Identify the user presence by GLRT and NP detection criteria in cognitive radio spectrum sensing

Cognitive radio (CR) system plays a major role in spectrum sensing. In cognitive radio, the spectrum slot is assigned to user intellectually according to a prescribed algorithm. Different researche...

Performance analysis of HFDI computing algorithm in intelligent networks

In the recent developments, the spectrum sensing and detection plays a major importance in day to day communication and it is very much essential for the user to utilize the spectrum bandwidth effectively in cognitive radio (CR) networks. The major performance metrics constraint that causes severe problems in spectrum sensing are probability of false alarm $$(\hbox {P}_{\mathrm{fa}})$$ and probability of miss detection $$(\hbox {P}_{\mathrm{md}})$$. In the proposed paper, the authors made an attempt to enhance the characteristic performances compared to existing methods, matched filter detection, cyclostationary detection and hybrid filter detection. The three detection methods are incorporated in to this non cooperative detection method of CR systems. In the proposed research work, a simulation result are obtained by using MATLab of the modified detection methods and shows the better performance by improving probability of detection $$(\hbox {P}_{\mathrm{D}})$$ and reducing $$\hbox {P}_{\mathrm{fa}}$$, $$\hbox {P}_{\mathrm{md}}$$.

Optimized design and analysis approach of user detection by non cooperative detection computing methods in CR networks

In cognitive networks, efficient spectrum sensing is of great importance for communication of unlicensed secondary users (SU) without interfering with the communication of licensed primary users (PU). Such spectrum sensing requires robust and reliable communication between the SUs to sense the spectrum efficiently under different network circumstances and to make a quick decision for the data transmission. In this paper, we are proposing a decentralized cooperative algorithm for efficient sensing of spectrum in the networked cognitive radios. The proposed algorithm is investigated under crash and Byzantine failure environments to study their behavior and efficiency for consensus. Energy detector module is modeled for each cooperating SU in cognitive radio network for sensing the presence of PU in a dedicated spectrum. Moreover, SU is modeled as agents connected through undirected graphs to simulate communication among them related to the spectrum availability. Multiple simulation scenarios, based on autonomous SU using the proposed distributed consensus algorithm are presented to demonstrate the theoretical development of proposed algorithm to be visualized in real scenarios. The simulation results reveal that the proposed method provides a significant improvement in convergence rate, reliability, and in terms of various key performance indicators.

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Dynamic Spectrum Sensing Under Crash and Byzantine Failure Environments for Distributed Convergence in Cognitive Radio Networks

In recent works of computer science, especially in the fields of image processing and pattern recognition techniques with machine learning, considerable focus is given to plant taxonomy which enhances the abilities of people to recognize plant species. This paper presents a method that analyzes color images of leaves using a type of Convolutional Neural Network to recognize plant species. The proposed Neural Network consists of four convolutional layers followed by two Fully-Connected layers and a final soft-max layer to offer a feature representation for different plant species. Four max-pooling layers are performed over a 2 × 2 pixel window with stride 2. Results on five plant datasets viz. Leaf snap (52 plant species), UCI leaf (40 plant species), PlantVillage (38 plant species), Flavia (32 plant species) and Swedish (15 plant species) are tabulated that demonstrate the remarkable performance of the proposed deep neural network when compared to the state of art methods.

Optimized convolutional neural network model for plant species identification from leaf images using computer vision

Spectrum sensing has been a major issue while dealing with cognitive radio (CR) networks. Predominantly the situation arises where noise is falsely interpreted as primary user signal called as probability of false alarm (Pfa). The user presence is estimated based on the parameters Pfa, probability of detection (PD) and receiver operating characteristics (ROC). Matched filter detection (MFD) and Neyman Pearson (NP) observer approaches are existing methods used to identify the Pfa. MFD measured ROC with different algorithms and suggests NP observer to improve the PD and minimise the Pfa. This paper proposes a novel method of matched filter detection with inverse covariance matrix-based spectrum sensing (MDI-SS). The ROC is measured, compared between MDI-SS and MFD. Next by comparing NP observer and MDI-SS, the affected samples of Pfa are identified, tabulated for different SNR levels. Finally, comparative analysis has been proposed between MDI-SS with NP observer for PD and Pfa.

MDI-SS: matched filter detection with inverse covariance matrix-based spectrum sensing in cognitive radio

The Telecommunication networks in the world had a great leap. Over the last few years around 6 billion consumers own mobile phones. In the proposed paper, it has been analyzed the various generations of cellular systems has been studied in the evolution of mobile communications from First Generation (1G) to Seventh Generation (7G). It can be analyzed that this could be due to increase in the Telecommunication customers day by day. The world wide revolution in mobile Phones is changing the lives in term of the working, learning and interacting. In the proposed paper, it has been discussed to review various existing generations of Mobile wireless technologies. This paper throws light on the evolution and development of various generations of Mobile wireless technology along with their significance and advantages of one over the other. There are various applications like Paging Service for 1G, Short Message Service (SMS), Multi Media Service (MMS) for 2G, Video calling for 3G, high speed video calling for 4G and for 5G spectrum free and very fast internet service with Wireless World Wide Web.

P. Trinatha Rao

Papers

MDI-SS: matched filter detection with inverse covariance matrix-based spectrum sensing in cognitive radio

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