Multiobjective load balancing for multiband downlink cellular networks: A meta-reinforcement learning approach
Load balancing has become a key technique to handle the increasing traffic demand and
improve the user experience. It evenly distributes the traffic across network resources by …
improve the user experience. It evenly distributes the traffic across network resources by …
Reinforcement learning for communication load balancing: approaches and challenges
The amount of cellular communication network traffic has increased dramatically in recent
years, and this increase has led to a demand for enhanced network performance …
years, and this increase has led to a demand for enhanced network performance …
Intent-based cognitive closed-loop management with built-in conflict handling
AC Baktir, ADN Junior, A Zahemszky… - 2022 IEEE 8th …, 2022 - ieeexplore.ieee.org
The ever-growing complexity in networks and the various future use cases with diverse, and
often stringent performance requirements call for a higher level of automation. A tool to …
often stringent performance requirements call for a higher level of automation. A tool to …
One for all: Traffic prediction at heterogeneous 5g edge with data-efficient transfer learning
By placing the computing, storage and networking resources close to the end users,
distributed edge computing greatly benefits the performance of 5G communication systems …
distributed edge computing greatly benefits the performance of 5G communication systems …
Traffic scenario clustering and load balancing with distilled reinforcement learning policies
Due to the rapid increase in wireless communication traffic in recent years, load balancing is
becoming increasingly important for ensuring the quality of service. However, variations in …
becoming increasingly important for ensuring the quality of service. However, variations in …
Platform for energy efficiency monitoring electrical vehicle in real world traffic simulation
The increasing popularity and attention in VANETs has prompted researchers to develop
accurate and realistic simulation tools. The realistic simulation for VANETs is a challenging …
accurate and realistic simulation tools. The realistic simulation for VANETs is a challenging …
Adaptive generalized proportional fair scheduling with deep reinforcement learning
J Song, Y Nam, H Kwon, I Sim… - 2022 IEEE Globecom …, 2022 - ieeexplore.ieee.org
The emergence of 5G and the upcoming 6G has been and will be supporting numerous
applications with various quality of service (QoS) requirements which inevitably come with …
applications with various quality of service (QoS) requirements which inevitably come with …
Probabilistic Mobility Load Balancing for Multi-Band 5G and Beyond Networks
The ever-increasing demand for data services and the proliferation of user equipment (UE)
have resulted in a significant rise in the volume of mobile traffic. Moreover, in multi-band …
have resulted in a significant rise in the volume of mobile traffic. Moreover, in multi-band …
A generalized load balancing policy with multi-teacher reinforcement learning
Although reinforcement learning (RL) shows advantages in cellular network load balancing,
it suffers from a low generalization ability, preventing it from real-world applications …
it suffers from a low generalization ability, preventing it from real-world applications …
Policy reuse for communication load balancing in unseen traffic scenarios
With the continuous growth in communication network complexity and traffic volume,
communication load balancing solutions are receiving increasing attention. Specifically …
communication load balancing solutions are receiving increasing attention. Specifically …