A tutorial on ultrareliable and low-latency communications in 6G: Integrating domain knowledge into deep learning
As one of the key communication scenarios in the fifth-generation and also the sixth-
generation (6G) mobile communication networks, ultrareliable and low-latency …
generation (6G) mobile communication networks, ultrareliable and low-latency …
Survey on machine learning for intelligent end-to-end communication toward 6G: From network access, routing to traffic control and streaming adaption
The end-to-end quality of service (QoS) and quality of experience (QoE) guarantee is quite
important for network optimization. The current 5G and conceived 6G network in the future …
important for network optimization. The current 5G and conceived 6G network in the future …
Classic meets modern: A pragmatic learning-based congestion control for the internet
These days, taking the revolutionary approach of using clean-slate learning-based designs
to completely replace the classic congestion control schemes for the Internet is gaining …
to completely replace the classic congestion control schemes for the Internet is gaining …
Do switches dream of machine learning? toward in-network classification
Z Xiong, N Zilberman - Proceedings of the 18th ACM workshop on hot …, 2019 - dl.acm.org
Machine learning is currently driving a technological and societal revolution. While
programmable switches have been proven to be useful for in-network computing, machine …
programmable switches have been proven to be useful for in-network computing, machine …
Network planning with deep reinforcement learning
Network planning is critical to the performance, reliability and cost of web services. This
problem is typically formulated as an Integer Linear Programming (ILP) problem. Today's …
problem is typically formulated as an Integer Linear Programming (ILP) problem. Today's …
Leveraging deep reinforcement learning for traffic engineering: A survey
After decades of unprecedented development, modern networks have evolved far beyond
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
DeepOPF: A feasibility-optimized deep neural network approach for AC optimal power flow problems
To cope with increasing uncertainty from renewable generation and flexible load, grid
operators need to solve alternative current optimal power flow (AC-OPF) problems more …
operators need to solve alternative current optimal power flow (AC-OPF) problems more …
Comprehensive review on congestion detection, alleviation, and control for IoT networks
Abstract Context: The Internet of Things (IoT) comprises various computing devices that
operate on a non-standard platform and can connect to wireless networks to transmit data …
operate on a non-standard platform and can connect to wireless networks to transmit data …
Deep reinforcement learning verification: a survey
Deep reinforcement learning (DRL) has proven capable of superhuman performance on
many complex tasks. To achieve this success, DRL algorithms train a decision-making agent …
many complex tasks. To achieve this success, DRL algorithms train a decision-making agent …
ACC: Automatic ECN tuning for high-speed datacenter networks
For the widely deployed ECN-based congestion control schemes, the marking threshold is
the key to deliver high bandwidth and low latency. However, due to traffic dynamics in the …
the key to deliver high bandwidth and low latency. However, due to traffic dynamics in the …