Single and multi-agent deep reinforcement learning for AI-enabled wireless networks: A tutorial
Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have
led to multiple successes in solving sequential decision-making problems in various …
led to multiple successes in solving sequential decision-making problems in various …
Deep reinforcement learning: Algorithm, applications, and ultra-low-power implementation
In order to overcome the limitation of traditional reinforcement learning techniques on the
restricted dimensionality of state and action spaces, the recent breakthroughs of deep …
restricted dimensionality of state and action spaces, the recent breakthroughs of deep …
From design to deployment of zero touch deep reinforcement learning WLANs
O Iacoboaiea, J Krolikowski, ZB Houidi… - IEEE Communications …, 2022 - ieeexplore.ieee.org
Machine learning is increasingly used to automate networking tasks, in a paradigm known
as zero touch network and service management (ZSM). In particular, deep reinforcement …
as zero touch network and service management (ZSM). In particular, deep reinforcement …
Cooperative multi-agent reinforcement learning for low-level wireless communication
Traditional radio systems are strictly co-designed on the lower levels of the OSI stack for
compatibility and efficiency. Although this has enabled the success of radio communications …
compatibility and efficiency. Although this has enabled the success of radio communications …
Stateless reinforcement learning for multi-agent systems: The case of spectrum allocation in dynamic channel bonding WLANs
S Barrachina-Muñoz, A Chiumento… - 2021 Wireless Days …, 2021 - ieeexplore.ieee.org
Spectrum allocation in the form of primary channel and bandwidth selection is a key factor
for dynamic channel bonding (DCB) wireless local area networks (WLANs). To cope with …
for dynamic channel bonding (DCB) wireless local area networks (WLANs). To cope with …
Big data goes small: Real-time spectrum-driven embedded wireless networking through deep learning in the RF loop
F Restuccia, T Melodia - IEEE INFOCOM 2019-IEEE …, 2019 - ieeexplore.ieee.org
The explosion of 5G networks and the Internet of Things will result in an exceptionally
crowded RF environment, where techniques such as spectrum sharing and dynamic …
crowded RF environment, where techniques such as spectrum sharing and dynamic …
A federated reinforcement learning framework for incumbent technologies in beyond 5G networks
Incumbent wireless technologies for futuristic fifth generation (5G) and beyond 5G (B5G)
networks, such as IEEE 802.11 ax (WiFi), are vital to provide ubiquitous ultra-reliable and …
networks, such as IEEE 802.11 ax (WiFi), are vital to provide ubiquitous ultra-reliable and …
Regularized Anderson acceleration for off-policy deep reinforcement learning
W Shi, S Song, H Wu, YC Hsu… - Advances in Neural …, 2019 - proceedings.neurips.cc
Abstract Model-free deep reinforcement learning (RL) algorithms have been widely used for
a range of complex control tasks. However, slow convergence and sample inefficiency …
a range of complex control tasks. However, slow convergence and sample inefficiency …
Tinyrl: Towards reinforcement learning on tiny embedded devices
We observe significant interest in reinforcement learning methods for real-world sensing-
control scenarios driven by the sensor data streams. However, the delay introduced to the …
control scenarios driven by the sensor data streams. However, the delay introduced to the …