Soft-Bar: a network-assisted resource allocation algorithm for throughput maximization and QoS provisioning in wireless data networks

2002 ◽  
Author(s):  
K. Chawla ◽  
Xiaoxin Qiu ◽  
J. Chuang ◽  
N. Sollenberger
Keyword(s):  
Resource Allocation ◽  
Throughput Maximization ◽  
Data Networks ◽  
Wireless Data Networks ◽  
Qos Provisioning ◽  
Wireless Data ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm

scholarly journals A Resource Allocation Algorithm for Ultra-Dense Networks Based on Deep Reinforcement Learning

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Huashuai Zhang ◽  
Tingmei Wang ◽  
Haiwei Shen
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Data Traffic ◽  
Wireless Data ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Q Learning ◽  
Dense Networks ◽  
Target Network ◽  
Wireless Resource Allocation

The resource optimization of ultra-dense networks (UDNs) is critical to meet the huge demand of users for wireless data traffic. But the mainstream optimization algorithms have many problems, such as the poor optimization effect, and high computing load. This paper puts forward a wireless resource allocation algorithm based on deep reinforcement learning (DRL), which aims to maximize the total throughput of the entire network and transform the resource allocation problem into a deep Q-learning process. To effectively allocate resources in UDNs, the DRL algorithm was introduced to improve the allocation efficiency of wireless resources; the authors adopted the resource allocation strategy of the deep Q-network (DQN), and employed empirical repetition and target network to overcome the instability and divergence of the results caused by the previous network state, and to solve the overestimation of the Q value. Simulation results show that the proposed algorithm can maximize the total throughput of the network, while making the network more energy-efficient and stable. Thus, it is very meaningful to introduce the DRL to the research of UDN resource allocation.

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