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Found 17 papers, 2 papers with code
Beam Training in mmWave Vehicular Systems: Machine Learning for Decoupling Beam Selection
Codebook-based beam selection is one approach for configuring millimeter wave communication links.
Coding for Gaussian Two-Way Channels: Linear and Learning-Based Approaches
In this work, we aim to enhance and balance the communication reliability in GTWCs by minimizing the sum of error probabilities via joint design of encoders and decoders at the users.
Class Label-aware Graph Anomaly Detection
Unsupervised GAD methods assume the lack of anomaly labels, i. e., whether a node is anomalous or not.
Label-based Graph Augmentation with Metapath for Graph Anomaly Detection
To further efficiently exploit context information from metapath-based anomaly subgraph, we present a new framework, Metapath-based Graph Anomaly Detection (MGAD), incorporating GCN layers in both the dual-encoders and decoders to efficiently propagate context information between abnormal and normal nodes.
Graph Anomaly Detection
Semi-supervised Anomaly Detection
+1
Rate-Splitting Multiple Access: The First Prototype and Experimental Validation of its Superiority over SDMA and NOMA
In multi-user multi-antenna communications, it is well-known in theory that Rate-Splitting Multiple Access (RSMA) can achieve a higher spectral efficiency than both Space Division Multiple Access (SDMA) and Non-Orthogonal Multiple Access (NOMA).
Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning
The design of codes for feedback-enabled communications has been a long-standing open problem.
Learning-Based Adaptive User Selection in Millimeter Wave Hybrid Beamforming Systems
We consider a multi-user hybrid beamforming system, where the multiplexing gain is limited by the small number of RF chains employed at the base station (BS).
Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks
Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can alter the wireless propagation environment through design of their reflection coefficients.
No Task Left Behind: Multi-Task Learning of Knowledge Tracing and Option Tracing for Better Student Assessment
One of the most common approach to student assessment is Knowledge Tracing (KT), which evaluates a student's knowledge state by predicting whether the student will answer a given question correctly or not.
Learning-Based Adaptive IRS Control with Limited Feedback Codebooks
We consider a practical setting where (i) the IRS reflection coefficients are achieved by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station to the IRS has a low data rate.
Closed-Loop Wireless Power Transfer with Adaptive Waveform and Beamforming: Design, Prototype, and Experiment
The transmitter sweeps through the codebook and then the receiver feeds back the index of the optimal codeword, so that the waveform and beamforming can be adapted to the multipath fading channel to maximize the output dc power without requiring explicit channel estimation and the knowledge of accurate Channel State Information.
Multi-IRS-assisted Multi-Cell Uplink MIMO Communications under Imperfect CSI: A Deep Reinforcement Learning Approach
Applications of intelligent reflecting surfaces (IRSs) in wireless networks have attracted significant attention recently.
Wireless Power Transfer with Distributed Antennas: System Design, Prototype, and Experiments
The measurements show that WPT DAS can boost the output dc power by up to 30 dB in single-user case and boost the sum of output dc power by up to 21. 8 dB in two-user case and broaden the service coverage area in a low cost, low complexity, and flexible manner.
Range Expansion for Wireless Power Transfer using Joint Beamforming and Waveform Architecture: An Experimental Study in Indoor Environment
Far-field Wireless Power Transfer (WPT) has emerged as a potential power source for the Internet of Things (IoT) and Wireless Sensor Network (WSN). The expansion of the power transfer range is one of the key challenges to make the technology viable.
Information Theory Information Theory
Minimum Overhead Beamforming and Resource Allocation in D2D Edge Networks
Device-to-device (D2D) communications is expected to be a critical enabler of distributed computing in edge networks at scale.
Prescribing Deep Attentive Score Prediction Attracts Improved Student Engagement
To that end, we apply a state-of-the-art deep attentive neural network-based score prediction model to Santa, a multi-platform English ITS with approximately 780K users in South Korea that exclusively focuses on the TOEIC (Test of English for International Communications) standardized examinations.
Joint Optimization of Signal Design and Resource Allocation in Wireless D2D Edge Computing
In this paper, unlike previous mobile edge computing (MEC) approaches, we propose a joint optimization of wireless MIMO signal design and network resource allocation to maximize energy efficiency.
Networking and Internet Architecture Signal Processing
