Noise Power Uncertainty Research Articles

Covert Communications via Two-Way IRS With Noise Power Uncertainty

Covert Communications via Two-Way IRS With Noise Power Uncertainty

A Novel Particle Swarm Optimization for Channel Allocation in OFDM Based Cognitive Radio Networks

It has become increasingly apparent that bandwidth scarcity is an issue as wireless communications advance. Alternatively, spectrum sensing techniques are used to detect licensed users. A spectrum sensor can detect energy, matched filters, and cyclostationary features. There are, however, some drawbacks to these methods. Energy detector performance is affected by noise power uncertainty. Every primary user needs a dedicated receiver for matched filter spectrum sensing. Computational effort and observation time are required for cyclo-stationary feature detection. Spectrum use is determined using particle swarm optimization (PSO), an algorithm for determining the best frequency allocation and highest accuracy. Using PSO operations, this paper proposes an improved energy detection method compared to conventional energy detection methods. Detecting energy and using the PSO channel allocation technique to detect fading channels is also mathematically described.

A Low SNR Signal-Sensing Method Based on a Multiple-Antenna Covariance Matrix

In this paper, we propose a promising signal-sensing method based on both single- and multiple-antenna covariance matrices of the received signal. In comparison with microwave diagnostics or energy detection, the proposed method does not need prior information of signal and noise. Therefore, it can be widely used in noise power uncertainty scenarios, such as signal transmission in hypersonic plasma sheath, with symmetry to the signal-sensing method in cognitive radio. Designing an efficient signal-sensing method that supports both the application for the field of hypersonic plasma sheath communication or cognitive radio is still an attractive subject deserving of study in symmetry journals. Theoretical analysis and the implementation algorithm of a single-antenna covariance matrix are carried out for the proposed method, with a possible extension to multiple-antenna situations. Simulations show the proposed method has excellent performance, even for very low signal-noise-ratio (SNR) signals, and is competitive with state-of-the-art algorithms.

Frequency hopping signal detection based on optimized generalized S transform and ResNet.

The performance of traditional frequency hopping signal detection methods based on time frequency analysis is limited by the tradeoff of time-frequency resolution and spectrum leakage. Machine learning-based frequency hopping signal detection techniques have a high level of complexity. Therefore, this paper proposes a residual network and the optimized generalized S transform to detect frequency hopping signals. First, based on the time-frequency aggregation measure, the generalized S transform parameters $ \lambda $ and $ p $ are optimized using a multi-population genetic algorithm. Second, the optimized generalized S transform is used to determine a signal's time-frequency spectrum, which is then normalized to make this robust to noise power uncertainty. Finally, a residual network structure is designed which receives the time-frequency spectrum. To detect frequency hopping signals, the network automatically learns the time-frequency properties of signals and noise. Simulated findings indicate that the multi-population genetic algorithm not only increases optimization efficiency when compared to a regular genetic algorithm, but also has faster convergence and more stable optimization results. Compared with a hybrid convolutional network/recurrent neural network algorithm, the proposed technique is better at detection and has less computational and storage complexity.

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.

Multifunctional quantum thermal device with initial-state dependence

We study the thermodynamic behaviours of a quantum four-level system in the steady-state regime by employing the master equation and the full counting statistics approach. It is found that the initial state can affect the steady state, thus some specific initial states can enable our system to behave as a perfect insulator irrespective of the working temperatures of the baths. In addition, we demonstrate that our system can work as multifunctional quantum thermal devices: amplifier, switch, and refrigerator. More importantly, one can control the power of these devices by preparing the initial state previously. The result also shows the noise power and thermodynamic uncertainty relation depend on both the initial state and time, and especially they will both diverge in the long-time limit.

Performance of a cognitive device‐to‐device network in disaster situation under a collision constraint

SummaryIn this paper, a multi‐hop cognitive radio (CR) enabled device‐to‐device (D2D) network is considered to extend the communication from a functional area (FA) to a non‐functional area (NFA). A D2D user utilizes a cellular user (CU) band in the cognitive mode for information transmission while maintaining the service quality of CUs by satisfying a collision constraint. It transmits with full power in overlay mode, while the power is controlled in underlay mode. We derive the outage probability of the considered D2D network. The main challenge in CR communications lies in the system robustness to uncertainties. In this paper, we also show the impact of the noise power uncertainty in a multi‐hop D2D network. The expressions for throughput and energy efficiency are also developed under collision constraint and noise uncertainty. The outage probability, throughput, and energy efficiency are investigated for several network parameters such as collision probability, sensing time, noise uncertainty, etc. It is observed that the collision constraint and noise uncertainty have significant impacts on network performance. A simulation test‐bed is developed in MATLAB, and the results obtained in the simulation are verified with the analytical results.

Computational Bayesian inference of range and depth of a mobile scatterer in a refractive ocean waveguide with a limited vertical aperture array

A computational Bayesian method is presented for inference from a limited vertical aperture regarding the range, depth and speed of a submerged large mobile object. Closely spaced multipath arrivals in Doppler and vertical angle off of the target in a refractive environment are addressed in the presence of uncertainty in the ambient acoustic noise power. Vertical angles and Doppler frequencies of the arrival structure are jointly inferred and their posterior density is mapped to the object’s range, depth, and speed through acoustic ray interpolation. A case study with the classic Munk sound speed profile is presented to lend credence to the approach.

Spectrum sensing based on deep learning classification for cognitive radios

Spectrum sensing is a key technology for cognitive radios. We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification. We normalize the received signal power to overcome the effects of noise power uncertainty. We train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new signals. We also use transfer learning strategies to improve the performance for real-world signals. Extensive experiments are conducted to evaluate the performance of this method. The simulation results show that the proposed method performs better than two traditional spectrum sensing methods, i.e., maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based method. In addition, the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals. Furthermore, the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning. Finally, experiments under colored noise show that our proposed method has superior detection performance under colored noise, while the traditional methods have a significant performance degradation, which further validate the superiority of our method.

Detection of Frequency Modulated Signals Using a Robust IF Estimation Algorithm

This paper presents a methodology to detect non-stationary frequency modulated signals under noise process uncertainty. The proposed method uses the combination of adaptive directional time–frequency distribution and modified Viterbi algorithm to robustly estimate the instantaneous frequency (IF) of the given signal. This IF information is then used to remove the frequency modulation from the given signal thus converting it into temporally correlated signal. This temporal correlation is then exploited to detect signals under noise power uncertainty. The effectiveness of the proposed detector is shown through numerical simulations.

A computational Bayesian approach for localizing an acoustic scatterer in a stratified ocean environment.

A computational Bayesian method is presented for inference regarding the state of a submerged mobile object. The approach addresses the challenge of closely spaced multipath arrivals in refractive environments with uncertainty in ambient acoustic noise power. Vertical angles and Doppler frequencies of the arrival returns are jointly inferred and their posterior density is mapped to the object's range, depth, and speed through acoustic ray interpolation. The object is localized under the challenging constraint of a small receive vertical aperture. A case study with the classic Munk sound speed profile is presented to lend credence to the approach.

Effective Energy Detection for IoT Systems Against Noise Uncertainty at Low SNR

This paper deals with spectrum sensing for cognitive radio-based Internet of Things (IoT) systems and their coexistence with Long Term Evolution (LTE) systems. Due to the sparsity of the covariance matrix of IoT/LTE signals, we reveal that the likelihood ratio test approximates to energy detection (ED) at low signal to noise ratio. However, the noise (power) uncertainty can degrade the performance of ED severely, especially when low-cost IoT devices are employed for spectrum sensing. To tackle this issue, we derive the relationship among noise power, total power, and autocorrelation coefficient of received signals, and propose an unbiased estimator of noise power without the knowledge of the presence/absence of IoT/LTE signals. We then design a new ED with multiple estimates of noise power from historical and current sensing data, and analyze its theoretical performance. Numerical results are provided to verify the theoretical results and demonstrate the superior performance of the proposed detector. It is shown that, by exploiting sufficient historical sensing data, the performance of the proposed ED can closely approach that of the ideal ED.

On Oversampling-Based Signal Detection

The availability of inexpensive devices allows nowadays to implement cognitive radio functionalities in large-scale networks such as the internet-of-things and future mobile cellular systems. In this paper, we focus on wideband spectrum sensing in the presence of oversampling, i.e., the sampling frequency of a digital receiver is larger than the signal bandwidth, where signal detection must take into account the front-end impairments of low-cost devices. Based on the noise model of a software-defined radio dongle, we address the problem of robust signal detection in the presence of noise power uncertainty and non-flat noise power spectral density (PSD). In particular, we analyze the receiver operating characteristic of several detectors in the presence of such front-end impairments, to assess the performance attainable in a real-world scenario. We propose new frequency-domain detectors, some of which are proven to outperform previously proposed spectrum sensing techniques such as, e.g., eigenvalue-based tests. The study shows that the best performance is provided by a noise-uncertainty immune energy detector (ED) and, for the colored noise case, by tests that match the PSD of the receiver noise.

Dynamic dual threshold cooperative spectrum sensing for cognitive radio under noise power uncertainty

The spectrum sensing performance depends on the accuracy of the detection about whether primary users are busy or idle. Previous studies on cognitive radio spectrum sensing have shown that the cooperation between secondary users can improve their spectrum detection performance in real cognitive networks. Aiming at the problem of threshold mismatch of energy detectors under noise power uncertainty, a cooperative spectrum sensing method with dynamic dual threshold is proposed. Firstly, the utility function is defined with the objective of minimizing the error probability of spectrum sensing, and the optimum threshold of energy detector is derived. Secondly, in order to mitigate the influence derived from noise uncertainty, an effective dynamic dual threshold adjustment mechanism is presented, and the optimizing combinative fusion rule is discussed with the prerequisite of the minimum global error probability. In addition, in view of insufficient number of cognitive users whose sensing results lie in decision zones, the parameter of credibility is defined to choose the secondary users with reliable local detection for final fusion. Simulation results show that our proposed method can mitigate the influence of noise uncertainty and increase the spectrum sensing accuracy compared with other existing methods.

Entropy and Energy Detection-Based Spectrum Sensing Over $\mathcal{F}$ -Composite Fading Channels

In this paper, we investigate the performance of energy detection-based spectrum sensing over $\mathcal {F}$ composite fading channels. To this end, an analytical expression for the average detection probability is first derived. This expression is then extended to account for collaborative spectrum sensing, square-law selection diversity reception, and noise power uncertainty. The corresponding receiver operating characteristics (ROC) are analyzed for different conditions of the average signal-to-noise ratio (SNR), noise power uncertainty, time-bandwidth product, multipath fading, shadowing, number of diversity branches, and number of collaborating users. It is shown that the energy detection performance is sensitive to the severity of the multipath fading and the amount of shadowing, whereby even small variations in either of these physical phenomena can significantly impact the detection probability. As a figure of merit to evaluate the detection performance, the area under the ROC curve (AUC) is derived and evaluated for different multipath fading and shadowing conditions. Closed-form expressions for the differential entropy and cross entropy are also formulated and assessed for different average SNR, multipath fading, and shadowing conditions. Then, the relationship between the differential entropy of $\mathcal {F}$ composite fading channels and the corresponding ROC/AUC is examined where it is shown that the average number of bits required for encoding a signal becomes small (i.e., low differential entropy) when the detection probability is high or when the AUC is large. The difference between composite fading and traditional small-scale fading is emphasized by comparing the cross entropy for Rayleigh and Nakagami- $m$ fading. A validation of the analytical results is provided through a careful comparison with the results of some simulations.

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.

Localization of a mobile acoustic scatterer from sub-Rayleigh resolved acoustic arrivals in a refractive environment

Computational Bayesian inference on the state of an underwater mobile object from a continuous active acoustic transmission is presented. The challenge of sub-Rayleigh resolvable wave vectors in refractive environments with uncertainty in ambient acoustic noise power is addressed. The location and speed of the mobile scatterer are inferred under the challenging constraint of a small receive vertical aperture. The need for jointly inferring the vertical angles and Doppler offsets of the arrivals is addressed with a Gibbs sampling approach. The posterior density of the plane wave components is mapped to the object's range, depth, and speed through ray interpolation. A case scenario from an acoustic duct environment in the western Indian ocean is presented.

Bayesian fuzzy hypothesis test in wireless sensor networks with noise uncertainty

Reliable event detection is an essential task of wireless sensor networks (WSNs) in which there are different types of uncertainty. In this paper, we consider a decentralized detection problem for a WSN and use fuzzy hypothesis test (FHT) in the Bayesian perspective to model the noise power uncertainty. FHT employs membership functions as hypotheses for modeling and analyzing the uncertainty. Using Bayesian FHT (BFHT), a local detector scheme is proposed at each sensor node in which the threshold depends on the noise power uncertainty bound. Local decisions of sensors are sent to the fusion center (FC) and combined to make a final decision about the absence/presence of the event. The proposed algorithm is evaluated in terms of probabilities of detection and false alarm. Simulations show that the proposed BFHT detector considerably outperforms the Anderson–Darling method as well as the conventional energy detector in the presence of the noise power uncertainty.

Exploiting Temporal Correlation for Detection of Non-stationary Signals Using a De-chirping Method Based on Time–Frequency Analysis

Novel time–frequency (t–f) methods are developed for the detection of non-stationary signals in the presence of noise with uncertain power. The proposed method uses instantaneous frequency estimation and de-chirping procedure to convert a non-stationary signal into a stationary signal, thus allowing us to exploit temporal correlation as an extra feature for signal detection in addition to the signal energy. The proposed method can be used for both mono-sensor and multi-sensor recordings. Area under receiver operating characteristic curve and probability of signal detection are used as criteria for comparing the performance of the proposed signal detection methods with the state of the art in the presence of noise power uncertainty. Simulation results indicate the superiority of the proposed approach.

Leveraging High Order Cumulants for Spectrum Sensing and Power Recognition in Cognitive Radio Networks

Hybrid interweave-underlay spectrum access in cognitive radio networks can explore spectrum opportunities when primary users (PUs) are either active or inactive, which significantly improves spectrum utilization. The practical wireless systems, such as long-term evolution-advanced, usually operate at multiple transmission power levels, leading to a multiple primary transmission power scenario. In such a case, the two fundamental issues in hybrid interweave-underlay spectrum access are to detect the “ON/OFF” status of PUs and to recognize the operating power level of PUs, which are challenging due to non-Gaussian transmitted signals. In this paper, we exploit high-order cumulants (HOCs) to efficiently perform spectrum sensing and power recognition. Specifically, for a given order and time lag, we first propose a single HOC-based spectrum sensing and power recognition scheme with low computational complexity, by leveraging minimum Bayes risk criterion. Moreover, we propose a hybrid multiple HOCs-based spectrum sensing and power recognition scheme with multiple orders and time lags, to further improve the detection performance. Both the proposed schemes can eliminate the adverse impact of the noise power uncertainty. Finally, simulation results are provided to evaluate the proposed schemes.