Machine learning techniques and a case study for intelligent wireless networks
With the widespread deployment of wireless technologies and IoT, 5G wireless networks will
support various communication connectivity and services for the huge number of wireless …
support various communication connectivity and services for the huge number of wireless …
Reinforcement learning for self organization and power control of two-tier heterogeneous networks
R Amiri, MA Almasi, JG Andrews… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Self-organizing networks (SONs) can help to manage the severe interference in dense
heterogeneous networks (HetNets). Given their need to automatically configure power and …
heterogeneous networks (HetNets). Given their need to automatically configure power and …
Resource management in wireless networks via multi-agent deep reinforcement learning
We propose a mechanism for distributed resource management and interference mitigation
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …
Joint optimization of handover control and power allocation based on multi-agent deep reinforcement learning
D Guo, L Tang, X Zhang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In this paper, we study the handover (HO), and power allocation problem in a two-tier
heterogeneous network (HetNet), which consists of a macro base station, and some …
heterogeneous network (HetNet), which consists of a macro base station, and some …
The frontiers of deep reinforcement learning for resource management in future wireless HetNets: Techniques, challenges, and research directions
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
Deep reinforcement learning for dynamic uplink/downlink resource allocation in high mobility 5G HetNet
Recently, the 5G is widely deployed for supporting communications of high mobility nodes
including train, vehicular and unmanned aerial vehicles (UAVs) largely emerged as the …
including train, vehicular and unmanned aerial vehicles (UAVs) largely emerged as the …
Multistep multiagent reinforcement learning for optimal energy schedule strategy of charging stations in smart grid
An efficient energy scheduling strategy of a charging station is crucial for stabilizing the
electricity market and accommodating the charging demand of electric vehicles (EVs). Most …
electricity market and accommodating the charging demand of electric vehicles (EVs). Most …
Stochastic power adaptation with multiagent reinforcement learning for cognitive wireless mesh networks
As the scarce spectrum resource is becoming overcrowded, cognitive radio indicates great
flexibility to improve the spectrum efficiency by opportunistically accessing the authorized …
flexibility to improve the spectrum efficiency by opportunistically accessing the authorized …
Power control based on deep reinforcement learning for spectrum sharing
In the current researches, artificial intelligence (AI) plays a crucial role in resource
management for the next generation wireless communication network. However, traditional …
management for the next generation wireless communication network. However, traditional …
Hybrid policy-based reinforcement learning of adaptive energy management for the Energy transmission-constrained island group
L Yang, X Li, M Sun, C Sun - IEEE Transactions on Industrial …, 2023 - ieeexplore.ieee.org
This article proposes a hybrid policy-based reinforcement learning (HPRL) adaptive energy
management to realize the optimal operation for the island group energy system with energy …
management to realize the optimal operation for the island group energy system with energy …