Intelligent computation offloading for MEC-based cooperative vehicle infrastructure system: A deep reinforcement learning approach
In the cooperative vehicle infrastructure system, the road side unit (RSU) equipped with a
mobile edge computing (MEC) server and sensors could provide vehicle infrastructure …
mobile edge computing (MEC) server and sensors could provide vehicle infrastructure …
Joint interference alignment and power control for dense networks via deep reinforcement learning
This letter proposes a joint interference suppression scheme in heterogeneous networks
(HetNets) with dense small cells (SCs) and users. Different from the majority of existing …
(HetNets) with dense small cells (SCs) and users. Different from the majority of existing …
A comprehensive survey on aerial mobile edge computing: Challenges, state-of-the-art, and future directions
Driven by the visions of Internet of Things (IoT), there is an ever-increasing demand for
computation resources of IoT users to support diverse applications. Mobile edge computing …
computation resources of IoT users to support diverse applications. Mobile edge computing …
Deep reinforcement learning for computation and communication resource allocation in multiaccess MEC assisted railway IoT networks
J Xu, B Ai, L Chen, Y Cui… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Multi-access mobile edge computing (MEC) is envisioned as a key enabling technology to
support compute-intensive and delay-sensitive applications in railway Internet of Things …
support compute-intensive and delay-sensitive applications in railway Internet of Things …
Resource allocation based on digital twin-enabled federated learning framework in heterogeneous cellular network
Federated learning (FL) allows user devices (UDs) to upload local model parameters to
participate in a global model training, which protects UDs' data privacy. Nevertheless, FL still …
participate in a global model training, which protects UDs' data privacy. Nevertheless, FL still …
Computation Rate Maximization for SCMA-Aided Edge Computing in IoT Networks: A Multi-Agent Reinforcement Learning Approach
P Liu, K An, J Lei, Y Sun, W Liu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Integrating sparse code multiple access (SCMA) and mobile edge computing (MEC) into the
Internet of Things (IoT) networks can enable efficient connectivity and timely computation for …
Internet of Things (IoT) networks can enable efficient connectivity and timely computation for …
[HTML][HTML] Deep learning enhanced NOMA system: A survey on future scope and challenges
V Andiappan, V Ponnusamy - Wireless Personal Communications, 2022 - Springer
As a key important approach for next generation communication systems, Non-Orthogonal
Multiple Access (NOMA) has made high attention in the wireless communication. NOMA …
Multiple Access (NOMA) has made high attention in the wireless communication. NOMA …
Deep learning-based NOMA system for enhancement of 5G networks: A review
RK Senapati, PJ Tanna - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
The fresh and rising demands for high-reliability and ultrahigh-capacity wireless
communication have led to extensive research into 5G communications. The wide progress …
communication have led to extensive research into 5G communications. The wide progress …
Computation offloading and resource allocation based on DT-MEC-assisted federated learning framework
Traditional centralized machine learning uses a large amount of data for model training,
which may face some privacy and security problems. On the other hand, federated learning …
which may face some privacy and security problems. On the other hand, federated learning …
DDPG-based joint resource management for latency minimization in NOMA-MEC networks
In this letter, we consider the latency minimization problem in NOMA-MEC networks. Each
user offloads partial tasks to the MEC server for remote execution and processes the …
user offloads partial tasks to the MEC server for remote execution and processes the …