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Cognitive radio (CR) is an effective method to optimize the use of spectrum resources. Spectrum sensing plays significant role in finding free channels that can be used with CR. the most important One of issues in CR is interference to the primary user (PU). Consequently, there is a significant research gap for an algorithm that distributes channels dynamically and takes into consideration all kinds of interference.This study looks at a universal optimal voting rule to an algorithm to determine the optimum parameters λ opt , n opt that minimize the Bayes risk cost function which has different weights for the probability of false alarm Q f and the missing probability Q m in general. As discussed formerly, Q m is most important from Q f . On another side, an algorithm that determines other technique which gives a large priority for Q m over the Q f . In this technique, we determine the optimum parameters λ opt , n opt , Supposing that secondary users (K) are fixed, i.e. what is the optimum fusion rule n opt and optimum threshold λ opt that minimizes Q f with constraint on Q m .In this study, we introduced a technique that ensures Qm ​ remains below a specified threshold while minimizing Qf as quickly as possible. We developed an algorithm to determine the optimal fusion rule and threshold that minimize Qf while keeping Qm​below the given threshold. The performance of cooperative spectrum sensing was analyzed using hard combination fusion rules. Among these, the OR rule showed superior performance in cooperative spectrum sensing for Cognitive Radio, outperforming the AND rule. [ABSTRACT FROM AUTHOR]

Spectrum sensing is a critical component of cognitive radio systems, enabling the detection and utilization of underutilized frequency bands. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in particular, have showed promise in recent years for enhancing the precision and effectiveness of spectrum sensing. This paper presents an overview of spectrum sensing using CNNs and RNNs and their performance in cognitive radio systems. Furthermore, the paper delves into the spectrum sensing performance of RNNs, particularly for processing time-series data. RNNs are capable of capturing temporal dependencies in sequential data, which is essential for spectrum sensing tasks where signals evolve over time. Further, the parakeets such as probability of detection (Pd), Probability of false alarm (PFA), Bite Error rate (BER) and Power spectral density (PSD) are analysed for spectrum sensing algorithms. RNNs and CNNs, two examples of deep learning methods (DLM), performed better than more traditional approaches.

Spectrum sensing is a critical component of cognitive radio systems, enabling the detection and utilization of underutilized frequency bands. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in particular, have showed promise in recent years for enhancing the precision and effectiveness of spectrum sensing. This paper presents an overview of spectrum sensing using CNNs and RNNs and their performance in cognitive radio systems. Furthermore, the paper delves into the spectrum sensing performance of RNNs, particularly for processing time-series data. RNNs are capable of capturing temporal dependencies in sequential data, which is essential for spectrum sensing tasks where signals evolve over time. Further, the parakeets such as probability of detection (Pd), Probability of false alarm (PFA), Bite Error rate (BER) and Power spectral density (PSD) are analysed for spectrum sensing algorithms. RNNs and CNNs, two examples of deep learning methods (DLM), performed better than more traditional approaches. [ABSTRACT FROM AUTHOR]

The implementation of the generalized detector (GD) in cognitive radio (CR) systems allows us to improve the spectrum sensing performance in comparison with employment of the conventional detectors. We analyze the spectrum sensing performance for the uncorrelated and spatially correlated receive antenna array elements. Addi¬tionally, we consider a practical case when the noise power at the output of GD linear systems (the preliminary and additional filters) is differed by value. The choice of the optimal GD threshold based on the minimum total error rate criterion is also discussed. Simulation results demonstrate superiority of GD implementation in CR sys¬tem as spectrum sensor in comparison with the energy detector (ED), weighted ED (WED), maximum-minimum eigenvalue (MME) detector, and generalized likelihood ratio test (GLRT) detector

Cognitive Radio has been skillful technology to improve the spectrum sensing as it enables Cognitive Radio to find Primary User (PU) and let secondary User (SU) to utilize the spectrum holes. However detection of PU leads to longer sensing time and interference. Spectrum sensing is done in specific "time frame" and it is further divided into Sensing time and transmission time. Higher the sensing time better will be detection and lesser will be the probability of false alarm. So optimization technique is highly required to address the issue of trade-off between sensing time and throughput. This paper proposed an application of Genetic Algorithm technique for spectrum sensing in cognitive radio. Here results shows that ROC curve of GA is better than PSO in terms of normalized throughput and sensing time. The parameters that are evaluated are throughput, probability of false alarm, sensing time, cost and iteration. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) technology allows the unlicensed user to access the licensed spectrum bands. Spectrum sensing is an essential function in cognitive radio to detect the spectrum holes and opportunistically use the underutilized frequency bands without causing interference to primary user (PU). In this paper we are maximizing the throughput capacity of cognitive radio user and hence the performance of spectrum sensing and protection to licensed user improves over a wideband spectrum sensing band. The simulation of cognitive radio is done by analyzing the performance of energy detector spectrum sensing technique to detect primary user and to formulate the optimization using multiband joint detection method (MJD) to achieve suitable trade- off between secondary user access and primary user network. The main aim of this paper is to maximize the probability of detection and to decrease the probabilities of miss detection and false alarm. To maximize the throughput it requires minimizing the throughput loss caused by miss detection and the significant reduction in probability of false alarm helps in achieving the spectral efficiency from the secondary user’s perspective. The simulation results show that the performance increases with the MJD method.

Cognitive Radio is an emerging and fascinating technology, which promises solution to the spectrum scarcity problem. This influential technology has the power to change the future technological trends forever if employed properly. In Cognitive Radio Networks, an unlicensed (secondary) user utilizes the licensed spectrum band opportunistically without causing any interference to the primary transmission or licensed (primary) users. Spectrum Sensing is the foremost task for successful Opportunistic Spectrum Access. It is a cardinal function of cognitive radio to avert the harmful interference with licensed users and recognize the available spectrum for ameliorating spectrum utilization. Over the past decade, legions of spectrum sensing techniques have been proposed in the literature. In this paper, a brief survey of all the major contributions in the field of single user or non-cooperative spectrum sensing for cognitive radio networks have been discussed. A comparative study has been carried out based on various important parameters, which needs to be considered for real-time implementation of Cognitive Radio Networks. [ABSTRACT FROM AUTHOR]

The implementation of the generalized detector (GD) in cognitive radio (CR) systems allows us to improve the spectrum sensing performance in comparison with employment of the conventional detectors. We analyze the spectrum sensing performance for the uncorrelated and spatially correlated receive antenna array elements. Additionally, we consider a practical case when the noise power at the output of GD linear systems (the preliminary and additional filters) is differed by value. The choice of the optimal GD threshold based on the minimum total error rate criterion is also discussed. Simulation results demonstrate superiority of GD implementation in CR system as spectrum sensor in comparison with the energy detector (ED), weighted ED (WED), maximum-minimum eigenvalue (MME) detector, and generalized likelihood ratio test (GLRT) detector. [ABSTRACT FROM AUTHOR]

Cognitive Radio (CR) users need to sense the environment or channel at regular time interval for sharing the spectrum band of the primary users (PUs). Once find the spectrum idle, CR users start their transmission through it. Even while transmitting, they need to continue the sensing process so that they can leave the spectrum immediately whenever find a PU wanting to use the band. Therefore, detecting PUs is one of the main functions of cognitive radio before transmission and higher the detection probability ensures better protection to the primary users. However, it is not possible to attain a high detection probability (or a low miss detection probability) and low false alarm probability simultaneously as there is a tradeoff between false alarm probability (Pfa) and the probability of detection (Pd). In this paper, the author has provided a comprehensive study on different sensing techniques and discussed their advantages and disadvantages. Moreover, it is expected that, with this article, readers can have a through understanding of sensing techniques in CR and the current research trends in this area. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) technology allows the unlicensed user to access the licensed spectrum bands. Spectrum sensing is an essential function in cognitive radio to detect the spectrum holes and opportunistically use the underutilized frequency bands without causing interference to primary user (PU). In this paper we are maximizing the throughput capacity of cognitive radio user and hence the performance of spectrum sensing and protection to licensed user improves over a wideband spectrum sensing band. The simulation of cognitive radio is done by analyzing the performance of energy detector spectrum sensing technique to detect primary user and to formulate the optimization using multiband joint detection method (MJD) to achieve suitable trade- off between secondary user access and primary user network. The main aim of this paper is to maximize the probability of detection and to decrease the probabilities of miss detection and false alarm. To maximize the throughput it requires minimizing the throughput loss caused by miss detection and the significant reduction in probability of false alarm helps in achieving the spectral efficiency from the secondary users perspective. The simulation results show that the performance increases with the MJD method. [ABSTRACT FROM AUTHOR]