ECCO: A Novel End-to-End Congestion Control Scheme in Multi-Hop Cognitive Radio Ad Hoc Networks

2019 ◽  
Vol 5 (1) ◽  
pp. 93-102 ◽  
Author(s):  
Dan Wang ◽  
Yi Song
2018 ◽  
Vol 72 ◽  
pp. 774-788 ◽  
Author(s):  
Kashif Naseer Qureshi ◽  
Abdul Hanan Abdullah ◽  
Omprakash Kaiwartya ◽  
Saleem Iqbal ◽  
Rizwan Aslam Butt ◽  
...  

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jing Gao ◽  
Changchuan Yin ◽  
Xi Han

Delay and throughput are important metrics for network performance. We analyze the end-to-end delay of cognitive radio ad hoc networks for two traffic models: backlogged and geometric, respectively. By modelling the primary users as a Poisson point process and the secondary network deploying multihop transmissions, we derive the closed-form expression for the end-to-end delay in secondary networks. Furthermore, we optimize the end-to-end delay in terms of the hop number and the secondary transmission probability, respectively. The range of the optimal hop number and the equation satisfied by the optimal transmission probability are obtained for backlogged source models. The equation met by the optimal hop number is presented for geometric source models.


2011 ◽  
Vol 68 (9) ◽  
pp. 859-875 ◽  
Author(s):  
Marco Di Felice ◽  
Kaushik Roy Chowdhury ◽  
Wooseong Kim ◽  
Andreas Kassler ◽  
Luciano Bononi

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Shaojie Wen ◽  
Lianbing Deng ◽  
Shuo Shi ◽  
Xiying Fan ◽  
Hao Li

Drastic changes in network topology of Flying Ad Hoc Networks (FANETs) result in the instability of the single-hop delay and link status accordingly. Therefore, it is difficult to implement the congestion control with delay-sensitive traffic according to the instantaneous link status. To solve the above difficulty effectively, we formulate the delay-aware congestion control as a network utility maximization, which considers the link capacity and end-to-end delay as constraints. Next, we combine the Lagrange dual method and delay auxiliary variable to decouple the link capacity and delay threshold constraints, as well as to update single-hop delay bound with the delay-outage mode. Built on the methods above, a distributed optimization algorithm is proposed in this work by considering the estimated single-hop delay bound for each transmission, which only uses the local channel information to limit the end-to-end delay. Finally, we deduce the relationship between the primal and dual solutions to underpin the advantages of the proposed algorithm. Simulation results demonstrate that the proposed algorithm effectively can improve network performances in terms of packet time-out rate and network throughput.


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