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Found 13 papers, 3 papers with code
Imperfect Digital Twin Assisted Low Cost Reinforcement Training for Multi-UAV Networks
Two cascade neural networks (NN) are used to optimize the joint number of virtually generated UAVs, the DT construction cost, and the performance of multi-UAV networks.
Label-free Deep Learning Driven Secure Access Selection in Space-Air-Ground Integrated Networks
Considering the inherent co-channel interference due to spectrum sharing among multi-tier access networks in SAGIN, it can be leveraged to assist the physical layer security among heterogeneous transmissions.
Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach
As a promising paradigm federated Learning (FL) is widely used in privacy-preserving machine learning, which allows distributed devices to collaboratively train a model while avoiding data transmission among clients.
Mobility-Aware Computation Offloading for Swarm Robotics using Deep Reinforcement Learning
Therefore, current swarm robotics have a small number of robots, which can only provide limited spatio-temporal information.
Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach
To fully leverage the high performance of traditional model-based methods and the low complexity of the NN-based method, a knowledge distillation (KD) based algorithm distillation (AD) method is proposed in this paper to improve the performance and convergence speed of the NN-based method, where traditional SINR optimization methods are employed as ``teachers" to assist the training of NNs, which are ``students", thus enhancing the performance of unsupervised and reinforcement learning techniques.
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles
By offloading computation-intensive tasks of vehicles to roadside units (RSUs), mobile edge computing (MEC) in the Internet of Vehicles (IoV) can relieve the onboard computation burden.
Interpretable and Secure Trajectory Optimization for UAV-Assisted Communication
Unmanned aerial vehicles (UAVs) have gained popularity due to their flexible mobility, on-demand deployment, and the ability to establish high probability line-of-sight wireless communication.
Collision Avoidance
Explainable Artificial Intelligence (XAI)
Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach
However, the dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: low scalability and high training costs.
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning
In this paper, to deal with the heterogeneity in federated learning (FL) systems, a knowledge distillation (KD) driven training framework for FL is proposed, where each user can select its neural network model on demand and distill knowledge from a big teacher model using its own private dataset.
SigT: An Efficient End-to-End MIMO-OFDM Receiver Framework Based on Transformer
Multiple-input multiple-output and orthogonal frequency-division multiplexing (MIMO-OFDM) are the key technologies in 4G and subsequent wireless communication systems.
Joint Flying Relay Location and Routing Optimization for 6G UAV–IoT Networks: A Graph Neural Network-Based Approach
In the training procedure, we design a reinforcement learning-based relay GNN (RGNN) to select the best relay path for each user.
On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks
On-demand service provisioning is a critical yet challenging issue in 6G wireless communication networks, since emerging services have significantly diverse requirements and the network resources become increasingly heterogeneous and dynamic.
Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling
We use DT to simulate the results of different decisions made by the agent, so that one agent can try multiple actions at a time, or, similarly, multiple agents can interact with environment in parallel in DT.
