Deep learning for intelligent wireless networks: A comprehensive survey
As a promising machine learning tool to handle the accurate pattern recognition from
complex raw data, deep learning (DL) is becoming a powerful method to add intelligence to …
complex raw data, deep learning (DL) is becoming a powerful method to add intelligence to …
Distributed multi-agent deep Q-learning for load balancing user association in dense networks
Distributed learning can lead to effective user association with low overhead, but faces
significant challenges in incorporating load balancing at all base stations (BS) because of …
significant challenges in incorporating load balancing at all base stations (BS) because of …
Deep-q reinforcement learning based resource allocation in wireless communication networks
V Aruna, L Anjaneyulu, C Bhar - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
Wireless communication networks of the future generations are expected to be inherently
complex owing to the network architecture they can incorporate. With such networks …
complex owing to the network architecture they can incorporate. With such networks …
[HTML][HTML] Q-learning-enabled channel access in next-generation dense wireless networks for IoT-based eHealth systems
One of the key applications for the Internet of Things (IoT) is the eHealth service that targets
sustaining patient health information in digital environments, such as the Internet cloud with …
sustaining patient health information in digital environments, such as the Internet cloud with …
Convolutional neural network-based deep Q-network (CNN-DQN) resource management in cloud radio access network
The recent surge of mobile subscribers and user data traffic has accelerated the
telecommunication sector towards the adoption of the fifth-generation (5G) mobile networks …
telecommunication sector towards the adoption of the fifth-generation (5G) mobile networks …
Deep Reinforcement Learning for Dynamic Radio Access Selection over Future Wireless Networks
CC González, EF Pupo… - 2022 IEEE …, 2022 - ieeexplore.ieee.org
Despite the fifth-generation (5G) of mobile communication systems being at its initial stage,
the research community has started to focus on its successor. The sixth-generation (6G) is …
the research community has started to focus on its successor. The sixth-generation (6G) is …
AIF: An artificial intelligence framework for smart wireless network management
To solve the policy optimizing problem in many scenarios of smart wireless network
management using a single universal algorithm, this letter proposes a universal learning …
management using a single universal algorithm, this letter proposes a universal learning …
Applications of Deep Reinforcement Learning in Wireless Networks-A Recent Review
Deep Reinforcement Learning (DRL) techniques have gained substantial attention in recent
years for future wireless networks. They can overcome the ever-increasing challenges of …
years for future wireless networks. They can overcome the ever-increasing challenges of …
Request delay and survivability optimization for software defined‐wide area networking (SD‐WAN) using multi‐agent deep reinforcement learning
Data exchange between headquarters and local branches represents a major challenge
issue for business success. For this issue, traditional solutions applied to wide area …
issue for business success. For this issue, traditional solutions applied to wide area …
An overview of intelligent wireless communications using deep reinforcement learning
Future wireless communication networks tend to be intelligentized to accomplish the
missions that cannot be preprogrammed. In the new intelligent communication systems …
missions that cannot be preprogrammed. In the new intelligent communication systems …