Mixed-signal Research Articles

Recursive signal denoising method for predictive maintenance of equipment by using deep learning based temporal masking

In the data driven predictive maintenance, high quality data is the premise of high accuracy diagnosis and prediction. In the industrial practice, reducing the noise is of great significance to ensure data quality. This paper proposes a recursive denoising method for manufacturing equipment signals in the data driven predictive maintenance. First, in signal decomposition method, equipment mixed signal is decomposed by temporal masking with dilated convolution neural network to generate a noise mask, which realizes signal decomposition of using recursive operation of temporal masking model. Second, in signal components recognition method, signal component features similarities are calculated, which act on the parameter regulation of signal recognition meta-learning model. The experimental results demonstrated that the proposed method effectively solves the noise reduction problem of the equipment signal. Further engineering tests of a chemical winding machine vibration signal decomposition and recognition show that the proposed method has strong adaptive performance for noise reduction.

Signal detection and material identification method for loose particles inside aerospace relays based on overlapping signals

Loose particle is an important factor affecting the reliability of aerospace relays. Particle Impact Noise Detection method is a commonly used loose particle detection method, but it can generate interference signals. Accurately identifying loose particle signals and component signals becomes the key to accurately detecting loose particles. Meanwhile, deeply exploring the properties of loose particle signals and further provide material information can provide guidance for manufacturing processes. However, the existing loose particle signal and component signal identification research, as well as the loose particle material identification research, has problems such as limited research objects to pure signals, independent identification results, and failure to refer to the detection requirements in real application scenarios. It is difficult to apply and its practicality is not high. Based on this, the authors proposed a signal detection and material identification method for loose particles based on overlapping signals. Specifically, referring to the latest loose particle detection information, aerospace relay samples were made, and the component identification model and material identification model based on parameter-optimized random forest were trained, respectively. They can achieve good classification effects on data sets created from pure loose particle signals, pure component signals, and pure loose particle signals corresponding to different materials, respectively. On this basis, confidence coefficient was proposed to quantify the degradation degree of the classification effects of the two models on the data set created from overlapping signals, thus the component confidence coefficient and the loose particle confidence coefficient were obtained, respectively. They can be used to determine valid pulses from pure loose particle signals, pure component signals, and mixed signals in overlapping signals, completing loose particle detection. Valid pulses from pure loose particle signals were screened for material identification. In this way, for the aerospace relay to be tested, first, the loose particle detection results can be obtained by a comprehensive judgment of the component identification model, component confidence coefficient, and loose particle confidence coefficient. Second, the material identification results can be obtained by combining the material identification model with the majority voting processing. In addition, the definition of identification accuracy applicable to the loose particle detection was proposed to meet the engineering application requirements in real application scenarios. Multiple experiments verified the feasibility, practicality, and stability of the proposed method.

Blind Polarization Demultiplexing of Shaped QAM Signals Assisted by Temporal Correlations.

While probabilistic constellation shaping (PCS) enables rate and reach adaption with finer granularity [1], it imposes signal processing challenges at the receiver. Since the distribution of PCS-quadrature amplitude modulation (QAM) signals tends to be Gaussian, conventional blind polarization demultiplexing algorithms are not suitable for them [2]. It is known that independently and identically distributed (iid) Gaussian signals, when mixed, cannot be recovered/separated from their mixture. For PCS-QAM signals, there are algorithms such as [3], [4] which are designed by extending conventional blind algorithms used for uniform QAM signals. In these algorithms, an initialization point is obtained by processing only a part of the mixed signal, which have non-Gaussian statistics. In this paper, we propose an alternative method wherein we add temporal correlations at the transmitter, which are subsequently exploited at the receiver in order to separate the polarizations. We will refer to the proposed method as frequency domain (FD) joint diagonalization (JD) probability aware-multi modulus algorithm (pr-MMA), and it is suited to channels with moderate polarization mode dispersion (PMD) effects. Furthermore, we extend our previously proposed JD-MMA [5] by replacing the standard MMA with a pr-MMA, improving its performance. Both FDJD-pr-MMA and JD-pr-MMA are evaluated for a diverse range of PCS (entropy ) over a first-order PMD channel that is simulated in a proof-of-concept setup. A MMA initialized with a memoryless constant modulus algorithm (CMA) is used as a benchmark. We show that at a differential group delay (DGD) of 10% of symbol period and 18 dB SNR/pol., JD-pr-MMA successfully demultiplexes the PCS signals, while CMA-MMA fails drastically. Furthermore, we demonstrate that the newly proposed FDJD-pr-MMA is robust against moderate PMD effects by evaluating it over a DGD of up to 40% of . Our results show that the proposed FDJD-pr-MMA successfully equalizes PMD channels with a DGD up to 20% of .

A new underwater acoustic signal denoising method based on modified uniform phase empirical mode decomposition, hierarchical amplitude-aware permutation entropy, and optimized improved wavelet threshold denoising

Underwater acoustic signal (UAS) denoising is base and prerequisite for UAS detection, recognition and classification. In order to perform UAS denoising effectively, a new UAS denoising method based on modified uniform phase empirical mode decomposition (MUPEMD), hierarchical amplitude-aware permutation entropy (HAAPE), and improved wavelet threshold denoising (IWTD) method optimized by sand cat swarm optimization (SCSO) (SIWTD), named MUPEMD-HAAPE-SIWTD, is proposed. Firstly, decompose original signal into a battery of intrinsic mode functions (IMFs) by MUPEMD. Secondly, determine the double threshold by HAAPE to classify signal into pure signal, mixed signal and noisy signal. Then, optimize IWTD by SCSO, so that it can adaptively select the optimal wavelet basis function to achieve the denoising of mixed signal. Finally, the denoised mixed signal is reconstructed with pure signal to obtain ultima denoised signal. The denoising experiments on Lorenz signal and Duffing signal demonstrate that signal-to-noise ratio can be improved by 9 dB–13 dB by the proposed method. The denoising experiments on simulating ship radiated noise signals and four kinds of actual ship radiated noise signals (https://www.aigei.com/, accessed on June 1, 2023) demonstrate that the proposed method makes phase diagram smoother and clearer, and makes the ability of suppressing noise higher.

Comprehensive Separation Algorithm for Single-Channel Signals Based on Symplectic Geometry Mode Decomposition.

This paper aims to explore the difficulty of obtaining source signals from complex mixed signals and the issue that the FastICA algorithm cannot directly decompose the received single-channel mixed signals and distort the signal separation in low signal-to-noise environments. Thus, in this work, a comprehensive single-channel mixed signal separation algorithm was proposed based on the combination of Symplectic Geometry Mode Decomposition (SGMD) and the FastICA algorithm. First, SGMD-FastICA uses SGMD to decompose single-channel mixed signals, and then it uses the Pearson correlation coefficient to select the Symplectic Geometry Components that exhibit higher correlation coefficients with the mixed signals. Then, these components are expanded with the single-channel mixed signals into virtual multi-channel signals and input into the FastICA algorithm. The simulation results show that the SGMD algorithm could eliminate noise interference while keeping the raw time series unchanged, which is achievable through symplectic geometry similarity transformation during the decomposition of mixed signals. Comparative experiment results also show that compared with the EMD-FastICA and VMD-FastICA, the SGMD-FastICA algorithm has the best separation effect for single-channel mixed signals. The SGMD-FastICA algorithm represents an improved solution that addresses the limitations of the FastICA algorithm, enabling the direct separation of single-channel mixed signals, while also addressing the challenge of proper signal separation in noisy environments.

Geochemical Characteristics of Deep-Sea Sediments in Different Pacific Ocean Regions: Insights from Fractal Modeling

Exploration of mineral resources in the deep sea has become an international trend. However, deep-sea mineral exploration faces challenges such as complex offshore drilling and the weak and mixed signals of ore deposits. Therefore, studying methods for identifying weak and mixed anomalies and extracting composite information in the deep sea is crucial for innovative prediction and evaluation of deep-sea mineral resources. In this study, the Central Pacific Ocean, Northwestern Pacific Ocean, and Eastern Pacific Ocean were selected as research areas. Drawing upon the fractal self-similarity exhibited by rare earth minerals in the deep-sea sediments within the Pacific Ocean, we conducted an analysis and comparison of the fractal geochemical characteristics in various regions of the Pacific Ocean’s deep-sea sediments. Thereafter, we studied the spatial distribution of rare earth elements (REEs) in deep-sea sediments in these regions to explore the mechanisms responsible for rare earth enrichment in the Pacific Ocean. The results revealed that the geochemical fractal characteristics of deep-sea sediments in the Northwestern Pacific Ocean Basin and the Central Pacific Ocean Basin were similar, whereas there were slight differences in the fractal characteristics observed in the Eastern Pacific Ocean Basin. By calculating the singularity index of CaO/P2O5, it was found that the singularity index in the Central and Northwestern Pacific Ocean basins was lower than that in the Eastern Pacific Ocean Basin, suggesting that the phosphorus content in the Eastern Pacific Ocean Basin was lower than that in the Central and Northwestern Pacific Ocean basins. In the Eastern Pacific Ocean, we found that phosphorus content in deep-sea sediments was the primary controlling factor for REE enrichment. Conversely, in the Central and Northwestern Pacific Ocean, both the phosphorus and calcium content in deep-sea sediments played significant roles in REE enrichment.

Open-source design of integrated circuits

This paper presents the design of a self-clocked 12-bit non-binary fully differential SAR-ADC using the SKY130 open-source PDK. The entire mixed-signal circuit design and layout were created with free and open-source software. The ADC reaches a sample rate of up to 1.44 MS/s at 1.8 V supply while consuming 703 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}W of power on a small 0.175 mm2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${}^{2}$$\\end{document} area. A configurable decimation filter can increase the ADC resolution up to 16 bits while using an oversampling factor of 256. A 9‑bit thermometer-coded and 3‑bit binary-coded DAC matrix using a 448 aF waffle-capacitor results in a total capacitance of 1.83 pF per input. Realizations of configurable analog functions using the form factor of SKY130 high-density standard cells allow the parametrization of an analog circuit in a hardware description language and hardening of the macro in an intentionally digital workflow.

A system-on-chip microwave photonic processor solves dynamic RF interference in real time with picosecond latency

Radio-frequency interference is a growing concern as wireless technology advances, with potentially life-threatening consequences like interference between radar altimeters and 5 G cellular networks. Mobile transceivers mix signals with varying ratios over time, posing challenges for conventional digital signal processing (DSP) due to its high latency. These challenges will worsen as future wireless technologies adopt higher carrier frequencies and data rates. However, conventional DSPs, already on the brink of their clock frequency limit, are expected to offer only marginal speed advancements. This paper introduces a photonic processor to address dynamic interference through blind source separation (BSS). Our system-on-chip processor employs a fully integrated photonic signal pathway in the analogue domain, enabling rapid demixing of received mixtures and recovering the signal-of-interest in under 15 picoseconds. This reduction in latency surpasses electronic counterparts by more than three orders of magnitude. To complement the photonic processor, electronic peripherals based on field-programmable gate array (FPGA) assess the effectiveness of demixing and continuously update demixing weights at a rate of up to 305 Hz. This compact setup features precise dithering weight control, impedance-controlled circuit board and optical fibre packaging, suitable for handheld and mobile scenarios. We experimentally demonstrate the processor’s ability to suppress transmission errors and maintain signal-to-noise ratios in two scenarios, radar altimeters and mobile communications. This work pioneers the real-time adaptability of integrated silicon photonics, enabling online learning and weight adjustments, and showcasing practical operational applications for photonic processing.

An intelligent dual-sensing e-skin system for pressure and temperature detection using laser-induced graphene and polydimethylsiloxane

Motivated by artificial intelligence, we present a novel electronic skin (e-skin) system capable of dual-sensing pressure and temperature signals. Our approach utilizes laser-induced graphene and polydimethylsiloxane, offering a simple yet efficient method for e-skin preparation. Experimental results reveal exceptional performance with good pressure sensitivity (0.037 kPa−1 at 0–50 kPa), a wide detection range (0–220 kPa), a fast response time of 56 ms, an ultra-low detection limit (30 Pa), and excellent stability (8000 cycles). Additionally, the e-skin exhibits positive temperature coefficients (0.0025 ℃-1) within 20–100 ℃, a rapid response time of 2.57 s, an extremely low detection limit (1 ℃), and stability after 50 cycles. Crucially, our intelligent e-skin system, employing a Long Short-Term Memory algorithm, enables real-time multi-modal tactile perception, accurately separating mixed pressure and temperature signals. This versatile technology holds immense potential for applications in intelligent robotics and human health monitoring.

Investigation of multi-input multi-output sine on random mixed vibration control

This article investigates Multi-Input Multi-Output (MIMO) mixed vibration control testing for Sinusoid on Random (SoR). The challenge of this vibration test is the mixed signal separation. The response signals at the control channels are composed of the broadband random component and the sinusoidal components. Firstly, the extracted accuracy of the Improved Correlation Integral Method (ICIM) is analyzed. The influences of the parameters used in ICIM are discussed in detail. Secondly, a new method, named Variance Minimum Method (VMM), is proposed for signal separation. Extraction accuracies of the ICIM and the proposed VMM are compared under different parameters. Finally, the control methods are proposed to iteratively update the driving signals of each component in the light of the deviations between the response signals and specified references. A cantilever beam example and biaxial vibration experiment manifest that the VMM and control method can be applied for the multi-shaker SoR vibration control tests.

CW_ICA: an efficient dimensionality determination method for independent component analysis.

Independent component analysis (ICA) is a widely used blind source separation method for signal pre-processing. The determination of the number of independent components (ICs) is crucial for achieving optimal performance, as an incorrect choice can result in either under-decomposition or over-decomposition. In this study, we propose a robust method to automatically determine the optimal number of ICs, named the column-wise independent component analysis (CW_ICA). CW_ICA divides the mixed signals into two blocks and applies ICA separately to each block. A quantitative measure, derived from the rank-based correlation matrix computed from the ICs of the two blocks, is utilized to determine the optimal number of ICs. The proposed method is validated and compared with the existing determination methods using simulation and scalp EEG data. The results demonstrate that CW_ICA is a reliable and robust approach for determining the optimal number of ICs. It offers computational efficiency and can be seamlessly integrated with different ICA methods.

Design and implementation of an analog and mixed-signal system on chip for ECG monitoring

Design and implementation of an analog and mixed-signal system on chip for ECG monitoring

Dynamic mode decomposition for blindly separating mixed signals and decrypting encrypted images

In this paper, we introduce the dynamic mode decomposition considering a lag $ \tau $ ($ \tau $-DMD) for solving the blind source separation (BSS) problem of chaotic signals and images. $ \tau $-DMD can be used in BSS-based image decryption with good separation performance. The unmixing problem was formulated as a modal decomposition problem. $ \tau $-DMD was applied on separating linear mixed chaotic signals showing a better separation performance than the existing blind source separation algorithms (Amuse, SOBI, FastICA and JADE). In addition, the case of adding noise in the mixing process was considered, and wavelet de-nosing before $ \tau $-DMD improved the separation performance. For the application, $ \tau $-DMD can be used to remove the noise in electrocardiograms (ECG) and the ocular artifacts in electroencephalograms (EEG). $ \tau $-DMD can also be applied in image processing, showing good separation performance of $ \tau $-DMD for both synthetic mixtures and real-life mixed texts. Structural similarity index measurement (SSIM) and peak signal-to-noise ratio (PSNR) were selected as the evaluation criteria. We tested the separation performance of $ \tau $-DMD on natural images, fingerprint images, and real-life text images and compared the results with other methods. Furthermore, $ \tau $-DMD was applied to decrypt the BSS-based encrypted images. In the process of encryption, we set up the underdetermined problem of BSS by mixing the original and key images, and then $ \tau $-DMD was used to extract the original images in the process of decryption with the secret seeds provided. Two simulations were performed to illustrate the performance of $ \tau $-DMD for image decryption, showing a better decryption results than FastICA.

Robust Fractional Low‐Order Multiple Window STFT for Infinite Variance Process Environment

Mechanical fault vibration signal is a typical non‐Gaussian process, they can be characterized by the infinite variance process, and the noise within these signals may also be the process in complex environments. The performance of the traditional cross‐term reduction algorithm is compromised, sometimes yielding incorrect results under the infinite variance process environment. Several robust fractional lower order time–frequency representation methods are proposed including fractional low‐order smoothed pseudo Wigner (FLOSPW), fractional low‐order multi‐windowed short‐time Fourier transform (FLOMWSTFT), and improved fractional low‐order multi‐windowed short‐time Fourier transform (IFLOMWSTFT) utilizing fractional low‐order statistics and short‐time Fourier transform (STFT) to mitigate cross‐terms, enhance time–frequency resolution, and accommodate the infinite variance process environment. When compared to traditional methods, simulation results indicate that they effectively suppress the pulse noise and function effectively in lower mixed signal noise ratio (MSNR) in an infinite variance process environment. The efficacy of the proposed time–frequency algorithm is validated through its application to mechanical bearing outer ring fault vibration signals contaminated with Gaussian noise and subjected to an α infinite variance process.

Stromal cell-expressed malignant gene patterns contribute to the progression of squamous cell carcinomas across different sites.

Squamous cell carcinomas (SCCs) across different anatomical locations possess common molecular features. Recent studies showed that stromal cells may contribute to tumor progression and metastasis of SCCs. Limited by current sequencing technology and analysis methods, it has been difficult to combine stroma expression profiles with a large number of clinical information. With the help of transfer learning on the cell line, single-cell, and bulk tumor sequencing data, we identified and validated 2 malignant gene patterns (V1 and V5) expressed by stromal cells of SCCs from head and neck (HNSCC), lung (LUSC), cervix (CESC), esophagus, and breast. Pattern V5 reflected a novel malignant feature that explained the mixed signals of HNSCC molecular subtypes. Higher expression of pattern V5 was related to shorter PFI with gender and cancer-type specificity. The other stromal gene pattern V1 was associated with poor PFI in patients after surgery in all the three squamous cancer types (HNSCC p = 0.0055, LUSC p = 0.0292, CESC p = 0.0451). Cancer-associated fibroblasts could induce HNSCC cancer cells to express pattern V1. Adjuvant radiotherapy may weaken the effect of high V1 on recurrence and metastasis, depending on the tumor radiosensitivity. Considering the prognostic value of stromal gene patterns and its universality, we suggest that the genetic subtype classification of SCCs may be improved to a new system that integrates both malignant and non-malignant components.

The Effect of Free Cash Flow, Leverage, and Profitability on Dividend Policy in IDX-Listed Transportation and Logistics Companies

In the business world, companies continuously strive to enhance their value through various strategies and policies. One of the critical policies influencing a company's value is the dividend policy. Dividend policy is a crucial decision for company management as it reflects the financial condition and prospects of the company and can influence shareholders' investment decisions. This study examines the influence of free cash flow, leverage, and profitability on dividend policy in transportation and logistics companies listed on the Indonesia Stock Exchange from 2018-2022. The data used in this study are secondary data obtained from the companies' annual financial reports. This research employs purposive sampling to select 20 companies from a population of 37 IDX-listed transportation and logistics companies. This study measures free cash flow using the free cash flow ratio, leverage using the debt-to-equity ratio, and profitability using return on equity. This study analyzed the data using descriptive statistics and Partial Least Squares (PLS) technique. The study's results indicate that free cash flow and profitability significantly influence dividend policy. Since profitability significantly affects dividend policy, companies with high profitability have to increase dividend payments to attract and retain investors. This will increase positive perceptions in the market and increase the company's stock value. Besides, high free cash flow indicates that the company has sufficient liquidity to pay dividends without sacrificing important investments in operations or growth. Therefore, companies must adopt effective cash management strategies to ensure healthy cash flow, such as optimizing operational costs, working capital efficiency, and appropriate financing strategies to maintain liquidity. Leverage does not affect dividend policy but can provide mixed signals to the market regarding the company's financial condition.

Design of high performance energy efficient CMOS voltage level shifter for mixed signal circuits applications

In modern integrated circuits (ICs), the near threshold voltage (NTV) regime has gained significant attention due to its potential for enabling energy-efficient and high-performance designs. Voltage level shifters (VLS) play a crucial role in facilitating signal transmission between different voltage domains, ensuring compatibility and reliable operation of ICs. However, the unique challenges posed by near threshold voltage operation necessitate a careful analysis and design of voltage level shifters to mitigate the impact of process variations, supply voltage fluctuations, and other performance limitations. This paper presents, a novel energy efficient voltage level shifter design for NTV regime. We validated the proposed VLS in an industrial 65 nm CMOS technology and ASAP7 7 nm Fin-Fet technology. The post layout parasitic extracted simulations highlight that the proposed VLS, an average improvement of ∼50 %, 37 % and 67 % in propagation delay, power dissipation and layout area, respectively, is obtained over the recently reported VLS design at a VDD = 0.4 V in 65 nm CMOS technology. At ASAP7 7 nm Fin-Fet technology, the proposed design shows an average improvement of ∼52 % and 29 % in propagation delay and power dissipation, respectively over the recently reported VLS design at VDD = 0.3 V. The proposed VLS design offers a compelling solution for achieving high-performance, energy-efficient designs in low-power applications.

Research on a Non-Intrusive Load Recognition Algorithm Based on High-Frequency Signal Decomposition with Improved VI Trajectory and Background Color Coding

Against the backdrop of the current Chinese national carbon peak and carbon neutrality policies, higher requirements have been put forward for the construction and upgrading of smart grids. Non-intrusive Load Monitoring (NILM) technology is a key technology for advanced measurement systems at the end of the power grid. This technology obtains detailed power information about the load without the need for traditional hardware deployment. The key step to solve this problem is load decomposition and identification. This study first utilized the Long Short-Term Memory Denoising Autoencoder (LSTM-DAE) to decompose the mixed current signal of a household busbar and obtain the current signals of the multiple independent loads that constituted the mixed current. Then, the obtained independent current signals were combined with the voltage signals to generate multicycle colored Voltage–Current (VI) trajectories, which were color-coded according to the background. These color-coded VI trajectories formed a feature library. When the Convolutional Neural Network (CNN) was used for load recognition, in light of the influence of the hyperparameters on the recognition results, the Bayesian Optimization Algorithm (BOA) was used for optimization, and the optimized CNN network was employed for VI trajectory recognition. Finally, the proposed method was validated using the PLAID dataset. The experimental results show that the proposed method exhibited better performance in load decomposition and identification than current methods.

Energy and synchronization between two neurons with nonlinear coupling.

Consensus and synchronous firing in neural activities are relative to the physical properties of synaptic connections. For coupled neural circuits, the physical properties of coupling channels control the synchronization stability, and transient period for keeping energy diversity. Linear variable coupling results from voltage coupling via linear resistor by consuming certain Joule heat, and electric synapse coupling between neurons derives from gap junction connection under special electrophysiological condition. In this work, a voltage-controlled electric component with quadratic relation in the i-v (current-voltage) is used to connect two neural circuits composed of two variables. The energy function obtained by using Helmholtz theorem is consistent with the Hamilton energy function converted from the field energy in the neural circuit. Chaotic signals are encoded to approach a mixed signal within certain frequency band, and then its amplitude is adjusted to excite the neuron for detecting possible occurrence of nonlinear resonance. External stimuli are changed to trigger different firing modes, and nonlinear coupling activates changeable coupling intensity. It is confirmed that nonlinear coupling behaves functional regulation as hybrid synapse, and the synchronization transition between neurons can be controlled for reaching possible energy balance. The nonlinear coupling is helpful to keep energy diversity and prevent synchronous bursting because positive and negative feedback is switched with time. As a result, complete synchronization is suppressed and phase lock is controlled between neurons with energy diversity.

Outage and throughput performance analysis of smart UAV‐assisted full‐duplex NOMA‐based energy harvesting cognitive radio networks

SummaryIn this paper, the throughput and outage performance for an unmanned aerial vehicle (UAV)‐assisted non‐orthogonal multiple access (NOMA)‐based hybrid cognitive radio (CR) network are examined under m‐Nakagami fading channel. To be more specific, a secondary source transmitter ( ) broadcasts a NOMA‐enabled mixed signal to secondary destinations via UAV relay, which operates in full‐duplex (FD) mode and employs decode‐and‐forward protocol. In our approach, an energy harvesting (EH) circuit is employed at both and UAV to harvest energy from the PU radio frequency (RF) signals. These secondary transmitters are also incorporated with energy detector (ED) circuit as well, which detects the activity of the primary user (PU). The status of PU to be busy or idle is confirmed on the basis of the energy detector's results. In turn, this drives and UAV to switch between underlay and overlay protocols in an adaptive manner to increase spectrum efficiency (SE). The closed form mathematical expressions of the secondary outage probability for NOMA‐based UAV equipped CR networks under imperfect successive interference cancellation (i‐SIC) conditions have been evaluated. The administration of the power allocation policy at the and UAV is assessed while maintaining the PU quality of service (QoS). Finally, MATLAB simulation testbed has been used to verify all the analytical closed form expressions.