Dynamic Resource Allocation in Ad-Hoc Hybrid High-speed Wireless Networks

2006 ◽  
Author(s):  
A. Gaynulin ◽  
V. Krylov
Keyword(s):  
Resource Allocation ◽  
Wireless Networks ◽  
High Speed ◽  
Ad Hoc ◽  
Dynamic Resource Allocation ◽  
Dynamic Resource

scholarly journals Load-Balancing and QoS Based Dynamic Resource Allocation Method for Smart Gird Fiber-Wireless Networks

2019 ◽  
Vol 28 (6) ◽  
pp. 1234-1243 ◽  
Author(s):  
Siya Xu ◽  
Peng Li ◽  
Feng Qi ◽  
Shaoyong Guo ◽  
Guiping Zhou ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Wireless Networks ◽  
Load Balancing ◽  
Dynamic Resource Allocation ◽  
Allocation Method ◽  
Dynamic Resource ◽  
Smart Gird

scholarly journals Interference resilient stochastic prediction based dynamic resource allocation model for cognitive MANETs

2020 ◽  
Vol 13 (41) ◽  
pp. 4332-4350
Author(s):  
K Shashi Raj ◽  
Keyword(s):  
Resource Allocation ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Dynamic Resource Allocation ◽  
Allocation Strategy ◽  
Secondary Users ◽  
Allocation Model ◽  
Resource Allocation Strategy ◽  
Dynamic Resource ◽  
Mobile Ad Hoc

Background/Objectives: Being dynamic in nature, Mobile Ad-hoc Network (MANET) requires robust resource allocation strategy that can ensure both optimal transmission reliability and resource efficiency to meet Quality of Service (QoS) demands. The objective of this research is to address interference resilience requirement in MANETs which is must due to greedy nature of nodes especially when accessing resource or bandwidth and develop a highly robust stochastic prediction based resource allocation strategy. Methods: The proposed Interference Resilient Stochastic Prediction based Dynamic Resource Allocation model for Cognitive MANET (ISP-DRACM) intends to enable optimal resource allocation under interweave and underlay network setup with instantaneous as well as average interference conditions. It employs a joint power management and resource allocation strategy where it intends to maximize the weighted sum-rate of the secondary users under certain defined conditions like average power and stochastic interference level. Findings/Novelty: Inculcating resource allocation problem as controlled Markov Decision Process using Hidden Markov Model (HMM) and Lagrange relaxation, our proposed model achieves better resource allocation under limited noise or interference condition and hence achieves both costeffectiveness as well as QoS provision. This method has exhibited satisfactory performance towards spectrum allocation to the secondary users without imposing any significant interference for both interweave as well as underlay Cognitive Radio setup. Keywords: Cognitive mobile ad-hoc network; stochastic prediction; interference resilience; channel state information; dynamic resource allocation; underlay and overlay cognitive MANET

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