Distributed algorithm to reduce contention in emergency situations by deploying cognitive radio
            <i>ad‐hoc</i>
            controllers

Sudhakaran, Chitra; Suganthi, M.

doi:10.1049/iet-com.2018.5833

Cited by 3 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The thresholds were obtained using Sevcik fractal dimension (SFD) of the received signal where the presence of PU signal was predicted by analysing different characteristics of the SFD between signal and noise. In [11], the authors explained how the CRT can be utilised in emergency circumstances using a decode and forward relay controller scheme.…”

Section: Introductionmentioning

confidence: 99%

Improvement in the performance of mobile/hidden secondary users in multi‐user CRN

Khedkar

Patil

2020

IET Communications

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Improvement in the performance of mobile/hidden secondary users in multi‐user CRN

Khedkar

Patil

2020

IET Communications

View full text Add to dashboard Cite

MACBHA: Modified Adaptive Cluster-Based Heuristic Approach with Co-operative Spectrum Sensing in Wireless Sensor Networks

Devaraj¹,

Aruna²

2020

Wireless Pers Commun

View full text Add to dashboard Cite

Intelligent Cognitive Radio Ad-Hoc Network: Planning, Learning and Dynamic Configuration

2021

View full text Add to dashboard Cite

Cognitive radio (CR) is an adaptive radio technology that can automatically detect available channels in a wireless spectrum and change transmission parameters to improve the radio operating behavior. A CR ad-hoc network (CRAHN) should be able to coexist with primary user (PU) systems and other CR secondary systems without causing harmful interference to licensed PUs as well as dynamically configure autonomous and decentralized networks. Therefore, an intelligent system structure is required for efficient spectrum use. In this paper, we present a learning-based distributed autonomous CRAHN network system model for network planning, learning, and dynamic configuration. Based on the system model, we propose machine learning-based optimization algorithms for spectrum sensing, cluster-based ad-hoc network configuration, and context-aware signal classification. Using the sensing engine and the cognitive engine, the surrounding spectrum usage and the neighbor network operation status can be analyzed. The proposed policy engine can create network operation policies for the dynamically changing surrounding wireless environment, detect policy conflicts, and infer the optimal policy for the current situation. The decision engine finally determines and configures the optimal CRAHN configuration parameters through cooperation with a learning engine, in which we implement the proposed machine-learning algorithms. The simulation results show that the proposed machine-learning CRAHN algorithms can construct CR cluster networks that have a long network lifetime and high spectrum utility. Additionally, with high signal context recognition performance, we can ensure coexistence with neighboring systems.

show abstract

Distributed algorithm to reduce contention in emergency situations by deploying cognitive radio ad‐hoc controllers

Cited by 3 publications

References 14 publications

Improvement in the performance of mobile/hidden secondary users in multi‐user CRN

Improvement in the performance of mobile/hidden secondary users in multi‐user CRN

MACBHA: Modified Adaptive Cluster-Based Heuristic Approach with Co-operative Spectrum Sensing in Wireless Sensor Networks

Intelligent Cognitive Radio Ad-Hoc Network: Planning, Learning and Dynamic Configuration

Contact Info

Product

Resources

About