Learning-based flexible cross-layer optimization for ultrareliable and low-latency applications in IoT scenarios
With the continuous popularization and deepening of the Internet-of-Things (IoT)
technologies, trillions of IoT Devices (IoTD) are connected to the network. The huge growth …
technologies, trillions of IoT Devices (IoTD) are connected to the network. The huge growth …
Energy-efficiency maximization of multiple RISs-enabled communication networks by deep reinforcement learning
Reconfigurable Intelligent Surfaces (RISs) have become an emerging paradigm to improve
the average sum-rate, enhance energy efficiency and extend coverage areas in wireless …
the average sum-rate, enhance energy efficiency and extend coverage areas in wireless …
Energy efficiency maximization oriented resource allocation in 5G ultra-dense network: Centralized and distributed algorithms
Spurred by both economic and environmental concerns, energy efficiency (EE) has now
become one of the key pillars for the fifth generation (5G) mobile communication networks …
become one of the key pillars for the fifth generation (5G) mobile communication networks …
Deep multiagent reinforcement-learning-based resource allocation for internet of controllable things
B Gu, X Zhang, Z Lin, M Alazab - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
Ultrareliable and low-latency communication (URLLC) is a prerequisite for the successful
implementation of the Internet of Controllable Things. In this article, we investigate the …
implementation of the Internet of Controllable Things. In this article, we investigate the …
Deep reinforcement learning based resource allocation for heterogeneous networks
This paper investigates the problem of distributed resource management (ie, joint device
association, spectrum allocation, and power allocation) in two-tier heterogeneous networks …
association, spectrum allocation, and power allocation) in two-tier heterogeneous networks …
Deep reinforcement learning based resource management for DNN inference in IIoT
In this paper, we investigate the joint task assignment and resource allocation for deep
neural network (DNN) inference in the device-edge-cloud based industrial Internet of things …
neural network (DNN) inference in the device-edge-cloud based industrial Internet of things …
A new block-based reinforcement learning approach for distributed resource allocation in clustered IoT networks
Resource allocation and spectrum management are two major challenges in the massive
scale deployment of Internet of Things (IoT) and Machine-to-Machine (M2M) communication …
scale deployment of Internet of Things (IoT) and Machine-to-Machine (M2M) communication …
Joint EH time and transmit power optimization based on DDPG for EH communications
L Li, H Xu, J Ma, A Zhou, J Liu - IEEE Communications Letters, 2020 - ieeexplore.ieee.org
Energy management and power allocation policy is considered for energy harvesting (EH)
communications. In this letter, we propose a joint optimization problem with the continuous …
communications. In this letter, we propose a joint optimization problem with the continuous …
Deep reinforcement learning for dynamic access control with battery prediction for mobile-edge computing in green iot networks
L Xu, M Qin, Q Yang, KS Kwak - 2019 11th International …, 2019 - ieeexplore.ieee.org
Mobile Edge Computing (MEC) technology has emerged as a promising paradigm to reduce
the energy consumption for the resource-constrained and energy-limited Internet of Things …
the energy consumption for the resource-constrained and energy-limited Internet of Things …
Multi-agent DRL for task offloading and resource allocation in multi-UAV enabled IoT edge network
The Internet of Things (IoT) edge network has connected lots of heterogeneous smart
devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging …
devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging …