Machine learning for resource management in cellular and IoT networks: Potentials, current solutions, and open challenges
Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart
devices connected to the Internet. In the wake of disruptive IoT with a huge amount and …
devices connected to the Internet. In the wake of disruptive IoT with a huge amount and …
20 years of evolution from cognitive to intelligent communications
It has been 20 years since the concept of cognitive radio (CR) was proposed, which is an
efficient approach to provide more access opportunities to connect massive wireless …
efficient approach to provide more access opportunities to connect massive wireless …
Vision, requirements, and technology trend of 6G: How to tackle the challenges of system coverage, capacity, user data-rate and movement speed
Since 5G new radio comes with non-standalone (NSA) and standalone (SA) versions in
3GPP, research on 6G has been on schedule by academics and industries. Though 6G is …
3GPP, research on 6G has been on schedule by academics and industries. Though 6G is …
Deep-reinforcement learning multiple access for heterogeneous wireless networks
This paper investigates a deep reinforcement learning (DRL)-based MAC protocol for
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …
Simultaneous navigation and radio mapping for cellular-connected UAV with deep reinforcement learning
Cellular-connected unmanned aerial vehicle (UAV) is a promising technology to unlock the
full potential of UAVs in the future by reusing the cellular base stations (BSs) to enable their …
full potential of UAVs in the future by reusing the cellular base stations (BSs) to enable their …
Deep reinforcement learning for joint channel selection and power control in D2D networks
Device-to-device (D2D) technology, which allows direct communications between proximal
devices, is widely acknowledged as a promising candidate to alleviate the mobile traffic …
devices, is widely acknowledged as a promising candidate to alleviate the mobile traffic …
Deep reinforcement learning-based spectrum allocation in integrated access and backhaul networks
We develop a framework based on deep reinforcement learning (DRL) to solve the spectrum
allocation problem in the emerging integrated access and backhaul (IAB) architecture with …
allocation problem in the emerging integrated access and backhaul (IAB) architecture with …
Device association for RAN slicing based on hybrid federated deep reinforcement learning
Network slicing (NS) has been widely identified as a key architectural technology for 5G-and-
beyond systems by supporting divergent requirements in a sustainable way. In radio access …
beyond systems by supporting divergent requirements in a sustainable way. In radio access …
Intelligent user association for symbiotic radio networks using deep reinforcement learning
In this paper, we are interested in symbiotic radio networks (SRNs), in which an Internet-of-
Things (IoT) network parasitizes in a primary cellular network to achieve spectrum-, energy …
Things (IoT) network parasitizes in a primary cellular network to achieve spectrum-, energy …
Deep reinforcement learning for multi-user access control in non-terrestrial networks
Non-Terrestrial Networks (NTNs) composed of space-borne (eg, satellites) and airborne
vehicles (eg, drones and blimps) have recently been proposed by 3GPP as a new paradigm …
vehicles (eg, drones and blimps) have recently been proposed by 3GPP as a new paradigm …