Search Results for author:
Found 28 papers, 8 papers with code
Learning Based User Scheduling in Reconfigurable Intelligent Surface Assisted Multiuser Downlink
Reconfigurable intelligent surface (RIS) is capable of intelligently manipulating the phases of the incident electromagnetic wave to improve the wireless propagation environment between the base-station (BS) and the users.
Active Sensing for Communications by Learning
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 predetermined number of time frames to gather information in order to perform a sensing or actuation task for maximizing some utility function.
SSDL: Self-Supervised Dictionary Learning
Inspired by the concept of self-supervised learning (e. g., setting the pretext task to generate a universal model for the downstream task), we propose a Self-Supervised Dictionary Learning (SSDL) framework to address this challenge.
Accelerating Coordinate Descent via Active Set Selection for Device Activity Detection for Multi-Cell Massive Random Access
Specifically, at each iteration, the proposed active set CD algorithm first selects a small subset of all devices, namely the active set, which contains a few devices that contribute the most to the deviation from the first-order optimality condition of the MLE problem thus potentially can provide the most improvement to the objective function, then applies the CD algorithm to perform the detection for the devices in the active set.
What Makes an Effective Scalarising Function for Multi-Objective Bayesian Optimisation?
Performing multi-objective Bayesian optimisation by scalarising the objectives avoids the computation of expensive multi-dimensional integral-based acquisition functions, instead of allowing one-dimensional standard acquisition functions\textemdash such as Expected Improvement\textemdash to be applied.
An Efficient Active Set Algorithm for Covariance Based Joint Data and Activity Detection for Massive Random Access with Massive MIMO
Specifically, at each iteration, the proposed algorithm focuses on only a small subset of all potential sequences, namely the active set, which contains a few most likely active sequences (i. e., transmitted sequences by all active devices), and performs the detection for the sequences in the active set.
Scalable Deep Reinforcement Learning for Routing and Spectrum Access in Physical Layer
This paper proposes a novel scalable reinforcement learning approach for simultaneous routing and spectrum access in wireless ad-hoc networks.
Modular Action Concept Grounding in Semantic Video Prediction
Recent works in video prediction have mainly focused on passive forecasting and low-level action-conditional prediction, which sidesteps the learning of interaction between agents and objects.
Learning to Reflect and to Beamform for Intelligent Reflecting Surface with Implicit Channel Estimation
Intelligent reflecting surface (IRS), which consists of a large number of tunable reflective elements, is capable of enhancing the wireless propagation environment in a cellular network by intelligently reflecting the electromagnetic waves from the base-station (BS) toward the users.
Action Concept Grounding Network for Semantically-Consistent Video Generation
Recent works in self-supervised video prediction have mainly focused on passive forecasting and low-level action-conditional prediction, which sidesteps the problem of semantic learning.
Grant-Free Access via Bilinear Inference for Cell-Free MIMO with Low-Coherent Pilots
We propose a novel joint activity, channel, and data estimation (JACDE) scheme for cell-free multiple-input multiple-output (MIMO) systems compliant with fifth-generation (5G) new radio (NR) orthogonal frequency-division multiplexing (OFDM) signaling.
Products-10K: A Large-scale Product Recognition Dataset
With the rapid development of electronic commerce, the way of shopping has experienced a revolutionary evolution.
Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO
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 frequency-division duplex massive multiple-input multiple-output system in which a base station (BS) serves multiple mobile users, but with rate-limited feedback from the users to the BS.
Information Theory Signal Processing Information Theory
Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks
Network densification and millimeter-wave technologies are key enablers to fulfill the capacity and data rate requirements of the fifth generation (5G) of mobile networks.
Efficient and Information-Preserving Future Frame Prediction and Beyond
Applying resolution-preserving blocks is a common practice to maximize information preservation in video prediction, yet their high memory consumption greatly limits their application scenarios.
Ranked #1 on
Video Prediction
on Moving MNIST
Adaptive Semantic-Visual Tree for Hierarchical Embeddings
To solve this new problem, we propose a hierarchical adaptive semantic-visual tree (ASVT) to depict the architecture of merchandise categories, which evaluates semantic similarities between different semantic levels and visual similarities within the same semantic class simultaneously.
Joint User Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA
To address the problem that the exact calculation of the messages exchanged within CSCE and between the two modules is complicated due to phase ambiguity issues, this paper proposes a rotationally invariant Gaussian mixture (RIGM) model, and develops an efficient JUICESD-RIGM algorithm.
CrevNet: Conditionally Reversible Video Prediction
Applying resolution-preserving blocks is a common practice to maximize information preservation in video prediction, yet their high memory consumption greatly limits their application scenarios.
Energy-Efficient Processing and Robust Wireless Cooperative Transmission for Edge Inference
Edge machine learning can deliver low-latency and private artificial intelligent (AI) services for mobile devices by leveraging computation and storage resources at the network edge.
Recovering the parameters underlying the Lorenz-96 chaotic dynamics
Climate projections suffer from uncertain equilibrium climate sensitivity.
Spatial Deep Learning for Wireless Scheduling
The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task.
Towards Optimal Power Control via Ensembling Deep Neural Networks
A deep neural network (DNN) based power control method is proposed, which aims at solving the non-convex optimization problem of maximizing the sum rate of a multi-user interference channel.
Transaction Fraud Detection Using GRU-centered Sandwich-structured Model
Rapid growth of modern technologies such as internet and mobile computing are bringing dramatically increased e-commerce payments, as well as the explosion in transaction fraud.
Neural EPI-Volume Networks for Shape From Light Field
In the first part the network encodes relevant information from the given input into a set of high-level feature maps.
On learning optimized reaction diffusion processes for effective image restoration
We propose to train the parameters of the filters and the influence functions through a loss based approach.
Visualizing and Comparing Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have achieved comparable error rates to well-trained human on ILSVRC2014 image classification task.
Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data
Image retrieval refers to finding relevant images from an image database for a query, which is considered difficult for the gap between low-level representation of images and high-level representation of queries.
The state-of-the-art in web-scale semantic information processing for cloud computing
Based on integrated infrastructure of resource sharing and computing in distributed environment, cloud computing involves the provision of dynamically scalable and provides virtualized resources as services over the Internet.
