Comprehensive survey on machine learning in vehicular network: Technology, applications and challenges
Towards future intelligent vehicular network, the machine learning as the promising artificial
intelligence tool is widely researched to intelligentize communication and networking …
intelligence tool is widely researched to intelligentize communication and networking …
The frontiers of deep reinforcement learning for resource management in future wireless HetNets: Techniques, challenges, and research directions
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
A walk in the park: Learning to walk in 20 minutes with model-free reinforcement learning
Deep reinforcement learning is a promising approach to learning policies in uncontrolled
environments that do not require domain knowledge. Unfortunately, due to sample …
environments that do not require domain knowledge. Unfortunately, due to sample …
Computation offloading and resource allocation in MEC-enabled integrated aerial-terrestrial vehicular networks: A reinforcement learning approach
As important services of the future sixth-generation (6G) wireless networks, vehicular
communication and mobile edge computing (MEC) have received considerable interest in …
communication and mobile edge computing (MEC) have received considerable interest in …
Learning and adapting agile locomotion skills by transferring experience
Legged robots have enormous potential in their range of capabilities, from navigating
unstructured terrains to high-speed running. However, designing robust controllers for highly …
unstructured terrains to high-speed running. However, designing robust controllers for highly …
Decentralized power allocation for MIMO-NOMA vehicular edge computing based on deep reinforcement learning
Vehicular edge computing (VEC) is envisioned as a promising approach to process the
explosive computation tasks of vehicular user (VU). In the VEC system, each VU allocates …
explosive computation tasks of vehicular user (VU). In the VEC system, each VU allocates …
Qoe-based task offloading with deep reinforcement learning in edge-enabled internet of vehicles
In the transportation industry, task offloading services of edge-enabled Internet of Vehicles
(IoV) are expected to provide vehicles with the better Quality of Experience (QoE). However …
(IoV) are expected to provide vehicles with the better Quality of Experience (QoE). However …
[HTML][HTML] AI augmented Edge and Fog computing: Trends and challenges
In recent years, the landscape of computing paradigms has witnessed a gradual yet
remarkable shift from monolithic computing to distributed and decentralized paradigms such …
remarkable shift from monolithic computing to distributed and decentralized paradigms such …
Deep learning-based flexible joint channel estimation and signal detection of multi-user OFDM-NOMA
This paper proposes a deep learning (DL)-based joint channel estimation and signal
detection in multi-user orthogonal-frequency division multiplexing-non-orthogonal multiple …
detection in multi-user orthogonal-frequency division multiplexing-non-orthogonal multiple …
A power allocation scheme for MIMO-NOMA and D2D vehicular edge computing based on decentralized DRL
In vehicular edge computing (VEC), some tasks can be processed either locally or on the
mobile edge computing (MEC) server at a base station (BS) or a nearby vehicle. In fact …
mobile edge computing (MEC) server at a base station (BS) or a nearby vehicle. In fact …