Interference Control Mechanism Based on Deep Reinforcement Learning in Narrow Bandwidth Wireless Network Environment

2021 ◽  
pp. 368-377
Author(s):  
Hao Li ◽  
Jianli Guo ◽  
Xu Li ◽  
Xiujuan Shi ◽  
Peng Yu
2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Ruyan Wang ◽  
Yang Tang ◽  
Junjie Yan

Based on the “storing-carrying-forwarding” transmission manner, the packets are forwarded flexibly in Intermittently Connected Wireless Network (ICWN). However, due to its limited resources, ICWN can easily become congested as a large number of packets entering into it. In such situation, the network performance is seriously deteriorated. To solve this problem, we propose a congestion control mechanism that is based on the network state dynamic perception. Specifically, through estimating the congestion risk when a node receives packets, ICWN can reduce the probability of becoming congested. Moreover, due to ICWN’s network dynamics, we determine the congestion risk threshold by jointly taking into account the average packet size, average forwarding risk, and available buffer resources. Further, we also evaluate the service ability of a node in a distributed manner by integrating the recommendation information from other intermediate nodes. Additionally, a node is selected as a relay node according to both the congestion risk and service ability. Simulation results show that the network performance can be greatly optimized by reducing the overhead of packet forwarding.


Author(s):  
Gustavo Pereira Mateus ◽  
Beatriz Wilges ◽  
Mario Antonio Ribeiro Dantas ◽  
Silvia Modesto Nassar

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