Joint trajectory and resource optimization of MEC-assisted UAVs in sub-THz networks: A resources-based multi-agent proximal policy optimization DRL with attention …
The use of Terahertz (THz) technology in sixth-generation (6 G) networks will bring high-
speed and capacity data services. But limitations like molecular absorption, rain attenuation …
speed and capacity data services. But limitations like molecular absorption, rain attenuation …
Emerging technologies for 6G communication networks: Machine learning approaches
The fifth generation achieved tremendous success, which brings high hopes for the next
generation, as evidenced by the sixth generation (6G) key performance indicators, which …
generation, as evidenced by the sixth generation (6G) key performance indicators, which …
[PDF][PDF] Special topic on computational radio intelligence: One key for 6G wireless
W JIANG, FL LUO - ZTE Communications, 2020 - zte.magtechjournal.com
The year of 2019 is the first deployment year of the fifth generation (5G) mobile
communications. As we are writing the editorial for this special issue, a list of coun⁃ tries …
communications. As we are writing the editorial for this special issue, a list of coun⁃ tries …
Learning to solve optimization problems with hard linear constraints
Constrained optimization problems have appeared in a wide variety of challenging real-
world problems, where constraints often capture the physics of the underlying system …
world problems, where constraints often capture the physics of the underlying system …
Deep reinforcement learning autoencoder with noisy feedback
End-to-end learning of communication systems enables joint optimization of transmitter and
receiver, implemented as deep neural network (NN)-based autoencoders, over any type of …
receiver, implemented as deep neural network (NN)-based autoencoders, over any type of …
Two applications of deep learning in the physical layer of communication systems
E Björnson, P Giselsson - arXiv preprint arXiv:2001.03350, 2020 - arxiv.org
Deep learning has proved itself to be a powerful tool to develop data-driven signal
processing algorithms for challenging engineering problems. By learning the key features …
processing algorithms for challenging engineering problems. By learning the key features …
A survey of online data-driven proactive 5G network optimisation using machine learning
In the fifth-generation (5G) mobile networks, proactive network optimisation plays an
important role in meeting the exponential traffic growth, more stringent service requirements …
important role in meeting the exponential traffic growth, more stringent service requirements …
Learning to continuously optimize wireless resource in a dynamic environment: A bilevel optimization perspective
There has been a growing interest in developing data-driven, and in particular deep neural
network (DNN) based methods for modern communication tasks. These methods achieve …
network (DNN) based methods for modern communication tasks. These methods achieve …
Optimized power control design for over-the-air federated edge learning
Over-the-air federated edge learning (Air-FEEL) has emerged as a communication-efficient
solution to enable distributed machine learning over edge devices by using their data locally …
solution to enable distributed machine learning over edge devices by using their data locally …
Quantum-inspired real-time optimization for 6G networks: Opportunities, challenges, and the road ahead
It is envisioned that 6G, unlike its predecessor 5G, will depart from connected machines and
connected people to connected intelligence. The main goal of 6G networks is to support …
connected people to connected intelligence. The main goal of 6G networks is to support …