Search Results for author:
Found 11 papers, 2 papers with code
Device-Free Human State Estimation using UWB Multi-Static Radios
We make use of the channel impulse response (CIR) measurements from the UWB sensors to estimate the human state - comprised of location and activity - in a given area.
SAGE: Smart home Agent with Grounded Execution
The common sense reasoning abilities and vast general knowledge of Large Language Models (LLMs) make them a natural fit for interpreting user requests in a Smart Home assistant context.
Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approach
In RIS-aided systems, channel estimation involves estimating two channels for the user-RIS (UE-RIS) and RIS-base station (RIS-BS) links.
CeBed: A Benchmark for Deep Data-Driven OFDM Channel Estimation
This work offers a comprehensive and unified framework to help researchers evaluate and design data-driven channel estimation algorithms.
Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues
Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generations wireless systems.
Dynamic Noises of Multi-Agent Environments Can Improve Generalization: Agent-based Models meets Reinforcement Learning
We study the benefits of reinforcement learning (RL) environments based on agent-based models (ABM).
On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems
We consider an Intelligent Reflecting Surface (IRS)-aided multiple-input single-output (MISO) system for downlink transmission.
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty
The stability and the robustness of deep MARL to practical challenges is still an open research problem.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial
In this context, this tutorial focuses on the role of DRL with an emphasis on deep Multi-Agent Reinforcement Learning (MARL) for AI-enabled 6G networks.
Hacking Google reCAPTCHA v3 using Reinforcement Learning
We present a Reinforcement Learning (RL) methodology to bypass Google reCAPTCHA v3.
DVOLVER: Efficient Pareto-Optimal Neural Network Architecture Search
Amongst these models one architecture has the same Top-1 accuracy on ImageNet as NASNet-A mobile with 8% less floating point operations and another one has a Top-1 accuracy of 75. 28% on ImageNet exceeding by 0. 28% the best MobileNetV2 model for the same computational resources.
