A Federated Deep Reinforcement Learning-Based Trust Model in Underwater Acoustic Sensor Networks
Underwater acoustic sensor networks (UASNs) have been widely deployed in many areas, such as marine ranching, naval applications, and marine disaster warning […]
Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models
The age of information (AoI) is used to measure the freshness of the data. In IoT networks, the traditional resource management schemes […]
Joint Content Update and Transmission Resource Allocation for Energy-Efficient Edge Caching of High Definition Map
Caching the high definition map (HDM) on the edge network can significantly alleviate energy consumption of the roadside sensors frequently conducting the […]
6G Fresnel Spot Beamfocusing using Large-Scale Metasurfaces: A Distributed DRL-Based Approach
We propose a novel approach to smart spot-beamforming (SBF) in the Fresnel zone leveraging extremely large-scale programmable metasurfaces (ELPMs). A smart SBF […]
Federated Deep Reinforcement Learning for Prediction-Based Network Slice Mobility in 6 G Mobile Networks
Network slices are generally coupled with services and face service continuity/unavailability concerns due to the high mobility and dynamic requests from users. […]
Multi-UAV Path Learning for Age and Power Optimization in IoT with UAV Battery Recharge
In many emerging Internet of Things (IoT) applications, the freshness of the is an important design criterion. Age of Information (AoI) quantifies […]
ACK-Less Rate Adaptation Using Distributional Reinforcement Learning for Reliable IEEE 802.11bc Broadcast WLANs
As a step towards establishing reliable broadcast wireless local area networks (WLANs) this paper proposes acknowledgement (ACK)-less rate adaptation to alleviate reception […]
Evolution Toward 6G Multi-band Wireless Networks
In this article we first present the vision key performance indicators key enabling techniques (KETs) and services of 6G wireless networks. Then […]
Joint Caching and Computing Service Placement for Edge-Enabled IoT based on Deep Reinforcement Learning
By placing edge service functions in proximity to IoT facilities edge computing can satisfy various IoT applications’ resource and latency requirements. Sensing-data-driven […]
Dynamic Task Allocation and Service Migration in Edge-Cloud IoT System based on Deep Reinforcement Learning
Edge computing extends the ability of cloud computing to the network edge to support diverse resource-sensitive and performance-sensitive IoT applications. However due […]