Utilization of idle time slot in spectrum sensing under noise uncertainty

<p>Spectrum sensing in cognitive radio (CR) is a critical process as it directly influences the accuracy of detection. Noise uncertainty affects the reliability of detecting vacant holes in the spectrum, thus limiting the access of that spectrum by secondary users (SUs). In such uncertain environment; SUs sense the received power of a primary user (PU) independently with different measures of signal-to-noise ratio (SNR). Long sensing time serves in mitigating the effect of noise uncertainty, but on the cost of throughput performance of CR system. In this paper, the scheme of an asynchronous and crossed sensing-reporting is presented. The scheme reduces energy consumption during sensing process without affecting the detection accuracy. Exploiting the included idle time (𝑇𝑖) in sensing time slot; each SU collects power samples with higher SNR directly performs the reporting process to a fusion center (FC) consecutively. The FC terminates the sensing and reporting processes at a specific sensing time that corresponds to the lowest SNR (𝑆𝑁𝑅𝑤𝑎𝑙𝑙). Furthermore, this integrated scheme aims at optimizing the total frame duration (𝑇𝑓). Mathematical expressions of the scheme are obtained. Analytical results show the efficiency of the scheme in terms of energy saving and throughput increment under noise uncerainty.</p>

Automated Security Light Motion Sensor with SMS Notification

The purpose of this study was to assist the faculty of the Institute of Information and Computer Studies in securing their records and personal devices that were left at the office. This study made use of developmental-descriptive research. In software development, the Rapid Application Development (RAD) model was used. The system was developed using the n-tier architectural design. Because the developed system will be deployed in the IICS Building, the concerned faculty can be added as secondary users of the system so that they, along with the guard-on-duty, receive notification whenever there is motion detected during the night. A short message service (SMS) support is essential as a communication tool in notifying the room custodian and security guard-on-duty.

Federated Learning for 5G Radio Spectrum Sensing

Spectrum sensing (SS) is an important tool in finding new opportunities for spectrum sharing. The users, called Secondary Users (SU), who do not have a license to transmit without hindrance, need to employ SS in order to detect and use the spectrum without interfering with the licensed users’ (primary users’ (PUs’)) transmission. Deep learning (DL) has proven to be a good choice as an intelligent SS algorithm that considers radio environmental factors in the decision-making process. It is impossible though for SU to collect the required data and train complex DL models. In this paper, we propose to employ a Federated Learning (FL) algorithm in order to distribute data collection and model training processes over many devices. The proposed method categorizes FL devices into groups by their mean Signal-to-Noise ratio (SNR) and creates a common DL model for each group in the iterative process. The results show that detection accuracy obtained via the FL algorithm is similar to detection accuracy obtained by employing several DL models, namely convolutional neural networks (CNNs), specialized in spectrum detection for a PU signal with a given mean SNR value. At the same time, the main goal of simplification of the SS process in the network is achieved.

A Coalition Formation Game for Cooperative Spectrum Sensing in Cognitive Radio Network under the Constraint of Overhead

Cooperative spectrum sensing improves the sensing performance of secondary users by exploiting spatial diversity in cognitive radio networks. However, the cooperation of secondary users introduces some overhead also that may degrade the overall performance of cooperative spectrum sensing. The trade-off between cooperation gain and overhead plays a vital role in modeling cooperative spectrum sensing. This paper considers overhead in terms of reporting energy and reporting time. We propose a cooperative spectrum sensing based coalitional game model where the utility of the game is formulated as a function of throughput gain and overhead. To achieve a rational average throughput of secondary users, the overhead incurred is to be optimized. This work emphasizes on optimization of the overhead incurred. In cooperative spectrum sensing, the large number of cooperating users improve the detection performance, on the contrary, it increases overhead too. So, to limit the maximum coalition size we propose a formulation under the constraint of the probability of false alarm. An efficient fusion center selection scheme and an algorithm to select eligible secondary users for reporting are proposed to reduce the reporting overhead. We also outline a distributed cooperative spectrum sensing algorithm using the properties of the coalition formation game and prove that the utility of the proposed game has non-transferable properties. The simulation results show that the proposed schemes reduce the overhead of reporting without compromising the overall detection performance of cooperative spectrum sensing.

Double Deep Recurrent Reinforcement Learning for Centralized Dynamic Multichannel Access

We consider the problem of dynamic multichannel access for transmission maximization in multiuser wireless communication networks. The objective is to find a multiuser strategy that maximizes global channel utilization with a low collision in a centralized manner without any prior knowledge. Obtaining an optimal solution for centralized dynamic multichannel access is an extremely difficult problem due to the large-state and large-action space. To tackle this problem, we develop a centralized dynamic multichannel access framework based on double deep recurrent Q-network. The centralized node first maps current state directly to channel assignment actions, which can overcome prohibitive computation compared with reinforcement learning. Then, the centralized node can be easy to select multiple channels by maximizing the sum of value functions based on a trained neural network. Finally, the proposed method avoids collisions between secondary users through centralized allocation policy.

Multiband Spectrum Sensing and Power Allocation for aCognitive Radio-Enabled Smart Grid

As part of an Internet of Things (IoT) framework, the Smart Grid (SG) relies on advanced communication technologies for efficient energy management and utilization. Cognitive Radio (CR), which allows Secondary Users (SUs) to opportunistically access and use the spectrum bands owned by Primary Users (PUs), is regarded as the key technology of the next-generation wireless communication. With the assistance of CR technology, the quality of communication in the SG could be improved. In this paper, based on a hybrid CR-enabled SG communication network, a new system architecture for multiband-CR-enabled SG communication is proposed. Then, some optimization mathematical models are also proposed to jointly find the optimal sensing time and the optimal power allocation strategy. By using convex optimization techniques, several optimal methods are proposed to maximize the data rate of multiband-CR-enabled SG while considering the minimum detection probabilities to the active PUs. Finally, simulations are presented to show the validity of the proposed methods.

Use Hierarchical Codebook to Improve the Primary User Detection in the Cognitive Radio’s Cooperative Spectrum Sensing

Currently, the cognitive network is receiving much attention due to the advantages it brings to users. An important method in cognitive radio networks is spectrum sensing, as it allows secondary users (SUs) to detect the existence of a primary user (PU). Information of probability of false detection or warning about the PU is sent to a fusion center (FC) by the SUs, from which the FC will decide whether or not to allow the SUs to use the PU spectrum to obtain information. The transmission of information with a high signal to noise ratio (SNR) will increase the FC's ability to detect the existence of the PU. However, researchers are currently focusing on probabilistic formulas assuming that the channel is known ideally or there is nominal channel information at the FC; moreover, one model where the FC only knows the channel correlation matrix. Furthermore, studies are still assuming this is a simple multiple input – multiple output (MIMO) channel model but do not pay much attention to the signal processing at the transmitting and receiving antennas between the SUs and the FCs. A new method introduced in this paper when combining beamforming and hierarchical codebook makes the ability to detect the existence of the PU at the FC significantly increased compared to traditional methods.

Eigenvalue-Based Spectrum Sensing with Small Samples Using Circulant Matrix

In cognitive radio (CR) networks, eigenvalue-based detectors (EBDs) have attracted much attention due to their good performance of detecting secondary users (SUs). In order to further improve the detection performance of EBDs with short samples, we propose two new detectors: average circulant matrix-based Roy’s largest root test (ACM-RLRT) and average circulant matrix-based generalized likelihood ratio test (ACM-GLRT). In the proposed method, the circulant matrix of samples at each time instant from SUs is calculated, and then, the covariance matrix of the circulant matrix is averaged over a short period of time. The eigenvalues of the achieved average circulant matrix (ACM) are used to build our proposed detectors. Using a circulant matrix can improve the dominant eigenvalue of covariance matrix of signals and also the detection performance of EBDs even with short samples. The probability distribution functions of the detectors undernull hypothesis are analyzed, and the asymptotic expressions for the false-alarm and thresholds of two proposed detectors are derived, respectively. The simulation results verify the effectiveness of the proposed detectors.

Smart contract-based secure cooperative spectrum sensing algorithm

Spectrum sensing is the key technology of cognitive radio. In this article, we apply blockchain technology in spectrum sensing process and propose a related algorithm based on reputation. The algorithm builds a system model based on smart contract in blockchain and applies blockchain asymmetric encryption algorithm and digital signature technology in the process of secondary users’ transmitting local judgments to the secondary user base station. The algorithm can resist spectrum sensing data falsification (SSDF) attack launched by malicious users. This article comprehensively considers the channel error rate, detection probability, secondary user base station budget and remaining energy of the secondary users (SUs) and then establishes the SU’s utility function as well as the game model. By solving the Nash equilibrium, the SU determines whether it uploads sensing data. Finally, the SU base station selects registered SUs by calculating and updating their reputation, obtaining the final judgment by voting rule. With simulations, we prove that the algorithm proposed in this article increases the accuracy and security of spectrum sensing and can effectively resist SSDF attack.

Energy-Efficient Secure Communications for Wireless-Powered Cognitive Radio Networks

In this study, we investigate energy-efficient secure communications for wireless-powered cognitive ratio networks, in which multiple secondary users (SUs) share the same frequency band with primary users (PUs) and energy harvesting (EH) nodes harvest energy from the transmitted signals, even though information decoding is not permitted. To maximize the average secrecy energy efficiency (SEE) of SUs while ensuring acceptable interference on PUs and the required amount of energy for the EH nodes, we propose an energy-efficient transmit power control algorithm using dual decomposition, wherein suboptimal transmit powers are determined in an iterative manner with low complexity. Through extensive simulations in various scenarios, we verify that the proposed scheme has a higher average SEE than conventional schemes and a considerably shorter computation time than the optimal scheme.