Journal ArticleDOI
Deep Reinforcement Learning for Network Selection over Heterogeneous Health Systems
TLDR
This paper leverage the dense heterogeneous network (HetNet) architecture over 5 G network to enhance network capacity and provide seamless connectivity for smart health systems, and formulate an optimization model that integrates the network selection problem with adaptive compression, at the network edge, to minimize the transmission energy consumption and latency.Abstract:
Smart health systems improve our quality of life by integrating information and technology into health and medical practices. Such technologies can significantly improve the existing health services. However, reliability, latency, and limited network resources are among the many challenges hindering the realization of smart health systems. Thus, in this paper, we leverage the dense heterogeneous network (HetNet) architecture over 5G network to enhance network capacity and provide seamless connectivity for smart health systems. However, network selection in HetNets is still a challenging problem that needs to be addressed. Inspired by the success of Deep Reinforcement Learning (DRL) in solving complicated control problems, we present a novel DRL model for solving the network selection problem with the aim of improving medical data delivery over heterogeneous health systems. Specifically, we integrate the network selection problem with adaptive compression at the edge to formulate an optimization model that aims at minimizing the transmission energy consumption while meeting diverse applications' Quality of service (QoS) requirements. Our experimental results show that the proposed DRL model could minimize the energy consumption and cost compared to the greedy techniques while meeting different users' demands in high dynamics environments.read more
Citations
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Journal ArticleDOI
The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions
TL;DR: A systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.
Posted ContentDOI
Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey
TL;DR: A systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.
Journal ArticleDOI
Network Selection Based on Evolutionary Game and Deep Reinforcement Learning in Space-Air-Ground Integrated Network
TL;DR: A network selection algorithm based on evolutionary game is proposed to study the autonomous decision-making process of network selection as a supplement and a deep deterministic policy gradient (DDPG)-based network selection algorithms to handle continuous and high-dimensional action spaces are proposed.
Journal ArticleDOI
Multi-Agent Reinforcement Learning for Network Selection and Resource Allocation in Heterogeneous Multi-RAT Networks
Mhd Saria Allahham,Alaa Awad Abdellatif,Naram Mhaisen,Amr Samy Mohamed,Aiman Erbad,Mohsen Guizani +5 more
TL;DR: This work proposes a distributed framework for dynamic network selection at the edge level, and resource allocation at the Radio Access Network (RAN) level, while taking into consideration diverse applications’ characteristics, and depicts that the solution outperforms state-of-the-art techniques of network selection in terms of energy consumption, latency, and cost.
Journal ArticleDOI
Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets
TL;DR: 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-generation heterogeneous wireless networks (HetNets), and proposes a hierarchical multi-agent deep reinforcement learning (DRL)-based framework, called DeepRAT, to solve it efficiently and learn system dynamics.