Cognitive Radio in 4G/5G Wireless Communication Systems

Moghaddam, Shahriar Shirvani

doi:10.5772/intechopen.74815

Cited by 8 publications

(1 citation statement)

References 7 publications

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“…Two important capabilities that differentiate CRNs from the traditional wireless networks are the ability of the cognitive radio (CR) to adapt spectrum environment and protect the transmission of PUs (Alqahtani et al, 2023;Moghaddam, 2018;Cavalcanti and Ghosh, 2008) for efficient spectrum utilization. Figure 1 shows a CRN structure indicating how PUs and SUs can coexist for data transmission.…”

Section: Introductionmentioning

confidence: 99%

A QoS-aware framework for spectrum characterization and switching decision in cognitive radio networks

Asuquo,

Umoh,

Ekpenyong

et al. 2023

wojast

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The increasing demand for seamless wireless services coupled with underutilization of limited spectrum due to fixed channel allocation policy have posed numerous challenges to wireless communications. There is need to design appropriate channel allocation strategies for continuous communication by mobile users for higher spectral efficiency. Cognitive radio network (CRN) offers a solution to the spectrum scarcity problem inherent in 4G and other networks through dynamic spectrum access, by allowing unlicensed secondary users (SUs) with cognitive devices to opportunistically access the spectrum holes when the licensed primary users (PUs) are not occupying them. Often times, measurements taken by SUs during the sensing process are uncertain due to multipath fading, shadowing and varying channel conditions. This results in imprecise spectrum detection and selection by SUs during switching decisions causing incessant spectrum handoff and undesirable ping-pong effect. In this paper, a support vector machine (SVM) classifier is used to categorize spectrum into two classes of busy and idle. Then, based on heterogeneous quality of service (QoS) requirements of the SUs, dynamic activities of PUs, and the fluctuating channel state information, a QoS-aware Adaptive Neuro-Fuzzy Inference System (ANFIS) framework is developed for spectrum switching decision using underlay spectrum access model. SVM predicted spectrum holes with 98.8% accuracy. ANFIS model yielded a 91.62% accuracy in the task of allocating spectrum holes to SUs for coexistent with PUs. Results further indicate that the intelligent framework can ensure fairness among SUs, reduce interference, improve throughput, and spectral efficiency. It can be deployed in disaster relief and emergency, public safety, and battlefield environments.

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Section: Introductionmentioning

confidence: 99%

A QoS-aware framework for spectrum characterization and switching decision in cognitive radio networks

Asuquo,

Umoh,

Ekpenyong

et al. 2023

wojast

View full text Add to dashboard Cite

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A General Framework for Sorting Large Data Sets Using Independent Subarrays of Approximately Equal Length

Moghaddam

2022

IEEE Access

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Designing an efficient data sorting algorithm that requires less time and space complexity is essential for computer science, different engineering disciplines, data mining systems, wireless networks, and the Internet of things. This paper proposes a general low-complex data sorting framework that distinguishes the sorted or similar data, makes independent subarrays approximately in equal length, and sorts the subarrays' data using one of the popular comparison-based sorting algorithms. Two frameworks, one for serial realization and another for parallel realization, are proposed. The time complexity analyses of the proposed framework demonstrate an improvement compared to the conventional Merge and Quick sorting algorithms. Following complexity analysis, the simulation results indicate slight improvements in the elapsed time and the number of swaps of the proposed serial Merge-based and Quick-based frameworks compared to the conventional ones for low/high variance integer/non-integer data sets, in different data sizes and the number of divisions. It is about (1−1.6%) to (3.5−4%) and (0.3−1.8%) to (2−4%) improvements in the elapsed times for 1, 2, 3, and 4 divisions, respectively for small and very large data sets in Mergebased and Quick-based scenarios. Although these improvements in serial realization are minor, making independent low-variance subarrays allows the sorted components to be extracted sequentially and gradually before the end of the sorting process. Also, it proposes a general framework for parallelizing conventional sorting algorithms using non-connected (independent) or connected (dependent) multi-core structures. As the second experiment, the numerical analyses that compare the results of the parallel realization of the proposed framework to the serial one in 1, 2, 3, and 4 divisions, show a speedup factor of (2 − 4) for small to (2 − 16) for very large data sets. The third experiment shows the effectiveness of the proposed parallel framework to the parallel sorting based on the random-access machine model. Finally, we prove that the mean-based pivot is as efficient as the median-based and much better than the random pivot for making subarrays of approximately equal length.

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A Fast Sub-Optimum Access Point Selection in Ultra-Dense Networks

Moghaddam

2021

2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)

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Cognitive Radio in 4G/5G Wireless Communication Systems

Cited by 8 publications

References 7 publications

A QoS-aware framework for spectrum characterization and switching decision in cognitive radio networks

A QoS-aware framework for spectrum characterization and switching decision in cognitive radio networks

A General Framework for Sorting Large Data Sets Using Independent Subarrays of Approximately Equal Length

A Fast Sub-Optimum Access Point Selection in Ultra-Dense Networks

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