Presence Of Noise Uncertainty Research Articles

Covariance absolute values spectrum sensing method based on two adaptive thresholds

Cognitive radio is a modern wireless communication methodology that deals with the issue of spectrum untapped. Cognitive users can continually perceive the licensed spectrum to hunt for unoccupied spots. The essential technology in cognitive radio (CR) for primary user detection is spectrum sensing. Due to signal to noise ratio (SNR), noise uncertainty in spectrum sensing would make the detection unreliable. In this paper, the two adaptive thresholds based on_covariance absolute values (TATCAV) are proposed to increase detection performance in the presence of noise uncertainty. According to the computer simulations using MATLAB 2021b, the value of the probability of detection is Pd=98.1% Compared with the results of two thresholds based on_covariance absolute values (TTCAV) Pd= 95.3% at SNR=-18, noise uncertainty Nu=1.761 dB, and using quadrature amplitude modulation (QAM). And the error rate for the proposed approach is Pe=12.1% under the same circumstances. The proposed approach results, according to the simulations, are considerably better than the results of the fixed two-threshold approach.

Enhancing Hybrid Spectrum Access in CR-IoT Networks: Reducing Sensing Time in Low SNR Environments

The current utilization of the licensed spectrum band is not optimal. the abundance of Internet of Things (IoT) gadgets could lead to congestion in the unlicensed spectrum band. A potential solution is to integrate cognitive radios into IoT devices, specifically by developing CR-IoT (cognitive-radio-enabled IoT) devices that leverage the hybrid spectrum access (HSA) technique to access the licensed spectrum band and employ energy detectors for spectrum sensing. While HSA can enable high data throughput for CR-IoT networks, environments with low signal-to-noise ratio (SNR) may experience reduced performance. Particularly, in low SNR environments with SNR values ranging from -20dB to -24dB, the high level of noise uncertainty can cause the energy detector to spend more time sensing, which results in a significant reduction in transmission time and overall throughput. In this study, a novel approach is presented to address the challenge of noise uncertainties on the energy detector by implementing an adaptive sensing threshold. The simulation outcomes reveal that the proposed technique can significantly improve the throughput in scenarios characterized by low signal-to-noise ratio (SNR), with a maximum enhancement of up to 28%. This innovative approach can pave the way for more robust and efficient energy detectors in the presence of noise uncertainties .

Extended kernel Risk-Sensitive loss unscented Kalman filter based robust dynamic state estimation

The traditional unscented Kalman filter (UKF) with mean square error (MSE) criterion for dynamic state estimation (DSE) is sensitive for unknown non-Gaussian noise and outliers. Leading to biased state estimates. This paper proposes a novel robust UKF with extended kernel risk-sensitive loss (EKRSL) for DSE considering unknown non-Gaussian process and measurement noises. Instead of MSE criterion, a novel robust EKRSL via the generalized Gaussian density is defined in KRSL framework, and we further develop a new robust UKF using the EnKRSL(called EKRSL-UKF). To obtain the recursive form of EKRSL-UKF, the statistical linear regression model is used and the fixed-point iteration is further utilized to iteratively get the optimal state estimate. An error constrained method is also introduced to restrict the error to address the numerical instability problem caused by large outliers. Furthermore, an enhanced EKRSL-UKF is established by using an exponential function of innovation to improve the estimation accuracy in the presence of noise uncertainties. Numerical results carried out on the IEEE 39-bus test system demonstrate that the proposed method can achieve desired robustness without loss of estimation accuracy under various conditions.

Spectral minima tracking for improving the energy detection under noise power uncertainty

Spectrum sensing involves identifying the unused license bands in Cognitive Radio Networks. Energy Detection (ED) is the most widely studied form of spectrum sensing. ED detects potentially unknown signals and it is mainly characterized by its low complexity. However, the ED performance is considerably deteriorated by the presence of noise uncertainty (NU). To increase the robustness of ED in NU scenarios, in this work we propose to use an adaptive detection threshold, which is calculated from the noise power estimate obtained at each sensing interval. The noise power is estimated using the technique called Spectral Minima Tracking (SMT). Moreover, a correlation analysis is conducted to quantify the degree of relationship between the parameters of the SMT technique and the resulting noise power estimate. The findings made in the correlation analysis allowed to adjust the parameters of the SMT technique, and thus giving place to the improvement of the quality of the noise power estimate. Simulation results show an improvement in the detection rate of the adaptive ED due to the reduction of the bias in the noise power estimate, as compared to the non-adaptive ED.

Asynchronous cooperative spectrum sensing via sequential detection in fuzzy hypothesis testing under noise uncertainty in cognitive radio networks

Spectrum sensing is a critical part in Cognitive radio networks to detect the available frequency resources, and its timeliness and accuracy are extremely important. The conventional detectors usually fail to be robust in the presence of noise uncertainty, hence an asynchronous cooperative spectrum sensing approach based on sequential probability ratio detection in fuzzy hypothesis testing is proposed. In the approach, sequential detection in fuzzy hypothesis testing is done by each cognitive radio to form local decisions, then fusion centre sequentially accumulates the local decisions of cooperative cognitive radios to make a global decision. Simulation results show that the proposed approach can reduce the average number of samples and overcome noise uncertainty when compared with conventional or selective asynchronous cooperative sensing and non-cooperative sensing.

A Metaheuristic Based Approach for Threshold Optimization for Spectrum Sensing in Cognitive Radio Networks

Background: The mounting growth of wireless technology is attracting a high demand for the frequency spectrum. The measurements of spectrum usage depict that a significant portion of the spectrum lays unoccupied or overcrowded. The main cause of the glitch is the existing inefficient and fixed scheme of spectral allocation. Cognitive radio is one such technology that permits wireless devices to detect the unused frequency band and reconfigure its operating parameters to attain the required quality of service. Objective: To permit the dynamic allocation of the frequency band, spectrum sensing is performed which is an essential function of Cognitive radio and involves detection of an unused spectrum space to set up a communication link. Method: This paper presents a meta-heuristic approach for selection of a decision threshold for energy detection based spectrum sensing. At low SNR and in the presence of noise uncertainty, the performance of energy detection method fails. A novel adaptive double threshold based spectrumsensing method is proposed to avoid such a sensing failure. Further, the metaheuristic approach employs Particle Swarm Optimization (PSO) algorithm to compute an optimal value of the threshold to attain robustness against noise uncertainty at low SNR. Results: The simulation results of the proposed metaheuristic double threshold based spectrum sensing method demonstrate enhanced performance in comparison to the existing methods in terms of reduced error rate and increased detection probability. Some of the existing methods have been analyzed and compared from a survey of recent patents on spectrum sensing methods to support the new findings The concept of adaptive thresholding improves the detection probability by 39 % and 27 % at noise uncertainty of 1.02 and 1.04, respectively at a signal to noise ratio of -10 dB. Furthermore, the error probability reduces to 58% at the optimal threshold using Particle Swarm Optimization (PSO) algorithm for the signal to noise ratio of -9 dB. Conclusions: The main outcome of this work is the reduction in the probability of sensing failure and improvement in the detection probability using adaptive double thresholds at low SNR. Further, particle swarm optimization helps in obtaining the minimum probability of error under noise uncertainty with an optimal threshold.

Double Threshold Weighted Energy Detection for Asynchronous PU Activities in the Presence of Noise Uncertainty

Cognitive Heterogeneous Network (CHN) is a network that provides heterogeneous services on a cognitive radio basis. The existence of such heterogeneous services introduces a syndrome of concurrent problems. Handling each problem separately may result in more consequences. This paper aims to mitigate the combined effects of noise uncertainty and asynchronous primary user occurrence within the sensing interval of the secondary user in cognitive heterogeneous network. The paper proposes a double threshold energy detection method based on unequal scale sampling criteria. An analytic formula is deduced for both thresholds employing the power scaling criteria. Simulation results show a complete agreement with the performance expectations in terms of increased probability of detection while keeping the false alarm probability within the required limits, which is guaranteed by plotting the ROC curves. Moreover, an optimal threshold is formulated to minimize the overall detection errors, that led to further enhancements in performance as illustrated also via simulations.

Cooperative Bayesian‐based detection framework for spectrum sensing in cognitive radio networks

In this study, a cognitive radio network is considered in which multiple secondary users intend to detect a primary user frequency band in order to specify whether it is occupied or not. To this end, a blind Bayesian framework is proposed by which secondary users cooperatively perform spectrum sensing. In practice, it is impossible to estimate the noise variance accurately (noise uncertainty problem) and this can degrade the performance of some previous spectrum sensing algorithms like energy detection (ER). To overcome this issue, unlike the conventional ER, the proposed algorithm utilises marginalisation to eliminate the effect of uncertainty in noise variance estimation. By computer simulations using MATLAB, it can be seen that the authors' algorithm reaches the ideal case for by improving the level of cooperation (increasing the number of secondary users) and yet its is also improved compared to ER in practical situations (presence of noise uncertainty).

Distributed detection with fuzzy censoring sensors in the presence of noise uncertainty

Distributed detection is an essential task of wireless sensor networks (WSNs) with stringent limitations of energy and processing resources. In most of detection scenarios, the optimal threshold depends on the noise power which itself consists of some uncertainty in practice. In this paper, we use fuzzy hypothesis test (FHT) for modeling the noise power uncertainty. Moreover, we propose an optimal censoring scheme by applying the Neyman–Pearson lemma on the FHT. It is proved that the optimal censoring method is obtained by simply comparing the energy of the observed data with a threshold. Interestingly, we show that the threshold would depend on the local communication constraint as well as the noise uncertainty bound. We evaluate the performance of the proposed censoring algorithm by resorting to the equal gain combining (EGC) as the relevant fusion rule, using common receiver operating curves. Simulation results show the effectiveness of the proposed method in counteracting the uncertainty.

SNR wall for generalized energy detector in the presence of noise uncertainty and fading

The performance of energy detection (ED) degrades under low SNR, noise uncertainty (NU) and fading. The generalized energy detector (GED) is obtained by changing the squaring operation in ED by an arbitrary positive number p. In this paper, we investigate the signal to noise ratio (SNR) wall for GED under diversity considering NU as well as fading by considering p-Law combining (pLC) and p−law selection (pLS) diversity. First, the SNR walls considering AWGN channel are derived. It is shown that for pLC diversity, increasing p results in lower SNR wall. It is also shown that under no diversity and pLS diversity, the SNR wall is independent of p. The analysis is then extended to the channel with Nakagami fading where it is shown that the SNR wall increases significantly. As a byproduct of this work, we also study the effect of NU and fading on the detection performance and show that above certain value, the effect of NU is more severe when compared to the fading. The effect of p on the performance is analyzed and it is shown that the performance is the best for values of p close to 2. The performance of pLC and pLS is also compared.

Adaptive Energy Detection Based Solution for Fronthaul Problem in C-RAN

It hasbeen anticipated that the fifth generation (5G) wireless systems will deliver an efficient growth of the energy and spectral efficiency. To accomplish these goals, centralized radio access networks (C-RAN) is presented as the advanced wireless access networks paradigm with centralized baseband units (BBUs) and remote radio heads (RRHs). Although it is affected by Common Public Radio Interface (CPRI) limitations which requires high bandwidth for existing BBU/RRH interfaces. Moreover, the fronthaul links the BBU and RRH. But CPRI limits the deployment of C-RAN for fronthaul networking due to its fixed-rate fronthaul interface which transmit CPRI streams even in the absence of traffic load. However, this causes data transmission inefficient. So, the classical fronthauling method demands high bandwidth and the failure of fronthaul will cause losing a very high bandwidth. The purpose of this paper is to develop energy detection (ED) based algorithm which permits the secondary users to use the channel without interfering the primary users, so that the bandwidth in the fronthaul can be used in an efficient manner. A dynamic threshold with noise uncertainty based ED algorithm is proposed in this paper. It compares the fixed threshold with dynamic threshold by comparing probability of detection with probability of false alarm. Finally, by using this proposed algorithm, it can be concluded that performance of the probability of detection improves even with the presence of noise uncertainty.

Cooperative Energy Detection With Heterogeneous Sensors Under Noise Uncertainty: SNR Wall and Use of Evidence Theory

The analyzed system model in this paper is a distributed parallel detection network in which each secondary user (SU) evaluates the energy-based test statistic from the received observations and sends it to a fusion center (FC), which makes the final decision. Uncertainty in the noise variance at each SU is modeled as an unknown constant in a certain interval around the nominal noise variance. It is assumed that the SUs are heterogeneous in that the nominal noise variances and the uncertainty intervals can be different for different SUs. Moreover, the received signal power at each SU may be different. For the considered system model, the paper presents important results for two inter-related themes on cooperative energy detection (CED) in the presence of noise uncertainty (NU). First, the expressions for generalized signal-to-noise ratio (SNR) walls are derived for the classical CED fusion rule, i.e., sum of energies from all SUs. Second, a Dempster–Shafer theory based CED is proposed in the presence of NU with heterogeneous sensors. In the proposed scheme, the test statistic from each SU is the energy-based basic mass assignment values, which are first discounted depending on the uncertainty level associated with the SU and then fused at the FC using the Dempster rule of combination to arrive at the global decision. It is shown that the proposed scheme outperforms the traditional sum fusion rule in terms of detection performance as well as the location of SNR wall.

A Novel PU Sensing Algorithm for Constant Energy Signals

In this correspondence, a novel spectrum sensing technique is proposed for primary user (PU) signals transmitted with constant energy. A new metric, namely the variance of the received signal energy, is first introduced and then used as the testing variable for detecting the PU signal. The performance of the proposed method is evaluated by means of theoretical analysis and simulations in the presence of noise uncertainty. The results confirm the efficiency of our method for fast and reliable PUs’ detection in cognitive radio systems.

Spectrum sensing based on cumulative power spectral density

This paper presents new spectrum sensing algorithms based on the cumulative power spectral density (CPSD). The proposed detectors examine the CPSD of the received signal to make a decision on the absence/presence of the primary user (PU) signal. Those detectors require the whiteness of the noise in the band of interest. The false alarm and detection probabilities are derived analytically and simulated under Gaussian and Rayleigh fading channels. Our proposed detectors present better performance than the energy (ED) or the cyclostationary detectors (CSD). Moreover, in the presence of noise uncertainty (NU), they are shown to provide more robustness than ED, with less performance loss. In order to neglect the NU, we modified our algorithms to be independent from the noise variance.

Fuzzy Hypothesis Testing for Cooperative Sequential Spectrum Sensing Under Noise Uncertainty

Efficient and reliable spectrum sensing is extremely significant, especially in the presence of noise uncertainty in low SNR environment below which conventional detectors fail to be robust. In this letter, by the development of a sequential probability ratio test for the fuzzy hypothesis testing (FHT), we propose a novel cooperative sequential detector to deal with the effect of noise power uncertainty. In this approach, for every measurement, FHT is computed by each cognitive radio. Subsequently, fusion center sequentially accumulates these fuzzy test statistics and decides about the sensing time. Simulation results are illustrated to show the effectiveness and robustness of the proposed sequential FHT detector. The significant reduction in sample complexity is demonstrated for our scheme in comparison with energy detector, sequential crisp hypothesis testing detector, and fixed sample size FHT detector.

Scaled Largest Eigenvalue Detection for Stationary Time-Series

This paper studies the performance of the scaled largest eigenvalue (SLE) detector used for the detection of stationary time-series. We focus on a single-antenna setup and a blind detection scenario (neither the signal covariance, nor the noise variance are known). The SLE detector has received much attention in the context of cognitive radios (CR) due to its simplicity, good performance and robustness to noise level uncertainties. Specifically, our goal is to analyze the detector based on the statistic $\Gamma={\lambda_{1}\over\sum_{i=1}^{p}\lambda_{i}}$ , where $\lambda_{1}\geq\lambda_{2}\geq\ldots\geq\lambda_{p}$ represent the ordered eigenvalues of the sample covariance matrix. We derive a large-sample-size closed-form approximation for the test statistic which allows us to derive its statistical distribution and set up the detector to achieve the required probability of false-alarm $(P_{fa})$ and probability of detection $(P_{d})$ . We also study the robustness of the detector in the presence of noise uncertainty and impulsive-noise and investigate the benefits of the spatial sign filter for such scenarios.

Subband Energy Based Reduced Complexity Spectrum Sensing Under Noise Uncertainty and Frequency-Selective Spectral Characteristics

The present work proposes a subband energy detection method that performs efficiently under noise uncertainty (NU) and frequency-selective channels. The critical impact of detrimental modeling uncertainties, such as NU, is analytically quantified and it is shown that the introduced method is robust to both NU and frequency-selectivity conditions. This is also the case for eigenvalue based sensing techniques, in contrast to traditional energy detector based sensing. Connections of the subband energy based approach and existing eigenvalue based methods are established analytically, which leads to a novel reduced complexity processing technique based on the difference between maximum and minimum subband energies. The proposed method is capable of providing accurate and robust performance with low signal-to-noise ratios (SNR) in the presence of NU. Closed-form expressions are derived for the corresponding probability of false alarm and probability of detection under frequency selectivity due to the primary signal spectrum and/or the transmission channel. The validity of the offered expressions is justified through comparisons with respective results from computer simulations. The sensing performance is evaluated in different communication scenarios, with different frequency-selective channel models and primary user waveforms. The offered results indicate that the proposed methods provide quite significant savings in complexity, e.g., 78% reduction in the considered example case, while also improving the detection performance at low SNRs and in the presence of NU.

Sparse Frequency Domain Spectrum Sensing and Sharing based on Cyclic Prefix Autocorrelation

Cognitive radio (CR) is considered an important solution to the current spectral scarcity, which is expected to be a significant issue in the next generation of wireless communication systems, namely, 5G. Wideband spectrum sharing and sensing constitute highly desirable features of CR systems as they aim to increase the probability of identifying available spectral bands, which ensures a more efficient resource utilization. This paper proposes an efficient frequency-domain cyclic prefix autocorrelation-based wideband spectrum sensing and sharing method that can provide accurate detection of orthogonal frequency-division multiplexing (OFDM)-based primaries in wideband CR systems. Novel analytic expressions are derived for the corresponding threshold, probability of false alarm, and probability of detection in the presence of noise uncertainty (NU) and frequency selectivity. The derived models are validated by extensive comparisons with respective results from computer simulations. It is demonstrated that the introduced autocorrelation-based sensing method is able to counteract NU and the frequency-selective multipath channel effects in realistic wideband communication scenarios. Furthermore, the method facilitates partial band sensing, allowing the sensing of weak OFDM-type primary user (PU) signals in channels, which are partly overlapped by other strong PU or CR transmissions. This is considered a crucial element in practical spectrum sharing scenarios. Since the proposed sensing method makes use of sparsity in the spectral domain, it can be technically considered as compressed sensing method. The flexibility of this approach supports robust wideband multi-mode, multi-channel sensing with low complexity. Finally, it is shown that the offered results are particularly useful in the context of spectrum sharing as their high performance and reduced complexity can enable the co-existence of non-exhaustive yet highly efficient algorithms.

Fast Sequency-Ordered Complex Hadamard Transform based Parzen Window Entropy detection for Spectrum Sensing in Cognitive Radio Networks

Spectrum sensing is accomplished at the physical layer of cognitive radio networks. This letter presents a fast sequency-ordered complex Hadamard transform (FSCHT)-based Parzen window entropy detection technique (PWED) for spectrum sensing. The energy compaction property of FSCHT leads to a discriminating sensing performance compared to fast Fourier transform (FFT) transform. In PWED, the kernel-based probability density estimation is employed to evaluate the entropy. The impact of orthogonal transforms on the computation of entropy is analyzed. The computational complexity of PWED technique is compared with Shannon entropy technique. A substantial improvement in the SNR wall is observed in the presence of noise uncertainty. The proposed technique detects the DVB-T signal up to $-54\;\text{dB}$ SNR with probability of detection ( $P_{d}$ ) 0.9 and probability of false alarm ( $P_{fa}$ ) 0.1.

Analysis of a class of adaptive robustified predictors in the presence of noise uncertainty

Analysis of a class of adaptive robustified predictors in the presence of noise uncertainty