Low-Latency Federated Reinforcement Learning-Based Resource Allocation in Converged
Access Networks Page 1 W2A.28.pdf OFC 2020 © OSA 2020 Low-Latency Federated …

Reinforcement learning (RL) has recently emerged as a promising solution for intelligence
bandwidth decisions that reduce latency in optical access networks. Even though RL drives …

Emerging applications such as remotely-controlled human-to-machine and tactile-haptic
applications in the Internet evolution demand stringent low-latency transmission. In realising …

6G networks will deliver dynamic and immersive applications that bridge the real and digital
worlds. The next-generation passive optical access network is a potential optical transport …

We propose a reinforcement-learning-based network design and control algorithm that
introduces reward variation dependent on maximum link utilization and link-adjacency …

In this work, we present a complete rethink of the decision-making process in allocating
bandwidth in a heterogeneous Fiber-Wireless network with machine intelligence. We …

Optical access networks are envisioned to become increasingly complex as they support
more and more diverse and immersive services, each with a different capacity, latency, and …

This paper presents the exploitation of an artificial neural network (ANN) to facilitate insights
into existing bandwidth allocation schemes in optical access networks and supervise …

The enormous success of wireless technology has recently led to an explosive demand for,
and hence a shortage of, bandwidth resources. This expected shortage problem is reported …

We propose a novel application of reinforcement learning (RL) with invalid action masking
and a novel training methodology for routing and wavelength assignment (RWA) in fixed …