Interference Resistant Waveform for Cognitive Radio MANET

Kaszuba-Chęcińska, Anna; Chęciński, R.; Gajewski, Piotr; Łopatka, Jerzy

doi:10.23919/ursi48707.2020.9254041

“…This paper addresses the question of what communications and networking technology can be used to fully realize data exchange by handheld radios in a complex radio-environment in the battlefield. It is an extension of work described in [ 15 ]. The concept of the waveform, which can prevent intentional jamming and interferences from other systems, has been demonstrated.…”

Section: Introductionmentioning

confidence: 93%

Cognitive Radio MANET Waveform Design and Evaluation

Kaszuba-Chęcińska

¹

,

Chęciński

²

,

Gajewski

³

et al. 2021

Sensors

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The problem of waveform construction for mobile ad hoc networks with cognitive radio (MANET-CR) is discussed. This is the main limitation to widely use this very attractive technique, which does not need the deployment of expensive communication infrastructure. Two main questions correspond to MANET-CR effectiveness: spectrum sensing and spectrum sharing. The paper presents the structure of CR nodes that enables Opportunistic Spectrum Sharing. Procedures for advanced Dynamic Spectrum Management together with the concept of policy-based radio and a sensing method are presented. In the proposed system, the basic policy is to avoid interference generated by other users or jammers. The experiments were performed in a real environment, using the elaborated testbed. The results show that the use of sensing and cognitive management mechanisms enable more efficient use of the spectrum while maintaining reasonable overhead values related to the management procedures.

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“…The system's idle and underutilised bands are absorbed by the new frequencies in interweave. The cognitive radio uses these network techniques to integrate additional frequencies into the current spectrum [8].…”

Section: Introductionmentioning

confidence: 99%

Design of an adaptive CRSN using OFDM

Kandasamy¹,

Ravi²

2022

Wireless Netw

0

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Cognitive Radio (CR) is a novel concept that enables wireless devices to detect and adapt to their surroundings in order to enhance communication quality. The cognitive radio sensor network (CRSN) has proved to be a cost-effective solution to the spectrum constraints that wireless sensor networks (WSN). Optimizing the optimum packet size is regarded to be an essential energy constrained issue to address the practical implementation of CRSN out of all the difficulties. Small packets generate data traffic in device-to-device communication, a flexible way for transferring data in wireless networks, while big packets may cause data bit corruption, requiring retransmission at a greater frequency. This will not allow access from the secondary network to the main network, since it may cause further disturbance. To maximise the WSN's energy efficiency, the optimum packet size for CRSN should be utilised while keeping the same degree of interference as the primary licenced users (PU). The purpose of this article is to examine formulations for small, medium, and large packet sizes in order to determine the optimum packet size for adaptive CRSN. To do so, CR requires a flexible physical layer capable of carrying out the necessary tasks. This article examines the performance of CR systems that use the Orthogonal Frequency Division Multiplexing (OFDM) technique, which is a possible transmission technology for CR. Interference delays are minimised, and the channels are ultimately utilised effectively. This article shows that medium-sized packets are the optimum choice for achieving the greatest performance on the Cognitive Radio Sensor Network (CRSN). The Jellyfish Search Optimization algorithm (JSO) and the hybrid Momentum Search Algorithm (MSA) are hybridised, and results are achieved. This makes it possible to calculate precise packet sizes. The suggested approach decision outperforms existing methods like the Group Sparse Optimization algorithm and the Throughput Maximization Algorithm. The MATLAB / SIMULINK Platform were used to get the results.

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Data Packet Optimization Incognitive Radio Sensor Network Using Ofdm

Kandasamy¹,

Ravi²

2022

Preprint

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Cognitive Radio (CR) is a novel concept that enables wireless devices to detect and adapt to their surroundings in order to enhance communication quality. The cognitive radio sensor network (CRSN) has proved to be a cost-effective solution to the spectrum constraints that wireless sensor networks (WSN). Optimizing the optimum packet size is regarded to be an essential energy constrained issue to address the practical implementation of CRSN out of all the difficulties. Small packets generate data traffic in device-to-device communication, a flexible way for transferring data in wireless networks, while big packets may cause data bit corruption, requiring retransmission at a greater frequency. This will not allow access from the secondary network to the main network, since it may cause further disturbance. To maximise the WSN's energy efficiency, the optimum packet size for CRSN should be utilised while keeping the same degree of interference as the primary licenced users (PU). The purpose of this article is to examine formulations for small, medium, and large packet sizes in order to determine the optimum packet size for adaptive CRSN. To do so, CR requires a flexible physical layer capable of carrying out the necessary tasks. This article examines the performance of CR systems that use the Orthogonal Frequency Division Multiplexing (OFDM) technique, which is a possible transmission technology for CR. Interference delays are minimised, and the channels are ultimately utilised effectively. This article shows that medium-sized packets are the optimum choice for achieving the greatest performance on the Cognitive Radio Sensor Network (CRSN). The Jellyfish Search Optimization algorithm (JSO) and the hybrid Momentum Search Algorithm (MSA) are hybridised, and results are achieved. This makes it possible to calculate precise packet sizes. The suggested approach decision outperforms existing methods like the Group Sparse Optimization algorithm and the Throughput Maximization Algorithm. The MATLAB / SIMULINK Platform were used to get the results.

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Interference Resistant Waveform for Cognitive Radio MANET

Cited by 5 publications

References 14 publications

Cognitive Radio MANET Waveform Design and Evaluation

Cognitive Radio MANET Waveform Design and Evaluation

Design of an adaptive CRSN using OFDM

Data Packet Optimization Incognitive Radio Sensor Network Using Ofdm

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