Holistic network virtualization and pervasive network intelligence for 6G
In this tutorial paper, we look into the evolution and prospect of network architecture and
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …
[HTML][HTML] Reconfigurable intelligent surface: Design the channel–A new opportunity for future wireless networks
In this paper, we survey state-of-the-art research outcomes in the burgeoning field of
Reconfigurable Intelligent Surface (RIS), given its potential for significant performance …
Reconfigurable Intelligent Surface (RIS), given its potential for significant performance …
Deep reinforcement learning-based energy-efficient edge computing for internet of vehicles
Mobile network operators (MNOs) allocate computing and caching resources for mobile
users by deploying a central control system. Existing studies mainly use programming and …
users by deploying a central control system. Existing studies mainly use programming and …
[HTML][HTML] Application of reinforcement learning and deep learning in multiple-input and multiple-output (MIMO) systems
The current wireless communication infrastructure has to face exponential development in
mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems …
mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems …
Hierarchical multi-agent DRL-based framework for joint multi-RAT assignment and dynamic resource allocation in next-generation HetNets
This article considers the problem of cost-aware downlink sum-rate maximization via joint
optimal radio access technologies (RATs) assignment and power allocation in next …
optimal radio access technologies (RATs) assignment and power allocation in next …
Attack-resistant, energy-adaptive monitoring for smart farms: Uncertainty-aware deep reinforcement learning approach
This work proposes an energy-adaptive monitoring system for a smart farm using solar
sensors attached to cows. The proposed system aims to achieve a high monitoring quality in …
sensors attached to cows. The proposed system aims to achieve a high monitoring quality in …
Offline reinforcement learning for wireless network optimization with mixture datasets
The recent development of reinforcement learning (RL) has boosted the adoption of online
RL for wireless radio resource management (RRM). However, online RL algorithms require …
RL for wireless radio resource management (RRM). However, online RL algorithms require …
[HTML][HTML] Research on task offloading optimization strategies for vehicular networks based on game theory and deep reinforcement learning
L Wang, W Zhou, H Xu, L Li, L Cai, X Zhou - Frontiers in Physics, 2023 - frontiersin.org
With the continuous development of the 6G mobile network, computing-intensive and delay-
sensitive onboard applications generate task data traffic more frequently. Particularly, when …
sensitive onboard applications generate task data traffic more frequently. Particularly, when …
DeepRAT: A DRL-based framework for multi-RAT assignment and power allocation in HetNets
Wireless heterogeneous networks (HetNets), where several systems with multi-radio access
technologies (multi-RATs) coexist for massive multi-connectivity networks, are in service …
technologies (multi-RATs) coexist for massive multi-connectivity networks, are in service …
Framework: Clustering-Driven Approach for Base Station Parameter Optimization and Automation (CeDA-BatOp)
SP Jayakumar, A Conte - 2024 IEEE 21st Consumer …, 2024 - ieeexplore.ieee.org
The growing demand for fast and reliable wireless services has led to the deployment of
more base stations, which has made manual optimization of base station parameters more …
more base stations, which has made manual optimization of base station parameters more …