A study on deep learning for latency constraint applications in beyond 5G wireless systems
The fifth generation (5G) of wireless communications has led to many advancements in
technologies such as large and distributed antenna arrays, ultra-dense networks, software …
technologies such as large and distributed antenna arrays, ultra-dense networks, software …
Exploiting future radio resources with end-to-end prediction by deep learning
J Guo, C Yang, I Chih-Lin - IEEE Access, 2018 - ieeexplore.ieee.org
Machine learning is a powerful tool to predict user behavior and harness the vast amount of
data measured in cellular networks. Predictive resource allocation is a promising approach …
data measured in cellular networks. Predictive resource allocation is a promising approach …
Learning to continuously optimize wireless resource in a dynamic environment: A bilevel optimization perspective
There has been a growing interest in developing data-driven, and in particular deep neural
network (DNN) based methods for modern communication tasks. These methods achieve …
network (DNN) based methods for modern communication tasks. These methods achieve …
Unsupervised deep learning for optimizing wireless systems with instantaneous and statistic constraints
Deep neural networks (DNNs) have been introduced for designing wireless policies by
approximating the mappings from environmental parameters to solutions of optimization …
approximating the mappings from environmental parameters to solutions of optimization …
Resource management in wireless networks via multi-agent deep reinforcement learning
We propose a mechanism for distributed resource management and interference mitigation
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …
Intelligent Resource Allocation for Grant-Free Access: A Reinforcement Learning Approach
M Elsayem, H Abou-Zeid, A Afana… - IEEE Networking …, 2023 - ieeexplore.ieee.org
Future wireless networks will support applications demanding high data-rates, ultra-low
latency, and high reliabilities. One technology for such ultra-reliable low latency …
latency, and high reliabilities. One technology for such ultra-reliable low latency …
Meta-gating framework for fast and continuous resource optimization in dynamic wireless environments
With the great success of deep learning (DL) in image classification, speech recognition,
and other fields, more and more studies have applied various neural networks (NNs) to …
and other fields, more and more studies have applied various neural networks (NNs) to …
Thirty years of machine learning: The road to Pareto-optimal wireless networks
Future wireless networks have a substantial potential in terms of supporting a broad range of
complex compelling applications both in military and civilian fields, where the users are able …
complex compelling applications both in military and civilian fields, where the users are able …
Deep learning for radio resource allocation with diverse quality-of-service requirements in 5G
To accommodate diverse Quality-of-Service (QoS) requirements in 5th generation cellular
networks, base stations need real-time optimization of radio resources in time-varying …
networks, base stations need real-time optimization of radio resources in time-varying …
Experienced deep reinforcement learning with generative adversarial networks (GANs) for model-free ultra reliable low latency communication
In this paper, a novel experienced deep reinforcement learning (deep-RL) framework is
proposed to provide model-free resource allocation for ultra reliable low latency …
proposed to provide model-free resource allocation for ultra reliable low latency …