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Found 10 papers, 0 papers with code
Preserving Data Privacy for ML-driven Applications in Open Radio Access Networks
Deep learning offers a promising solution to improve spectrum access techniques by utilizing data-driven approaches to manage and share limited spectrum resources for emerging applications.
ASCENT: A Context-Aware Spectrum Coexistence Design and Implementation Toolset for Policymakers in Satellite Bands
It serves two essential purposes (a) evaluating the potential for harmful interference to primary users in satellite bands and (b) facilitating the analysis, design, and implementation of diverse regulatory policies on spectrum usage and sharing.
Keep It Simple: CNN Model Complexity Studies for Interference Classification Tasks
However, there have been limited research on the complexity of such deep learning models.
AI Testing Framework for Next-G O-RAN Networks: Requirements, Design, and Research Opportunities
This article presents a general automated, distributed and AI-enabled testing framework to test AI models deployed in O-RAN in terms of their decision-making performance, vulnerability and security.
A Practical AoI Scheduler in IoT Networks with Relays
The proposed v-PPO based AoI scheduler adapts well to changing network conditions and accounts for unknown traffic generation patterns, making it practical for real-world IoT deployments.
AoI-minimizing Scheduling in UAV-relayed IoT Networks
Our simulation results show that DQN-based scheduler outperforms MAF-MAD scheduler and three other baseline schedulers, i. e., Maximal AoI First (MAF), Round Robin (RR) and Random, employed at both hops under general conditions when the network is small (with 10's of IoT devices).
RAN Slicing in Multi-MVNO Environment under Dynamic Channel Conditions
In this work, we study the problem of Modulation and Coding Scheme(MCS) aware RAN slicing(MaRS) in the context of a wireless system having MVNOs which have users with minimum data rate requirement.
Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mmWave Networks
In LoS conditions, the selection of the beams is consequential and improves the accuracy by up to 70%.
Cross-layer Band Selection and Routing Design for Diverse Band-aware DSA Networks
As several new spectrum bands are opening up for shared use, a new paradigm of \textit{Diverse Band-aware Dynamic Spectrum Access} (d-DSA) has emerged.
Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networks
This paper presents DeepIA, a deep learning solution for faster and more accurate initial access (IA) in 5G millimeter wave (mmWave) networks when compared to conventional IA.
