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AI/ML-aided capacity maximization strategies for URLLC in 5G/6G wireless systems: A survey
Ultra-reliable low-latency communication (URLLC) refers to cellular applications in fifth and
sixth-generation (5G/6G) networks with specific latency, reliability, and availability demands …
sixth-generation (5G/6G) networks with specific latency, reliability, and availability demands …
A Parallel Zeroth-Order Framework for Efficient Cellular Network Optimization
Network optimization plays a crucial role in wireless communications. However, the
optimization of contemporary 5G networks is challenging due to its black-box nature and …
optimization of contemporary 5G networks is challenging due to its black-box nature and …
[HTML][HTML] BEERL: Both ends explanations for reinforcement learning
Deep Reinforcement Learning (RL) is a black-box method and is hard to understand
because the agent employs a neural network (NN). To explain the behavior and decisions …
because the agent employs a neural network (NN). To explain the behavior and decisions …
Multi-agent reinforcement learning with graph q-networks for antenna tuning
Future generations of mobile networks are expected to contain more and more antennas
with growing complexity and more parameters. Optimizing these parameters is necessary for …
with growing complexity and more parameters. Optimizing these parameters is necessary for …
Off-policy learning in contextual bandits for remote electrical tilt optimization
We investigate the problem of Remote Electrical Tilt (RET) optimization using off-policy
learning techniques devised for Contextual Bandits (CBs). The goal in RET optimization is to …
learning techniques devised for Contextual Bandits (CBs). The goal in RET optimization is to …
[PDF][PDF] Towards cooperative marl in industrial domains
JD Thomas - 2023 - research-information.bris.ac.uk
This thesis investigates the application of Deep Multi-Agent Reinforcement Learning
(DMARL) to problems within telecommunications and logistics. These sectors are exemplary …
(DMARL) to problems within telecommunications and logistics. These sectors are exemplary …
Bandit Methods for Network Optimization: Safety, Exploration, and Coordination
F Vannella - 2023 - diva-portal.org
The increasing complexity of modern mobile networks poses unprecedented challenges to
their optimization. Mobile Network Operators (MNOs) need to control a large number of …
their optimization. Mobile Network Operators (MNOs) need to control a large number of …
Network Parameter Control in Cellular Networks through Graph-Based Multi-Agent Constrained Reinforcement Learning
Cellular networks are growing in complexity at increasing speed and the geographical
locations in which they are deployed in are getting denser. Traditional control methods fall …
locations in which they are deployed in are getting denser. Traditional control methods fall …
Optimizing Wireless Coverage and Capacity with PPO-Based Adaptive Antenna Configuration
Optimizing antenna parameters like azimuth, down-tilt, and power is crucial for coverage
and capacity optimization (CCO) in next-generation wireless networks. However, traditional …
and capacity optimization (CCO) in next-generation wireless networks. However, traditional …
Learning Cellular Coverage from Real Network Configurations using GNNs
Cellular coverage quality estimation has been a critical task for self-organized networks. In
real-world scenarios, deep-learning-powered coverage quality estimation methods cannot …
real-world scenarios, deep-learning-powered coverage quality estimation methods cannot …