Intelligent cloud-edge collaborations assisted energy-efficient power control in heterogeneous networks

L Zhang, J Peng, J Zheng… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
We consider a typical heterogeneous network (HetNet), which consists of a macro base
station (BS) and multiple small BSs sharing the same spectrum band. Since the spectrum …

[HTML][HTML] Task Offloading Decision-Making Algorithm for Vehicular Edge Computing: A Deep-Reinforcement-Learning-Based Approach

W Shi, L Chen, X Zhu - Sensors, 2023 - mdpi.com
Efficient task offloading decision is a crucial technology in vehicular edge computing, which
aims to fulfill the computational performance demands of complex vehicular tasks with …

Intelligent Computation Offloading for Joint Communication and Sensing-Based Vehicular Networks

H Yang, Z Feng, Z Wei, Q Zhang, X Yuan… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
To realize an intelligent cooperative vehicle infrastructure system and high-level
autonomous driving, the introduction of the joint communication and sensing (JCS) …

Reliable and efficient lane changing behaviour for connected autonomous vehicle through deep reinforcement learning

S Alagumuthukrishnan, S Deepajothi, R Vani… - Procedia Computer …, 2023 - Elsevier
The establishment of future intelligent transport systems is dependable on the reliable and
seamless function of Connected and Autonomous Vehicles (CAV). Reinforcement learning …

[HTML][HTML] Resource allocation on blockchain enabled mobile edge computing system

X Zheng, Y Zhang, F Yang, F Xu - Electronics, 2022 - mdpi.com
Currently, the concept of Mobile Edge Computing (MEC) has been applied as a solution
against the plethora of demands for high-quality computing services. It comprises several …

Energy efficiency optimization for SWIPT-based D2D-underlaid cellular networks using multiagent deep reinforcement learning

S Muy, D Ron, JR Lee - IEEE Systems Journal, 2021 - ieeexplore.ieee.org
In this article, we study the optimization of energy efficiency in wireless device-to-device
(D2D-underlaid cellular networks where multiple D2D pairs adopt simultaneous wireless …

Deep reinforcement learning-based MEC offloading and resource allocation in uplink NOMA heterogeneous network

W Liu, Y He, J Zhang, J Qiao - … , Communications and IoT …, 2021 - ieeexplore.ieee.org
With the advancement of fifth generation (5G) technology, mobile edge computing (MEC)
has been considered an effective solution to 5G technical problems. The applications of non …

Energy efficient train-ground mmWave mobile relay system for high speed railways

L Wang, B Ai, Y Niu, Z Zhong, S Mao… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
The rapid development of high-speed railways (HSRs) puts forward high requirements on
the corresponding communication system. Millimeter wave (mmWave) can be a promising …

Resource allocation for multiple RISs assisted NOMA empowered D2D communication: A MAMP-DQN approach

L Guo, J Jia, Y Zou, J Chen, L Yang, X Wang - Ad Hoc Networks, 2023 - Elsevier
In this paper, we investigate the non-orthogonal multiple access (NOMA) empowered D2D
communications system, where multiple reconfigurable intelligent surfaces (RISs) are …

Deep Reinforcement Learning for Energy Efficient Routing and Throughput Maximization in Various Networks

V Mohanavel, M Tamilselvi… - … Conference on I …, 2022 - ieeexplore.ieee.org
Large bandwidth and more mobility are only two reasons why wireless and mobile networks
are fast overtaking wired ones as the preferred mode of connectivity. Heterogeneous …