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Found 30 papers, 3 papers with code
Recursive GNNs for Learning Precoding Policies with Size-Generalizability
Then, we design size-generalizable GNNs that are with these key characteristics and satisfy the PE properties of precoding policies in a recursive manner.
(Why) Is My Prompt Getting Worse? Rethinking Regression Testing for Evolving LLM APIs
Large Language Models (LLMs) are increasingly integrated into software applications.
Beyond Testers' Biases: Guiding Model Testing with Knowledge Bases using LLMs
Current model testing work has mostly focused on creating test cases.
Learning Resource Allocation Policy: Vertex-GNN or Edge-GNN?
We find that the expressive power of the GNNs depends on the linearity and output dimensions of the processing and combination functions.
LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMs
We reflect on human and LLMs' different sensitivities to instructions, stress the importance of enabling human-facing safeguards for LLMs, and discuss the potential of training humans and LLMs with complementary skill sets.
A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges
We also found the most important reasons why developers do not use these tools are because these tools do not output code that addresses certain functional or non-functional requirements and because developers have trouble controlling the tool to generate the desired output.
Multidimensional Graph Neural Networks for Wireless Communications
Based on the observation that the mismatched permutation property from the policies and the information loss during the update of hidden representations have large impact on the learning performance and efficiency, in this paper we propose a unified framework to learn permutable wireless policies with multidimensional GNNs.
A Model-based GNN for Learning Precoding
Simulation results show that the proposed GNN can well learn spectral efficient and energy efficient precoding policies in single- and multi-cell multi-user multi-antenna systems with low training complexity, and can be well generalized to the numbers of users.
Understanding the Performance of Learning Precoding Policy with GNN and CNNs
Learning-based precoding has been shown able to be implemented in real-time, jointly optimized with channel acquisition, and robust to imperfect channels.
Capabilities for Better ML Engineering
In spite of machine learning's rapid growth, its engineering support is scattered in many forms, and tends to favor certain engineering stages, stakeholders, and evaluation preferences.
Graph Reinforcement Learning for Radio Resource Allocation
Deep reinforcement learning (DRL) for resource allocation has been investigated extensively owing to its ability of handling model-free and end-to-end problems.
Deep Reinforcement Learning Aided Packet-Routing For Aeronautical Ad-Hoc Networks Formed by Passenger Planes
Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology.
Deep Reinforcement Learning with Symmetric Prior for Predictive Power Allocation to Mobile Users
Deep reinforcement learning has been applied for a variety of wireless tasks, which is however known with high training and inference complexity.
Federated Learning Based Proactive Handover in Millimeter-wave Vehicular Networks
Proactive handover can avoid frequent handovers and reduce handover delay, which plays an important role in maintaining the quality of service (QoS) for mobile users in millimeter-wave vehicular networks.
Multicell Power Control under Rate Constraints with Deep Learning
In the paper we study a deep learning based method to solve the multicell power control problem for sum rate maximization subject to per-user rate constraints and per-base station (BS) power constraints.
Learning Power Control for Cellular Systems with Heterogeneous Graph Neural Network
In this paper, we show that the power control policy has a combination of different PI and PE properties, and existing HetGNN does not satisfy these properties.
A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning
As one of the key communication scenarios in the 5th and also the 6th generation (6G) of mobile communication networks, ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications.
Unsupervised Deep Learning for Optimizing Wireless Systems with Instantaneous and Statistic Constraints
Deep neural networks (DNNs) have been introduced for designing wireless policies by approximating the mappings from environmental parameters to solutions of optimization problems.
Improving Learning Efficiency for Wireless Resource Allocation with Symmetric Prior
The samples required to train a DNN after ranking can be reduced by $15 \sim 2, 400$ folds to achieve the same system performance as the counterpart without using prior.
Accelerating Deep Reinforcement Learning With the Aid of Partial Model: Energy-Efficient Predictive Video Streaming
Predictive power allocation is conceived for energy-efficient video streaming over mobile networks using deep reinforcement learning.
Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks
In this article, we first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC, and discuss some open problems of these methods.
Constructing Deep Neural Networks with a Priori Knowledge of Wireless Tasks
In this paper, we show that two kinds of permutation invariant properties widely existed in wireless tasks can be harnessed to reduce the number of model parameters and hence the sample and computational complexity for training.
Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning
If the objective and constraint functions are unavailable, reinforcement learning can be applied to find the solution of a functional optimization problem, which is however not tailored to optimization problems in wireless networks.
Structure of Deep Neural Networks with a Priori Information in Wireless Tasks
Deep neural networks (DNNs) have been employed for designing wireless networks in many aspects, such as transceiver optimization, resource allocation, and information prediction.
Proactive Optimization with Machine Learning: Femto-caching with Future Content Popularity
In this paper, we introduce a proactive optimization framework for anticipatory resource allocation, where the future information is implicitly predicted under the same objective with the policy optimization in a single step.
Model-Free Unsupervised Learning for Optimization Problems with Constraints
In many optimization problems in wireless communications, the expressions of objective function or constraints are hard or even impossible to derive, which makes the solutions difficult to find.
Learning to Optimize with Unsupervised Learning: Training Deep Neural Networks for URLLC
Learning the optimized solution as a function of environmental parameters is effective in solving numerical optimization in real time for time-sensitive applications.
Unsupervised Deep Learning for Ultra-reliable and Low-latency Communications
In this paper, we study how to solve resource allocation problems in ultra-reliable and low-latency communications by unsupervised deep learning, which often yield functional optimization problems with quality-of-service (QoS) constraints.
Deep Reinforcement Learning for Resource Management in Network Slicing
Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices.
A Learning-based Approach to Joint Content Caching and Recommendation at Base Stations
We then formulate a joint caching and recommendation problem maximizing the successful offloading probability, which is a mixed integer programming problem.
