Efficient Centralized Cooperative Spectrum Sensing Techniques for Cognitive Networks

Adopting cognitive radios (CRs) having multiple antennas in blind non-cooperative and cooperative spectrum sensing (CSS) under fading channels has gained attention due to higher detection performances provided by the spatial diversity gain of multi-sensors in different geographical locations and lower complexity, respectively. However, most studies do not consider sensing scenarios of more practical significance: for example, sometimes adopting only uncalibrated antenna arrays and sometimes only correlated signals at antenna arrays of CRs, but almost always, none of these impairments. Therefore, this paper studies these combined impairments on the performances of two blind techniques in centralized CSS with decision fusion (DF) and data fusion for different numbers of CRs and antennas per CR, both under frequency selective fading channels. One is the circular folding cooperative power spectral density split cancellation (CFCPSC), and the other is the generalized likelihood ratio test (GLRT). Extensive numerical results show, for instance, that with sample fusion (SF) and calibrated antennas, GLRT outperforms CFCPSC independently of correlation, numbers of antennas, or CRs. However, uncalibrated antennas severely penalize GLRT while surprisingly benefiting CFCPSC. Correlation is detrimental to GLRT and CFCPSC with SF but may help CFCPSC in DF and GLRT in DF or eigenvalue fusion. Generally, CFCPSC outperforms GLRT.

Cognitive radio employs an opportunistic spectrum access approach to ensure efficient utilization of the available spectrum by secondary users (SUs). To allow SUs to access the spectrum opportunistically, the spectrum sensing process must be fast and accurate to avoid possible interference with the primary users. Previously, two-stage spectrum sensing methods were proposed that consider the sensing time and sensing accuracy parameters independently at the cost of a non-optimal spectrum sensing performance. To resolve this non-optimality issue, we consider both parameters in the design of our spectrum sensing scheme. In our scheme, we first derive optimal thresholds using an optimization equation with an objective function of maximizing the probability of detection, subject to the minimal probability of error. We then minimize the average spectrum sensing time using signal-to-noise ratio estimation. Our simulation results show that the proposed improved two-stage spectrum sensing (ITSS) scheme provides a 4%, 7%, and 6% better probability of detection accuracy rate than two-stage combinations of energy detection (ED) and maximum eigenvalue detection, energy detection and cyclostationary feature detection (CFD), and ED and combination of maximum-minimum eigenvalue (CMME) detection, respectively. The ITSS is superior also to single-stage ED by 19% and shows an improved average spectrum sensing time.

In this paper, we address the problem of multiantenna spectrum sensing in Cognitive Radios (CRs) by considering the correlation between the received channels at different antennas. First, we derive the optimum genie-aided detector which assumes perfect knowledge of the antenna correlation coefficients, Primary User (PU) signal power and noise variance. This is used as a benchmark for comparing with more practical detectors when some or all of these parameters are unknown to the Secondary User (SU). Two scenarios are considered: 1) the antenna correlation coefficients and PU signal power are unknown to the SU; 2) the antenna correlation coefficients, PU signal power and noise variance are unknown to the SU. To derive sub-optimum detectors for these two scenarios, we apply the Rao test, an asymptotically equivalent test to the Generalized Likelihood Ratio Test (GLRT) that does not require the Maximum Likelihood (ML) estimates of unknown parameters. Additionally, we calculate analytical approximations to the detection and false-alarm probabilities of the proposed detectors and verify them with Monte-Carlo simulations. The simulation results show that these new detectors outperform several recently proposed detectors for CR using multiple antennas.

Cognitive radios can utilise the idle spectrum holes that are not occupied by the Primary Users (PUs) (i.e. licensed users) for temporary wireless communication tasks. A challenging issue is to efficiently reach global/regional views among the Secondary Users (SUs) on channel availability. The SUs need to exchange control messages on their spectrum sensing results. A traditional way is to send out 'raw' channel information from each individual node. It consumes much wireless communication bandwidth. This research proposes a new cognitive radio spectrum sensing scheme based on our proposed Spatially-Decaying, Time-Incremental (SDTI) updating algorithm. Our SDTI algorithm is based on the extension of Gossiping Updates for Efficient Spectrum Sensing (GUESS) scheme that adopts Flajolet-Martin (FM) aggregation to reduce data amount. Our SDTI automatically assigns weights to channel information based on the distance between a source node and an observing node. Further, distance nodes have less important information to the observing node.

Fifth-generation (5G) network demands of higher data rate, massive user connectivity and large spectrum can be achieve using Sparse Code Multiple Access (SCMA) scheme. The integration of cognitive feature spectrum sensing with SCMA can enhance the spectrum efficiency in a heavily dense 5G wireless network. In this paper, we have investigated the primary user detection performance using SCMA in Centralized Cooperative Spectrum Sensing (CCSS). The developed model can support massive user connectivity, lower latency and higher spectrum utilization for future 5G networks. The simulation study is performed for AWGN and Rayleigh fading channel. Log-MPA iterative receiver based Log-Likelihood Ratio (LLR) soft test statistic is passed to Fusion Center (FC). The Wald-hypothesis test is used at FC to finalize the PU decision.

In this paper, we review some of the recent patents on spectrum sensing techniques for cognitive radio (CR) applications. The presented patents are categorized under: 1) spectrum sensing algorithms and architectures, 2) spectrum sensing and data communication and 3) cooperative spectrum sensing. The presented patents include special sensing algorithms that are based on the multi-resolution spectrum sensing (MRSS) approach, which ensures reliability of the spectrum sensing in wireless RF environments. We also discuss a CR patent that includes multiple RF chains to perform simultaneous cognitive tasks. Several patents that consider cooperative sensing are also presented as a solution to the hidden terminal problem inherent in wireless networks. We review the advantages and disadvantages of the presented patents and propose future directions for developing spectrum sensing techniques for CR’s. Keywords: Cognitive radio, cooperative spectrum sensing, dynamic spectrum access, energy detection, multi-resolution spectrum sensing, opportunistic spectrum access, spectrum sensing

In this paper, we propose a new blind spectrum sensing method based on the polarization characteristic of the received signal, which is completely represented by the orientation of a polarization vector. We first discuss a spectrum sensing model based on polarization vectors' orientation. Then we develop the directional statistics of polarization vectors that contain both the signal and noise or noise only. The distinctive difference between the two statistics can be used to decide whether the primary signal exists or not. Based on this, by using the well-known generalized likelihood ratio test (GLRT) paradigm, a new polarization sensing algorithm GLRT-polarization vector (GLRT-PV) is proposed. By applying directional statistics, we derive closed-form expressions for the probability of false alarm and the probability of detection under both dual-polarized additive white Gaussian noise (AWGN) and Rayleigh-fading channels. Our numerical simulation and experimental results show that the proposed method exhibits better performance than other existing methods in the case of unknown primary transmitter polarization and/or presence of noise power uncertainty.

One prominent feature of cognitive radio (CR) involves spectrum sensing (SS), which allows licensed primary users to remain unaffected by secondary users’ ability to discover and exploit unoccupied frequency bands. Spectrum sensing enhances the use of spectrum in CR devices, increasing their adaptability and efficiency in wireless communication systems. The rise of wireless equipment and the advent of IoT technologies compound this need for flexibility. Over time, the fixed allocation of frequencies has led to inefficiencies and underutilization as bandwidth needs increase. Deep learning and artificial intelligence have improved spectrum sensing by increasing detection probability of primary users’ presence under noisy environments, enabling cognitive radio systems to respond intelligently to fluctuations in RF environments. This article is concerned with deep learning techniques for spectrum sensing and modulation categorization with CBRT structure, which combines convolutional neural networks (CNNs), bidirectional recurrent neural networks (BRNNs), and transformer networks (TNs) to improve spectrum sensing. CNNs are responsible for performing spectrum feature extraction; BRNNs are used to capture temporal dependencies; and TNs are good at long range dependencies. Better performance for this model is aimed by integrating the three architectures described. In the proposed work, six digital modulation schemes were considered, for spectrum sensing. The sensing of spectrum in this model is performed using the RadioML2016.10B open-source dataset and performance metrics like the Jaccard Index (JI), Fowlkes’s Mallows Index, and F1 Score. Modulation classification has been performed using MIGOU-MOD open-source dataset. The proposed model exhibits good detection probability and low sensing error, unlike other methods at lower SNR.

A ground-breaking technology called Cognitive Radio Network (CRN) was developed to increase the effectiveness of spectrum usage. If the primary user is not using it, it gives cognitive radios the ability to transmit on the licensed portions of the spectrum. But, when the primary user decides to reclaim the spectrum, the cognitive radio must give it up. A cognitive radio contributes to more effective radio spectrum use by utilizing the untapped area of the spectrum. Spectrum sensing is the procedure used to determine the spectrum's condition. This work focuses on spectrum sensing by cognitive radios in a 5G communication network. Different ML algorithms were used for spectrum sensing and their performance was evaluated under different channel conditions such as AWGN high accuracy rates of spectrum sensing were achieved. It was observed that the logistic regression algorithm outperforms other classification algorithms at different Signal-to-Noise ratio (SNR) values for different observation window lengths. This implies that the system is able to work under different signal conditions.

ABSTRACTIn Cognitive Radio (CR), effective spectrum utilization is regarded as vital to enhance the spectrum efficacy and to accommodate the need for wireless communication services. Spectrum sensing is more essential in CR networks for permitting spectrum prospects without any harmfulness to Primary Users (PUs). However, existing spectrum sensing approaches depend on energy detection, which leads to various disadvantages, like noise sensitivity, ambiguity in detecting weak signals, and fluctuation in background noises. Hence, this paper introduces a new technique termed Harmonic Elk Herd Optimization (HEHO)‐PyramidNet+ Kernel Least Mean Square (HEHO‐PyramidNet+KLMS) for spectrum sensing in CR networks. First, the signal is collected from the simulated CR system network. Next, the cyclic spectrum is extracted, then the spectrum sensing is carried out by Kernel Least Mean Square (KLMS) filter. On the other hand, the extracted cyclic spectrum is subjected to spectrum sensing, which is performed using PyramidNet. Here, the PyramidNet is tuned using Harmonic Elk Herd Optimizer (HEHO). Afterwards, the attained spectrum sensing outcomes are integrated using the Average Fusion approach. The HEHO‐PyramidNet + KLMS measured a maximum probability of detection, throughput, and energy efficiency of 0.919, 91.77 Mbps, and 94.88 bits/J, and a minimum probability of false alarm of 0.089 and a detection time of 21.54 ms.

As it is not suffered by the well-known noise uncertainty problem of energy detection (ED), generalized likelihood ratio test (GLRT) based spectrum sensing schemes for a single secondary user node have recently been extensively addressed. In this paper, we extend the existing research to an optimal GLRT based cooperative spectrum sensing scheme in a heterogeneous network scenario, where each node can be equipped with different number of antennas and have different signal processing capacity, such as sampling rate. By considering the discrepancy of different secondary user nodes' sensing reliability due to different hardware configuration, we demonstrate that the optimal cooperative spectrum sensing scheme based on GLRT is a linear combination of each node's local test statistic. Moreover, the local test statistic is the logarithm of the arithmetic-to-geometric mean ratio of the eigenvalues associated with the sample covariance matrix. Furthermore, the optimal combining coefficient is a simple function of the numbers of the antennas and the received signal samples at each secondary user node, which can be intrinsically provided by each secondary user node without any prior information. Numerical results show that the proposed GLRT based cooperative spectrum sensing scheme performs better than the practical ED based scheme, i.e., equal gain combination based ED, especially under the case where the sensing reliability discrepancy of each secondary user node is significant.

Spectrum Sensing (SS) is expected to play a crucial role in forthcoming 6G Cognitive Radio Networks (CRNs), where unlicensed users will be able to dynamically access the spectrum and perform opportunistic transmissions without generating interference for licensed users. In this work, we investigate multiple-antenna SS techniques by analyzing the performance of several widely used detection schemes-namely, Roy's Largest Root Test (RLRT), the Generalized Likelihood Ratio Test (GLRT), the Eigenvalue Ratio Detector (ERD), and the Energy Detector (ED)-under varying false-alarm probabilities and signal-to-noise ratios (SNRs). We assume there are a fixed number of sensors at the secondary-user receiver, namely, four. To evaluate the behavior of these detectors in realistic conditions, we developed a software-defined radio (SDR) testbed using Universal Software Radio Peripherals (USRPs), enabling both primary-user signal transmission and secondary-user data acquisition. The experimental results, illustrated through Receiver Operating Characteristic (ROC) and performance curves, are compared with simulation outcomes. The analysis is complemented by a detailed state-of-the-art listing of the available analytical characterizations of the false-alarm probabilities for the considered SS schemes. In particular, the GLRT false-alarm probability, previously unavailable in explicit form for a four-antenna equipped receiver, is computed as well. These results validate the superior detection capability of RLRT over the other schemes tested, confirming its effectiveness not only in theoretical analysis but also in practical SDR-based implementations.

We consider the problem of detecting a primary user in a cognitive radio network by employing multiple antennas at the cognitive receiver. In vehicular applications, cognitive radios typically transit regions with differing densities of primary users. Therefore, speed of detection is key, so detection based on a small number of samples is particularly advantageous for vehicular applications. Without assuming any prior knowledge of the primary user's signaling scheme, the channels between the primary user and the cognitive user, and the variance of the noise seen at the cognitive user, a generalized likelihood ratio test (GLRT) is developed to detect the presence/absence of the primary user. Asymptotic performance analysis for the proposed GLRT is also presented. Performance comparison between the proposed GLRT and other existing methods such as the energy detector and several eigenvalue-based methods under the condition of unknown or inaccurately known noise variance is provided. Our results show that the proposed GLRT exhibits better performance than other existing techniques, especially when the number of samples is small, which is particularly critical in vehicular applications.

Cooperative Spectrum Sensing (CSS) is a reliable detection approach that performs better over faded channel. In CSS, all Secondary Users (SU) have equal weight. We have studied performance of Centralized CSS (CCSS) using Normal Factor Graph (NFG) model with logical OR and Neyman-Pearson based Likelihood Ratio Testing (LRT) fusion rules. Here, we have proposed a novel Weighted CCSS (WCCSS) using NFG based probabilistic inference model. Sum Product algorithm (SPA) has been used for message passing and different weight assignment strategies have been used. The performance of WCCSS approach in Cognitive Radio Network (CRN) under different channels have been studied through simulation.

This paper investigates the design issues of spectrum sensing in the cognitive radio (CR) networks of opportunistic unlicensed spectrum access. The cognitive radios can perform a communication using the incumbent user spectrum band without the interference caused by the cognitive radio users. In this case, the cognitive radios must know the real-time radio environments of the incumbent user spectrum band using the spectrum sensing, beacon signal, and geo-location database access. Then in this paper, we are going to provide spectrum sensing issues which include the sensing techniques, the regulatory requirements, the analysis of DTV detection threshold, and main considerations associated with the spectrum sensing design in cognitive radio systems. Also, this paper introduces design trade-offs in order to optimize the sensing parameters such as sensing time and sensing complexity.