Search Results for author:
Found 23 papers, 2 papers with code
AGO: Adaptive Grounding for Open World 3D Occupancy Prediction
Open-world 3D semantic occupancy prediction aims to generate a voxelized 3D representation from sensor inputs while recognizing both known and unknown objects.
TQD-Track: Temporal Query Denoising for 3D Multi-Object Tracking
For these paradigms, we further design an association mask in the association module to ensure the consistent interaction between track and detection queries as during inference.
Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations
This study presents an empirical investigation into the energy consumption of Discriminative and Generative AI models within real-world MLOps pipelines.
Task-Oriented Connectivity for Networked Robotics with Generative AI and Semantic Communications
The convergence of robotics, advanced communication networks, and artificial intelligence (AI) holds the promise of transforming industries through fully automated and intelligent operations.
Building the Self-Improvement Loop: Error Detection and Correction in Goal-Oriented Semantic Communications
Error detection and correction are essential for ensuring robust and reliable operation in modern communication systems, particularly in complex transmission environments.
From Hype to Reality: The Road Ahead of Deploying DRL in 6G Networks
The industrial landscape is rapidly evolving with the advent of 6G applications, which demand massive connectivity, high computational capacity, and ultra-low latency.
Adapting MLOps for Diverse In-Network Intelligence in 6G Era: Challenges and Solutions
ML operations (MLOps) offer a systematic approach to tackle these challenges.
SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
Scene flow estimation predicts the 3D motion at each point in successive LiDAR scans.
Ranked #1 on
Self-supervised Scene Flow Estimation
on Argoverse 2
Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach
Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents.
Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed
UMBRELLA is a large-scale, open-access Internet of Things (IoT) ecosystem incorporating over 200 multi-sensor multi-wireless nodes, 20 collaborative robots, and edge-intelligence-enabled devices.
Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems
We evaluate our approach within an AFL deployment consisting of 10 simulated clients with heterogeneous compute constraints and non-IID data.
DRL-Based Sidelobe Suppression for Multi-focus Reconfigurable Intelligent Surface
Reconfigurable intelligent surface (RIS) technology is receiving significant attention as a key enabling technology for 6G communications, with much attention given to coverage infill and wireless power transfer.
A DRL-based Reflection Enhancement Method for RIS-assisted Multi-receiver Communications
In reconfigurable intelligent surface (RIS)-assisted wireless communication systems, the pointing accuracy and intensity of reflections depend crucially on the 'profile,' representing the amplitude/phase state information of all elements in a RIS array.
Demo: A Digital Twin of the 5G Radio Access Network for Anomaly Detection Functionality
Recently, the concept of digital twins (DTs) has received significant attention within the realm of 5G/6G.
PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View
Accurately perceiving instances and predicting their future motion are key tasks for autonomous vehicles, enabling them to navigate safely in complex urban traffic.
Federated Meta-Learning for Traffic Steering in O-RAN
In order to provide such services, 5G systems will support various combinations of access technologies such as LTE, NR, NR-U and Wi-Fi.
Transmit Power Control for Indoor Small Cells: A Method Based on Federated Reinforcement Learning
The model will also be used as the base model for adaptive training in the new environment.
Variational Autoencoder Assisted Neural Network Likelihood RSRP Prediction Model
Measuring customer experience on mobile data is of utmost importance for global mobile operators.
Sim2real for Reinforcement Learning Driven Next Generation Networks
One of the main reasons is the modelling gap between the simulation and the real environment, which could make the RL agent trained by simulation ill-equipped for the real environment.
Bayesian Optimisation-Assisted Neural Network Training Technique for Radio Localisation
Radio signal-based (indoor) localisation technique is important for IoT applications such as smart factory and warehouse.
RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN
We provide a taxonomy for the challenges faced by ML/RL models throughout the development life-cycle: from the system specification to production deployment (data acquisition, model design, testing and management, etc.).
Deep Transfer Learning for WiFi Localization
Finally, an ablation study of the training dataset shows that, in both office and sport hall scenarios, after reusing the feature extraction layers of the base model, only 55% of the training data is required to obtain the models' accuracy similar to the base models.
Wireless Localisation in WiFi using Novel Deep Architectures
Meanwhile, using a well-organised architecture, the neural network models can be trained directly with raw data from the CSI and localisation features can be automatically extracted to achieve accurate position estimates.
