDPs), with the goal of identifying certain conditions under which the SDP has a low-rank solution. We prove that when the data matrices of the SDP satisfy certain matrix inequalities, the SDP has a low-rank solution. Moreov…
This paper focuses on volume minimization (VolMin)-based structured matrix factorization (SMF), which factors a data matrix into a full-column rank basis and a coefficient matrix whose columns reside in the unit simplex. The VolMin criterion achieves this goal via finding a minimum-volume enclosing…
In this paper, we consider a cloud relay network (C-RN) which provides reliable communication between long-distance users. Specifically, we study the amplify-and-forward (AF) schemes in C-RNs. In our scenario setting, with the cloud processor units fully coordinating in the network, the C-RN can be…
We consider the design of the linear precoder for a multiple-input single-output (MISO) downlink in a system that employs limited feedback using Grassmannian quantization. The goal is to minimize the outage probability of a target signal-to-interference-and noise ratio (SINR) under a transmitted pow…