Noise Figure Research Articles

State-of-health estimation for lithium-ion batteries based on Kullback–Leibler divergence and a retentive network

Accurate state-of-health (SOH) estimation is crucial for the lithium-ion battery industry, as it underpins the safety, durability, and reliability of lithium-ion batteries. Currently, most researchers use various methods of health indicator (HI) extraction for the SOH estimation of batteries. However, these methods may require certain expertise and prior knowledge to achieve accurate modeling, being affected by measurement noise and other factors. To solve the abovementioned problems, three Kullback–Leibler (KL) divergence features based on partial voltage sequences are proposed as new HIs that are independent of prior knowledge and strongly correlated with SOH. Moreover, a modified retentive network is proposed to enhance SOH estimation accuracy and better utilize HIs than traditional deep learning methods, which have high training costs and insufficient accuracy. To ensure consistent extraction of KL divergence features across various experimental conditions and time intervals, a B-spline algorithm is utilized for interpolation. The effectiveness of the proposed method is validated through analysis of Pearson correlation coefficients and experiments conducted in four dimensions. Additionally, the potential of using the proposed method to compress data on the cloud-side is explored.

Detection of Beaded Karst Caves in Subway Works by Mixed-Source Surface Wave Survey: A Case Study

Abstract Karst cave causes geological disasters in tunneling engineering, which brings great risks to subway construction. The microtremor survey has become one of the main methods for karst cave detection in urban areas due to its convenience and great detection depth. However, the microtremor survey lacks high-frequency signals, resulting in poor detection accuracy on the shallow layer. An advanced method called mixed-source surface wave (MSW) method is proposed by imposing active sources during the continuous passive surface wave survey. MSW is applied to the detection of beaded karst caves (i.e. two or three karst caves at different depths in the same location) in Ji'nan, China. The influences of ambient noise, array type, and other factors on the detection accuracy were studied. Experimental results show that the detection depth and accuracy of MSW are much better than those of passive surface wave method. The inversion accuracy of linear array perpendicular to road is better than the triangle array and L-shaped array. The accuracy of the linear array along the road is worst. The detection accuracy of MSW is similar to that of combined active–passive surface wave methods. However, the basic principle of MSW is easier to understand, and its operation is more convenient. The two-dimensional measurement results show that the MSW method can well reflect the size and location of the beaded karst cave and provide important references for the practical engineering.

Living environment, service quality satisfaction and depression among Chinese older adults in elderly caring social organizations

BackgroundOlder adults living in elderly caring social organizations (SOs) are prone to suffer from depression. Many studies have found correlations between environmental and quality-of-life factors and depression; however, evidence from elderly caring SOs is rare, particularly in China. MethodsA cross-sectional study was conducted among service recipients in elderly caring SOs in Anhui and Chongqing, China. Data on demographic and health-related characteristics, living environment factors, and service quality satisfaction factors in 2171 older adults were used for analysis. The binary logistic regression model was conducted to estimate the association between living environment and service quality satisfaction factors and depression. ResultsOur results indicated that living environment factors in terms of exposure to suitable temperature and humidity (OR = 0.655; 95 % CI: 0.446, 0.963), green coverage >30 % (OR = 0.432; 95 % CI: 0.337, 0.553) were associated with lower odds of developing depression. Also, an opposite relationship was found in the noise factor (OR = 1.985; 95 % CI: 1.395, 2.823). Higher satisfaction with admission and discharge services, dietary services, entertainment services, and psychological support services were also found to be associated with a lower risk of depression. LimitationsA cross-sectional design precluded determining whether living environment, service quality satisfaction, and depression are causally related. Measurement of living environment factors and service quality satisfaction factors needs to be further clarified comprehensively. ConclusionsEnhancing the living environment and the quality of the services provided to seniors in the elderly caring SOs is conducive to the reduction of the likelihood of depression.

Multivariate statistical analysis and bespoke deviation network modeling for geochemical anomaly detection of rare earth elements

Rare earth elements (REEs), a significant subset of critical minerals, play an indispensable role in modern society and are regarded as “industrial vitamins,” making them crucial for global sustainability. Geochemical survey data proves highly effective in delineating metallic mineral prospects. Separating geochemical anomalies associated with specific types of mineralization from the background reflecting geological processes has long been a significant subject in exploration geochemistry. The processing of high-dimensional, non-linear geochemical survey data necessitates a systematic framework to address common issues, including missing values, the closure effect, the selection of appropriate multivariate analysis methods, and anomaly detection techniques in order to detect geochemical anomalies associated with mineral occurrences. The Curnamona Province in South Australia is considered an emerging REE province with significant REE mineralization potential. In this study, we use data from this region to evaluate the performance of a novel machine learning-based framework that incorporates data pre-processing, multivariate statistical analysis, and anomaly recognition to address challenges such as missing data, noise interference, data imbalance and high non-linearity. We utilize lithogeochemical data to map potential greenfield regions of REE mineralization. The primary advantages of our framework lie in its provision of an effective random forest-based data imputation method, utilization of isometric log-ratio transformation to eliminate the closure effect, and reduction of the impact of outliers on data interpretation through robust principal component analysis. Additionally, the framework utilizes a deviation network to learn anomaly scores from complex, non-linear data under imbalanced data conditions, identifying geochemical anomalies associated with REE occurrences by leveraging prior knowledge rather than those caused by data noise or anthropogenic factors. The anomalous areas identified by this framework delineate all known REE deposits and extend to the surrounding regions. Furthermore, a close spatial coupling relationship exists between these strongly anomalous areas and the felsic granite intrusions. The comprehensive workflow for processing geochemical data proposed in this study can effectively address common challenges in the geochemical exploration of critical minerals. The identified geochemical anomalies can provide important clues for subsequent exploration.

TFLN-based nondegenerate optical parametric amplifier with high gain, high saturated optical power and linear wavelength tunability

Optical parametric amplifier (OPA) utilizing thin-film lithium niobate (TFLN) is a cornerstone in integrated microwave optics and high-speed optical communication due to its outstanding properties, including high gain, low noise figure, and high integration. Here, we proposed a pump-depletion nondegenerate amplifier to discuss the saturated optical power. This OPA exhibits a good performance with high gain (over 30 dB) and high saturated optical power (over 15 dBm). The signal frequency is well separated from idle which reduces the difficulties of separation. Meanwhile, we found that the quasi-phase-matching wavelength can be regulated by temperature to achieve a linear adjustable OPA. Detailed design strategies are provided in this paper to underpin the fabrication of high-performance OPA in integrated optics. This theoretical analysis creates opportunities for performance enhancement in microwave photonic links.

Optical assessment of vertical TFET based on heterojunction of GaSb-Si

This paper investigates the electrical and opto-sensing analysis of hetero stack vertical TFETs based on III/V group Gallium antimonide (GaSb)-Si at various wavelengths within the visible spectrum. Initially, the drain current, energy band diagram, and electron density contour plot are extracted in order to study the DC and electrical behavior of the device. Following that, their performance is evaluated by altering the source side thickness and observe the tunneling rate of device. Furthermore, the photo application of the device is noticed by computing the critical optical parameter such as sensitivity (Sn), noise factor, and efficient characteristics. Finally, a comparative table is included to set the benchmark and highlight the importance of hetero stack TFET. The device's maximum reported sensitivity is approximately 25.2 and responsivity (R) is 0.8 A/Watt at 320 nm wavelength. Additionally, we examined the effect of trap charges under light state at the gate-channel, isolator-channel, and source-channel interface respectively.

Low-light enhancement method with dual branch feature fusion and learnable regularized attention

Restricted by the lighting conditions, the images captured at night tend to suffer from color aberration, noise, and other unfavorable factors, making it difficult for subsequent vision-based applications. To solve this problem, we propose a two-stage size-controllable low-light enhancement method, named Dual Fusion Enhancement Net (DFEN). The whole algorithm is built on a double U-Net structure, implementing brightness adjustment and detail revision respectively. A dual branch feature fusion module is adopted to enhance its ability of feature extraction and aggregation. We also design a learnable regularized attention module to balance the enhancement effect on different regions. Besides, we introduce a cosine training strategy to smooth the transition of the training target from the brightness adjustment stage to the detail revision stage during the training process. The proposed DFEN is tested on several low-light datasets, and the experimental results demonstrate that the algorithm achieves superior enhancement results with the similar parameters. It is worth noting that the lightest DFEN model reaches 11 FPS for image size of 1224×1024 in an RTX 3090 GPU.Graphical

Multi-switch-controlled multi-mode TRFE with a high-linearity ATRSW for 2.4-GHz ISM applications

This paper presents a multi-switch-controlled multi-mode transceiver front end (TRFE) designed for the 2.4-GHz industrial–scientific–medical (ISM) band, with a particular focus on enhancing the linearity of the transmit/receive switch (TRSW) on the antenna side. Considering the distinct power levels in the RX/TX paths, the TRSW is designed to be asymmetric. This design not only elevates IP-0.1dB by over 50 % compared to the conventional symmetric topology, but also absorbing its function into the input matching network (IMN) of the low noise amplifier (LNA), thereby minimally impacting the noise figure (NF) of the RX chain. For the TX path, the high-pass component of the power amplifier (PA) output matching network (OMN) is repurposed, with a singular inductor being redeployed to serve as an inductive ESD safeguard. Moreover, the driving stage employs a passive gain-boosting technique, which elevates the gain by 3 dB compared to the traditional cascode structure. For verification, this TRFE is fabricated in 0.18-μm CMOS with a die size of 1.53 mm2. It achieves a NF of 2.4 dB and a RX gain of 16.7 dB under a 3.3-V supply. A TX gain of 26.2 dB and a saturation output power of 23.3 dBm with a peak power-added efficiency (PAE) of 38.8 % are also demonstrated.

Teachers’ Perception of Urban Noise in The Classroom: Case Study in Mukah, Sarawak

Environmental noise is an unwanted sound created by urbanisation and industrialisation processes, which has been linked to problems of teachers in the school such as lack of concentration, annoyance, speech interference and low performances. The impact of noise in the classroom during the teaching session can cause increased stress and fatigue to teachers. This study aims to identify the noise factor in the school and the impacts of urban noise on teachers during the teaching session through subjective evaluation. In the present study, a questionnaire survey was distributed to the teachers at three secondary schools in Mukah, Sarawak. The developed questionnaire was evaluated by the experts and pilot study was carried out prior to the actual survey. A total of 204 respondents from secondary schools named as School A, School B and School C were participated in this study. The questionnaires were sent to all teachers in three schools in the form of Google Forms and sent by using the WhatsApp application. The main factor of environmental noise that affects teachers from all schools studied came from transportation. Most of the teachers agreed that they need to raise their voice and walk around inside the classroom during the class in order to make sure students can hear their message clearly

Low Frequency Electronic Noise Characterization of Au-W/β-Ga2O3 Schottky Diodes

Ultra-wide bandgap (UWBG) semiconductors have attracted tremendous amount of interest for applications in high-power electronics and solar-blind UV photodetectors. In particular, β-Ga2O3 has attracted significant research attention in recent years due to its promising electrical and optical characteristics including a large bandgap, high breakdown electrical field, and large Baliga’s figure of merit compared to well-established wide-bandgap technologies such as GaN and SiC [1].Low frequency electronic noise, which is present in all electronic devices, can be used as a diagnostic tool for semiconductor material and device characterization. By studying the low frequency noise in semiconductor materials and devices, the physics associated with the noise properties of bulk and interface defects and traps can be investigated [2]. Measurement of low frequency noise is also crucial for evaluating material quality and device reliability.In this work, we investigate the forward-bias diode parameters and low-frequency electronic noise in Au-W/β-Ga2O3 Schottky barrier diodes. The Schottky contact is formed by depositing 20 nm of Tungsten (W) using dc sputtering followed by 340 nm of Gold (Au) using e-beam evaporation, and subsequent annealing [3].We first measure the I-V characteristics and extract important diode parameters, such as the Schottky barrier height, ideality factor, and series resistance from room temperature forward-bias current-voltage characteristics. The extracted Schottky barrier height for these devices is in the range 0.7-0.8 eV and the ideality factor is approximately 1.2.We next measure the low-frequency electronic noise in Au-W/β-Ga2O3 Schottky diodes at different forward bias voltages at room temperature. We find that the current noise spectral density exhibits 1/f-type behavior for all forward bias voltages at low frequency with an exponent ranging from 0.75 to 1.15, indicating that the low frequency noise of these devices is dominated by excess (flicker) noise. No Lorentzian-shaped generation-recombination (G-R) noise is observed. At low frequencies, the shot noise contribution is found to be negligible for the values of the current in our devices, and the total noise is 1/f-limited.We also investigate the dependence of the current noise spectral density on forward bias current, revealing different relationships in different current regimes. At low currents, the noise spectral density is found to scale approximately as the square of current, whereas at high currents, the noise spectral density begins to saturate. Such noise behavior can be attributed to different noise sources dominating in different current regimes. Furthermore, using the noise figure of merit adapted for ultra-wide bandgap semiconductor devices [4], we find that the Au-W/β-Ga2O3 devices have lower noise levels compared to other emerging UWBG materials and similar noise levels as well-established technologies such as GaN.These results provide important insights into the electronic noise properties of Au-W/β-Ga2O3 Schottky diodes and show that low frequency noise characterization is an important diagnostic tool for assessing the quality and reliability of gallium oxide based wide-bandgap materials and device technologies.

Past rewinding of fluid dynamics from noisy observation via physics-informed neural computing.

Reconstructing the past of observed fluids has been known as an ill-posed problem due to both numerical and physical challenges, especially when observations are distorted by inevitable noise, resolution limits, or unknown factors. When employing traditional differencing schemes to reconstruct the past, the computation often becomes highly unstable or diverges within a few backward time steps from the distorted and noisy observation. Although several techniques have been recently developed for inverse problems, such as adjoint solvers and supervised learning, they are also unrobust against errors in observation when there is time-reversed simulation. Here we present that by using physics-informed neural computing, robust time-reversed fluid simulation is possible. By seeking a solution that closely satisfies the given physics and observations while allowing for errors, it reconstructs the most probable past from noisy observations. Our work showcases time rewinding in extreme fluid scenarios such as shock, instability, blast, and magnetohydrodynamic vortex. Potentially, this can be applied to trace back the interstellar evolution and determining the origin of fusion plasma instabilities.

A double clad ASE Re-injected hybrid TDFA and HDFA amplifier with ±1.44 dB GF

Abstract Current developments in wireless communications accentuate the exigency of high gain, low noise figure (NF) and minimum gain fluctuations (GF) based optical amplifier in 1.9–2.15 μm eye safe wavelength band. Hybrid thulium (Tm) doped fiber amplifier (TDFA) and holmium (Ho) doped fiber amplifier (HDFA) are the perfect candidates to extend the bandwidth towards 2 μm long wavelengths. In this work, amplifier spontaneous emission (ASE) re-injected double clad TDFA-HDFA hybrid amplifier using Fiber bragg gratings (FBGs) is presented for the amplification of 64 wavelength division multiplexed (WDM) channels covering 63 nm wavelength window. Further, performance of proposed amplifier with and without ASE reinjection is evaluated in terms of Gain, NF, GF and optical signal to noise ratio (OSNR). Effects of single and double clad on the Gain are studied and optimization of Tm and Ho doped fiber length, pump powers, and input power has also been accomplished. The GF as low as ±1.44 dB, Gain >28 dB, NF < 5.08 dB, output power 4.2 W are achieved in ASE re-injected configuration using 785 nm pump at 55 dBm in TDFA and 1,900 nm pump at 20 dBm in HDFA. It is noticed that double clad and ASE reinjection in TDFA and HDFA offer better GF.

Detailed scrutiny of FWM in holmium-doped fiber amplifier (HOFA) in WDM systems

Abstract Four wave mixing (FWM) in wavelength division multiplexed (WDM) systems is prominent performance degrading nonlinearity and limits the total capacity of the system. FWM is explored in different fiber optical amplifiers such as erbium-doped fiber amplifier (EDFA), Raman fiber amplifier (RFA), and also in semiconductor optical amplifier (SOA). Reported studies in HOFA revealed that nonlinear effects in these amplifiers are low; however, in present study, in this research article WDM-HOFA system has been simulated and studied FWM in detail at different HOFA parameters such as input power, length, core radius, doping radius, numerical aperture (NA), Ho ion density, and effects of co- and counter-pumping. Gain and noise figure (NF) are also investigated at aforementioned parameters, and performance is evaluated in terms of optical signal to noise ratio (OSNR) at 2,030 nm wavelength window. It is observed that least FWM obtained at 3 m Ho fiber length, 2 µm core radius and doping radius, 15.6 × 1023 m−3 Ho ion doping, 0.1 NA, −10 dB input power, 0.8 GHz channel spacing, and 4 WDM channels. However, better gain and NF comes at different values of these parameters, and as a result, there is trade-off between FWM and gain, NF.

Two gm/ID design approaches for high-performance dual-resistive feedback noise-cancelling LNA

ABSTRACT In this article, a self-bias MOSFET-based dual-resistive feedback low-noise amplifier (LNA) is investigated. A detailed mathematical analysis of the addressed amplifier has been carried out using the return ratio-based feedback analysis technique. Furthermore, the LNAs have been simulated via Cadence using the TSMC 65 nm PDK. Two g m / I D strategies are described to handle the performance trade-offs for enhancing gain, noise figure (NF) and power consumption. The two strategies are conceptually similar to one another, with the selection of the transconductance efficiency being the only difference. While the first technique uses a transconductance efficiency to achieve the lowest power consumption while keeping an acceptable NF, the second strategy uses a particular transconductance efficiency for flawless noise cancelling without consideration for reduced power consumption. According to simulation results, the first recommended LNA has a voltage gain of 12 dB over a 3 dB bandwidth of 0.5–10 GHz, a minimal NF of 1.7 dB @ 7.014 GHz, a maximum NF of 1.932 dB @ 0.5 GHz, consumes 12.42 mW from a low supply voltage of 1.2 V and achieved a third-order intercept of −2.34 dBm @ 1 GHz. The simulation results of the second recommended LNA also show that it can operate with a low supply voltage of 1.2 V and still achieve a minimum NF of 0.978 dB @ 2.729 GHz, a maximum NF of 1.048 dB @ 6 GHz, II P 3 of 1.34 dBm and a voltage gain of 13 dB over a 3 dB bandwidth of 0.5–6 GHz with a consuming power of 25 mW.

Polynomial Fitting-Based Noise Reduction for Correlation Functions in Medium-Frequency Radar

In the theoretical calculation of atmospheric wind fields using the cross-correlation analysis method of Medium-Frequency radar, it is necessary to compute a series of correlation parameters from the received echo signals, such as autocorrelation and cross-correlation functions, within the main lobe range of the antenna array to retrieve atmospheric parameters. However, both theoretical analysis and practical applications have shown that the shape of correlation functions can be affected by atmospheric conditions and receiver noise, leading to significant biases in the estimated correlation parameters within the main lobe range. In this study, we theoretically analyze the influence of noise on the amplitude of autocorrelation and cross-correlation functions. We propose a noise reduction method based on the characteristics of correlation functions at the zero-delay point to calculate the noise factor and process the correlation functions within the main lobe range. Furthermore, we conduct simulation analysis to evaluate the performance of this noise reduction method and summarize the effects of the number of fitting points and fitting methods on the noise reduction performance.

HQC-MCDCNN: a novel hybrid quantum–classical multi-path denoising convolutional neural network

Image denoising is a longstanding and enduring visual problem, and with the continuous rise of quantum computing in the field of machine learning, its role in image processing has become increasingly important. This paper introduces for the first time the use of multiscale variational quantum circuits in the field of image denoising, aiming to enhance the performance of classical convolutional neural networks and explore the potential advantages of combining quantum and classical approaches. In this work, we propose a novel Hybrid Quantum-Classical Multi-Path Denoising Convolutional Neural Network, abbreviated as HQC-MCDCNN. The HQC-MCDCNN is composed of a hybrid of quantum and classical elements, with the quantum part using multiscale variational quantum circuits instead of classical convolutional layers for feature extraction, and the classical part employing a newly designed multi-path denoising convolutional neural network for supervised learning. Together, these components synergistically achieve image denoising. It is worth noting that this paper aims to build readers’ intuition for quantum computing, presenting all internal details of this work with rich images and visualizations. To demonstrate the denoising capability of HQC-MCDCNN, we conducted rigorous comparative experiments. Due to the constraints of Noisy Intermediate-Scale Quantum (NISQ) devices and the limited number of quantum bits, the experiments were based on the MNIST and Fashion-MNIST datasets with varying degrees of noise (noise factors ranging from 0.3 to 0.7), employing a 6-fold stratified sampling strategy for cross-validation. The experimental results indicate that HQC-MCDCNN is promising across all evaluation metrics, particularly outperforming other models by 56.5% in the average UIQ index. This suggests that our hybrid model exhibits outstanding feature extraction capabilities and excellent denoising performance, providing a promising path for addressing image denoising challenges.

A Pseudo-Differential LNA with Noise Improvement Techniques for Concurrent Multi-Band GNSS Applications

A low-noise amplifier (LNA) design with the operation of concurrent dual-band for Global Navigation Satellite System (GNSS) receivers with single channel is presented in this work. This LNA structure has an inductively degenerated cascode architecture and is pseudo-differential, operating at two frequencies simultaneously (1.2 GHz and 1.57 GHz). Two noise reduction/cancellation techniques, using load capacitor and feedforward path, respectively, are proposed resulting in an excellent improvement in the noise figure (NF). The input matching circuit uses both series and parallel resonant components to enable concurrency. The adopted pseudo-differential structure results in input balun elimination. Inductively degenerated cascode topology provides both input impedance and optimum noise impedance matching. The soundness of the proposed approach has been demonstrated in a 0.18-µm CMOS technology by TSMC. Simulation results show that at 1.2 GHz and 1.57 GHz the LNA achieves −13 dB and −11 dB of input matching, 24.6 dB and 24.7 dB of gain, 1.47 dB and 1.43 dB of NF, respectively. The input-referred 1-dB compression point (IP1dB) is around −16 dBm, while the input-referred third-order intercept point (IIP3) achieves −2.2 dBm at 1.2 GHz and −0.6 dBm at 1.57 GHz. The LNA draws about 13 mA from a 1.8-V supply voltage.

Cryogenic bipolar low noise dc amplifier for low frequency applications

A low-noise bipolar differential dc amplifier was studied at temperatures of 300 and 77 K. It was shown that to ensure the best amplifier performance in terms of noise figure when the operating temperature decreases from 300 to 77 K, it is advisable to use the transistor in the mode of low currents not exceeding 2 mA. It has been established that lowering the operating temperature to 77 K leads to a decrease in the input resistance of the amplifier from a value of several kiloohms to 100 Ohms, the dynamic range increases from 80 to 85 dB, and the harmonic coefficient increases from 0.09% to 1%. In addition, lowering the operating temperature to 77 K has a significant effect on the noise properties of the amplifier: the spectral density of voltage noise decreases from 1 to 0.4 nV/Hz1/2, the spectral density of current noise increases from 2.5 to 9 pA/Hz1/2, while also The threshold frequencies of 1/f noise increase: for voltage from (0.1...10) to 20 Hz and for current from (10...100) to 1000 Hz. The possibility of using an amplifier for low-temperature measurements of samples with low input resistance is substantiated.

Singular Spectrum Analysis of Exoplanetary Transits

Transit photometry is currently the most efficient and sensitive method for detecting extrasolar planets (exoplanets) and a large majority of confirmed exoplanets have been detected with this method. The substantial success of space-based missions such as NASA’s Kepler/K2 and Transiting Exoplanet Survey Satellite has generated a large and diverse sample of confirmed and candidate exoplanets. Singular spectrum analysis (SSA) provides a useful tool for studying photometric time series and exoplanetary transits. SSA is a technique for decomposing a time series into a sum of its main components, where each component is a separate time series that incorporates specific information from the behavior of the initial time series. SSA can be implemented for extracting important information (such as main trends and signals) from the photometry data or reducing the noise factors. The detectability and accurate characterization of an exoplanetary transit signal is principally determined by its signal-to-noise ratio (S/N). Stellar variability of the host star, small planet to star radius ratio, background noises from other sources in the field of observations and instrumental noise can cause lower S/Ns and consequently, more complexities or inaccuracies in the modeling of the transit signals, which in turn leads to the inaccurate inference of the astrophysical parameters of the planetary object. Therefore, implementing SSA leads to a more accurate characterization of exoplanetary transits and is also capable of detecting transits with low S/Ns (S/N < 10). In this paper, after discussing the principles and properties of SSA, we investigate its applications for studying photometric transit data and detecting low S/N exoplanet candidates.

Channel selection method for the CH4 profile retrieval using the Atmospheric Sounder Spectrometer by Infrared Spectral Technology

Methane (CH4) is the second-largest greenhouse gas contributing to global warming, surpassed only by CO2, has a large difference in its vertical concentration distribution, and closely affects the global environment and climate change. The variations in the vertical concentrations of CH4 need to be monitored. Ground-based infrared hyperspectrometers can measure the fine variations of the CH4 concentrations in the vertical distribution within the planetary boundary layer (PBL). However, different detection channels are easily affected by instrumental noise and other environmental factors, leading to differences in the channel spectral characteristics and thereby affecting the accuracy of the CH4 profile retrieval. In this study, an information-weighted channel selection method is proposed for the CH4 profile retrieval using the Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST) to address the differences in the channel characteristics from the different interference factors. This method leverages the information content of CH4 and its environmental interference factors in each channel to derive the weighting factors, and a comprehensive weighting approach is subsequently applied to ascertain the effective information content of CH4. The method then establishes the threshold for the effective CH4 information content, considering the influence of noise, to select the optimal channels. We employ this method in our study, and 22 channels are selected as the optimal channels for the CH4 profile retrieval. We also evaluate the retrieval capability of the CH4 profile and the anti-interference ability of the selected channels using simulated spectra under clear-sky conditions. When retrieving the CH4 profile using the 1200–1390 cm−1 band (394 channels in total), the CH4 profile is mainly affected by temperature, water vapor, aerosol optical depth (AOD) and N2O. In addition, the mean absolute error (MAE) and the root mean square error (RMSE) for the CH4 profile retrieval using the selected channels are substantially reduced.