Spectrum-Efficient Resource Allocation in Multi-Radio Multi-Hop Cognitive Radio Networks

Han, Bin; Luo, Ying; Zeng, Min; Jiang, Hong

doi:10.3390/s19204493

Cited by 6 publications

(6 citation statements)

References 28 publications

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“…(2) The centralized algorithm [34] uses the clustering process criteria to acknowledge the base station. FA finalizes the CHs at base station to identify best optimal location for the cost function.…”

Section: Proposed Improved Cooperative Clustering Algorithm For Cogni...mentioning

confidence: 99%

Assessment of Dynamic Swarm Heterogeneous Clustering in Cognitive Radio Sensor Networks

Bhatt

Onyema

Almuzaini

et al. 2022

Wireless Communications and Mobile Computing

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Many optimization algorithms have been created to determine the most energy-efficient transmission mode, allowing for lower power consumption during transmission over shorter distances while minimising interference from primary users (PUs). The improved cooperative clustering algorithm (ICCA) performs superior spectrum sensing across groups of multiusers compared to any other method currently available in terms of sensing inaccuracy, power savings, and convergence time than any other method currently available. The proposed ICCA algorithm is employed in this research study to find the optimal numbers of clusters based on its connectivity and the most energy-efficient distributed cluster-based sensing technique available. In this research, many randomly chosen secondary users (SUs) and primary users (PUs) are investigated for potential implementation opportunities. Therefore, as compared to the current optimization strategies, the proposed ICCA algorithm enhanced the convergence speed by integrating the multiuser clustered communication into a single communication channel. Experimental results revealed that the new ICCA algorithm reduced node power by 9.646 percent compared to traditional ways when comparing the novel algorithm to conventional approaches. In a similar vein, as compared to the prior methodologies, the ICCA algorithm reduced the average node power of SUs by 24.23 percent on average. When the SNR is decreased to values below 2 dB, the likelihood of detection improves dramatically, as seen in the figure. ICCA has a low false alarm rate when matched to other optimization algorithms for direct detection, and the proposed method outperforms them all. Following the findings of the simulations, the proposed ICCA technique effectively addresses multimodal optimization difficulties and optimizes network capacity performance in wireless networks. A detailed discussion of SS applications for the IoT and wireless sensor networks, both based on CR, is provided. There is also a thorough discussion of the most recent advancements in spectrum sensing as a facility. IoT or WSN may be essential in feeding the CR networks with spectrum sensing data and the future of spectrum sensing. The use of CR for fifth generation and afar its potential application in frequency allocation are discussed. To stay up with the advancement of communication technology, SS should give additional features to remain competitive, like the capacity to investigate various available channels and accessible places for transmission. Based on present and prospective methods in wireless communications, we highlight the crucial upcoming study paths and difficulty spots in signal processing for cognitive radio and potential solutions (SS-CR).

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Section: Proposed Improved Cooperative Clustering Algorithm For Cogni...mentioning

confidence: 99%

Assessment of Dynamic Swarm Heterogeneous Clustering in Cognitive Radio Sensor Networks

Bhatt

Onyema

Almuzaini

et al. 2022

Wireless Communications and Mobile Computing

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“…We select three indicators to test the pixel regression framework : (1) The visual display of the radio environment maps; (2) the estimated power spectrum of primary users; (3) the average REMs estimating error (AREE) against different numbers of secondary users.…”

Section: Tests For the Prf Algorithmmentioning

confidence: 99%

“…Cognitive radio network (CRN) is a promising technology to optimize the utilization of the spectrum resources [1]. In the cognitive radio network, unlicensed users can access the spectrum holes in time, frequency and space or any of their combinations, provided they cause no interference [2].…”

Section: Introductionmentioning

confidence: 99%

“…It should be noted that we do not use the same sampling locations as the true incomplete REMs images, i.e., the color grids' locations of I r and I e are different in the training process of the PRF algorithm. The reasons are as follows: (1) In our proposed algorithm, the discriminator is trained to identify if the input incomplete images are sampled from the real complete REMs C r or from the estimated REMs C e . When we collect the true incomplete REMs for the training data set, the sensing nodes are set in a random way in the target area, i.e., the I r is an incomplete image randomly sampled from C r .…”

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confidence: 99%

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A Radio Environment Maps Estimation Algorithm based on the Pixel Regression Framework for Underlay Cognitive Radio Networks Using Incomplete Training Data

Xue

et al. 2020

Sensors

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In the underlay cognitive radio networks, the radio environment maps (REMs) estimation is the main challenge in sensing the idle wireless spectrum resources. Traditional deep learning-based algorithms estimate the REMs on the basis of the high-quality, large-scale complete training images. However, collecting the complete radio environment images is time-consuming and requires a numerous number of power spectrum sensing nodes. For this reason, we propose a generative adversarial networks-based pixel regression framework (PRF) for underlay cognitive radio networks. The PRF algorithm relaxes the requirement of the complete training images, and estimates the radio environment maps only on the basis of the incomplete REMs images, which are easier to be collected. First, we transform the radio environment maps estimation task into a pixel regression task through the color mapping progress. Then, to extract helpful information from the incomplete training data, we design a feature enhancing module for the PRF algorithm, which intelligently learns and emphasizes the important features from the training images. Finally, we use the trained pixel regression framework to reconstruct the radio environment maps in the target area. The proposed algorithm learns accurate radio environment characteristics from the incomplete training data rather than making direct biased or imprecise radio propagation assumptions as in the traditional methods. Thus, the PRF algorithm has a better REMs reconstruction performance than the traditional methods, as verified by simulations.Sensors 2020, 20, 2245 2 of 17 use the licensed band if the interference does not exceed the thresholds of the primary users because of the power attenuation in the wireless radio propagation path [8]. The above radio resources allocation scheme improves the spectrum efficiency by utilizing the wireless "white space", which is about the idle licensed spectrum resource in the space and frequency domains. The underlay CRN has strict requirements of the working power of the SUs, which can significantly improve the utilization of the radio resources based on the efficient and intelligent spectrum management.In the underlay cognitive radio network, we adopt the radio environment maps (REMs) to display the primary users' power spectrum (PS) in the CRN area. It is a visible map of the wireless environment, which adds the PS information into the spatial map. Based on the estimation of the REMs, we can handle the conflict between the SUs and PUs and maximize the utilization of the spectrum resources in a particular CRN region.Estimating and utilizing the radio environment maps is extraordinarily helpful in wide-area CRN [9]. To reduce the interference to PUs, the SUs intelligently change their transmitting power on the basis of the REMs, allowing the remote users to dynamically utilize the idle radio resources [10,11].As shown in Figure 1, for estimating the radio environment maps, the general setting includes several transmitting PUs and receiving SUs. We suppose that they ...

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“…Cognitive radio (CR) technology is an important technology in wireless communications. To achieve better exploitation of the radio resources, the CR transceivers intelligently change their transmitting parameters based on detecting and utilizing the radio "white holes" [1,2]. The term "white holes" refers to the unused radio resources in frequency, space, time domains, etc.…”

Section: Introductionmentioning

confidence: 99%

A Power Spectrum Maps Estimation Algorithm Based on Generative Adversarial Networks for Underlay Cognitive Radio Networks

Xue

Shao

et al. 2020

Sensors

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In the underlay cognitive radio networks, the main challenge in detecting the idle radio resources is to estimate the power spectrum maps (PSMs), where the radio propagation characteristics are hard to obtain. For this reason, we propose a novel PSMs estimation algorithm based on the generative adversarial networks (GANs). First, we constructed the PSMs estimation model as a regression model in deep learning. Then, we converted the estimation task into an image reconstruction task by image color mapping. We fulfilled the above task by designing an image generator and an image discriminator in the proposed maps’ estimation GANs (MEGANs). The generator is trained to extract the radio propagation characteristics and generate the PSMs images. However, the discriminator is trained to identify the generated images and help to improve the generator’s performance. With the training process of MEGANs, the abilities of the generator and the discriminator are enhanced continually until reaching a balance, which means a high-accuracy PSMs estimation is achieved. The proposed MEGANs algorithm learns and utilizes accurate radio propagation features from the training process rather than making direct imprecise or biased propagation assumptions as in the traditional methods. Simulation results demonstrate that the MEGANs algorithm provides a more accurate estimation performance than the conventional methods.

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Spectrum-Efficient Resource Allocation in Multi-Radio Multi-Hop Cognitive Radio Networks

Cited by 6 publications

References 28 publications

Assessment of Dynamic Swarm Heterogeneous Clustering in Cognitive Radio Sensor Networks

Assessment of Dynamic Swarm Heterogeneous Clustering in Cognitive Radio Sensor Networks

A Radio Environment Maps Estimation Algorithm based on the Pixel Regression Framework for Underlay Cognitive Radio Networks Using Incomplete Training Data

A Power Spectrum Maps Estimation Algorithm Based on Generative Adversarial Networks for Underlay Cognitive Radio Networks

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