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Found 10 papers, 0 papers with code
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration
In this paper, a joint O-RAN/MEC orchestration using a Bayesian deep reinforcement learning (RL)-based framework is proposed that jointly controls the O-RAN functional splits, the allocated resources and hosting locations of the O-RAN/MEC services across geo-distributed platforms, and the routing for each O-RAN/MEC data flow.
Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models
This way, we make the constellation symbols sent by the transmitter, and what is inferred (reconstructed) at the receiver become as similar as possible, resulting in as few mismatches as possible.
Denoising Diffusion Probabilistic Models for Hardware-Impaired Communication Systems: Towards Wireless Generative AI
Thanks to the outstanding achievements from state-of-the-art generative models like ChatGPT and diffusion models, generative AI has gained substantial attention across various industrial and academic domains.
Diffusion Models for Wireless Communications
In this article, we outline the applications of diffusion models in wireless communication systems, which are a new family of probabilistic generative models that have showcased state-of-the-art performance.
Deep Learning-Based Active User Detection for Grant-free SCMA Systems
Grant-free random access and uplink non-orthogonal multiple access (NOMA) have been introduced to reduce transmission latency and signaling overhead in massive machine-type communication (mMTC).
Deep Contextual Bandits for Fast Neighbor-Aided Initial Access in mmWave Cell-Free Networks
Access points (APs) in millimeter-wave (mmWave) and sub-THz-based user-centric (UC) networks will have sleep mode functionality.
Elevated LiDAR based Sensing for 6G -- 3D Maps with cm Level Accuracy
However, LiDARs are power hungry devices that generate a lot of data, and these characteristics limit their use as on-board sensors in robots.
Event-Driven Source Traffic Prediction in Machine-Type Communications Using LSTM Networks
This is done by restructuring the transmission data in a way that the LSTM network can identify the causal relationship between the devices.
Deep Contextual Bandits for Fast Initial Access in mmWave Based User-Centric Ultra-Dense Networks
In this paper, we propose a novel deep contextual bandit (DCB) based approach to perform fast and efficient IA in mmWave based UC UD networks.
Cyber-Physical Security and Safety of Autonomous Connected Vehicles: Optimal Control Meets Multi-Armed Bandit Learning
For sensors having a prior information, a DIA detection approach is proposed and an optimal threshold level is derived for the difference between the actual and estimated values of sensors data which enables ACV to stay robust against cyber attacks.
