HAP-assisted RSMA-enabled vehicular edge computing: A DRL-based optimization framework
In recent years, the demand for vehicular edge computing (VEC) has grown rapidly due to
the increasing need for low-latency and high-throughput applications such as autonomous …
the increasing need for low-latency and high-throughput applications such as autonomous …
Empowering smart cities: High-altitude platforms based Mobile Edge Computing and Wireless Power Transfer for efficient IoT data processing
This work presents an efficient framework that combines High Altitude Platform (HAP)-based
Mobile Edge Computing (MEC) networks with Wireless Power Transfer (WPT) to optimize …
Mobile Edge Computing (MEC) networks with Wireless Power Transfer (WPT) to optimize …
Rate-splitting multiple access aided mobile edge computing with randomly deployed users
In this paper, a rate-splitting multiple access (RSMA) scheme is proposed to aid a mobile
edge computing (MEC) system where multiple randomly deployed users offload their …
edge computing (MEC) system where multiple randomly deployed users offload their …
Deep reinforcement learning-based partial task offloading in high altitude platform-aided vehicular networks
Compared with traditional terrestrial access networks, a mobile edge computing-enabled
aerial access network is a potential paradigm for performing complicated computations by …
aerial access network is a potential paradigm for performing complicated computations by …
A survey on deep reinforcement learning-driven task offloading in aerial access networks
Internet of Things computation offloading is a challenging problem, particularly in distant
places where mobile edge computing (MEC) or cloud infrastructure is absent. Fortunately …
places where mobile edge computing (MEC) or cloud infrastructure is absent. Fortunately …
A review on AI-driven aerial access networks: Challenges and open research issues
Aerial access networks (AANs) consisting of low altitude platforms (LAPs) and high altitude
platforms (HAPs) have been considered as emerging wireless networking technologies to …
platforms (HAPs) have been considered as emerging wireless networking technologies to …
Recent studies on deep reinforcement learning in RIS-UAV communication networks
Unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) technologies
have recently been identified as enablers for future wireless networks. Deep reinforcement …
have recently been identified as enablers for future wireless networks. Deep reinforcement …
Rate-splitting and common message decoding in hybrid cloud/mobile edge computing networks
This paper proposes, and evaluates the benefits of, a hybrid central cloud (CC) and mobile
edge computing (MEC) platform, especially introduced to balance the network resources for …
edge computing (MEC) platform, especially introduced to balance the network resources for …
Energy-efficient rate-splitting multiple access: A deep reinforcement learning-based framework
M Diamanti, G Kapsalis, EE Tsiropoulou… - IEEE Open Journal …, 2023 - ieeexplore.ieee.org
Rate-Splitting Multiple Access (RSMA) has been recognized as an effective technique to
reconcile the tradeoff between decoding interference and treating interference as noise in …
reconcile the tradeoff between decoding interference and treating interference as noise in …
Intelligent QoE Management for IoMT Streaming Services in Multi-User Downlink RSMA Networks
The exponential growth of the Internet of Multimedia Things (IoMT) traffic has posed a threat
of service quality degradation due to the limitation of current communication, networking …
of service quality degradation due to the limitation of current communication, networking …