Frequency Channels Research Articles

Dual-polarization RF channelizer based on microcombs.

We report a dual-polarization radio frequency (RF) channelizer based on microcombs. Two high-Q micro-ring resonators (MRRs) with slightly different free spectral ranges (FSRs) are used: one MRR is pumped to yield soliton crystal microcombs ("active"), and the other MRR is used as a "passive" periodic optical filter supporting dual-polarization operation to slice the RF spectrum. With the tailored mismatch between the FSRs of the active and passive MRRs, wideband RF spectra can be channelized into multiple segments featuring digital-compatible bandwidths via the Vernier effect. Due to the use of dual-polarization states, the number of channelized spectral segments, and thus the RF instantaneous bandwidth (with a certain spectral resolution), can be doubled. In our experiments, we used 20 microcomb lines with ∼ 49 GHz FSR to achieve 20 channels for each polarization, with high RF spectra slicing resolutions at 144 MHz (TE) and 163 MHz (TM), respectively; achieving an instantaneous RF operation bandwidth of 3.1 GHz (TE) and 2.2 GHz (TM). Our approach paves the path towards monolithically integrated photonic RF receivers (the key components - active and passive MRRs are all fabricated on the same platform) with reduced complexity, size, and unprecedented performance, which is important for wide RF applications with digital-compatible signal detection.

Carrier frequency phase calibration method for global navigation satellite systems signal simulators

Measurements of carrier frequency phase differences of global navigation satellite system signals between multiple antennas are used in many coordinate and navigation applications. Global navigation satellite systems signal simulators are used for controlling global navigation satellite systems receiver’s instrumental errors. Therefore, it is necessary to determine the systematic error of the carrier frequency phase of generated signals for signal simulators. For this purpose a new calibration method of global navigation satellite systems simulators was developed. Method is based on refined errors model of generated phase differences of two radio frequency channels of the simulator. The proposed method was implemented using a wideband oscilloscope as an analog-to-digital converter. Approbation of the calibration method was carried out by comparison with the measurement results obtained using the State secondary standard of units of complex transmission coefficients in the range of 0...–60 dB and complex reflection coefficients in the range of 0.002–1 in the frequency range of 0.05–65 GHz (registration No. 2.1.ZZT.0210.2015). The calibration method allows one to determine carrier frequencies phase offsets of navigation signals for global navigation satellite systems simulators with expanded uncertainty (with a coverage factor of 3) of 1°. The proposed method provides the required accuracy of generating phase differences of the carrier frequencies of global navigation satellite systems signals is ensured for modern types of global navigation satellite systems simulators.

Co‐channel interference cancellation based on BSS for multi‐satellite MIMO communication systems with FFR

SummaryMultiple‐input multiple‐output (MIMO) technology can boost the capacity of conventional satellite communications, and full frequency reuse (FFR) can further improve the capacity by making full use of frequency resources. However, FFR will cause severe co‐channel interference and lead to a degradation of channel capacity. Blind source separation (BSS) algorithm can be used to eliminate co‐channel interference and does not require prior information, which avoids the extra occupation of channel frequency resources. While the multi‐satellite MIMO communication system have delay mixing characteristic, the separation performance will be degraded if the delay is ignored. In this paper, DMBSS algorithm for delay mixing is proposed. Firstly, the DMBSS algorithm utilizes Taylor expansion to achieve dimension expansion of observed signals and so as to transform the delay mixing into instantaneous mixing with extended dimension. Secondly, complex non‐orthogonal joint diagonalization algorithm is used to solve the separation matrix equation of extended observation signals and extended source signals and thus to eliminate the co‐channel interference. Simulation results show that for multi‐satellite MIMO communication scenarios with FFR, the proposed algorithm can effectively improve the performance of co‐channel interference cancellation and reduce the value of BER. Furthermore, the influence of order of Taylor expansion on the separation performance is also analyzed and simulated in this paper to show the cost performance of the proposed algorithm.

A Nonredundant SVD-based Precoding Matrix for Blind Channel Estimation in CP-OFDM Systems Over Channels with Memory

In this paper, we investigate blind channel estimation in cyclic, prefixed orthogonal frequency division multiplexing (OFDM) systems using precoding matrices. Many studies show how the performance of bit error rate (BER) and mean square error of channel estimation are impacted by using a circulant matrix as a precoding matrix. We investigate a new structure for the precoding matrix in which the first row is generated by an exponentially decreasing equation along with the Toeplitz function and deploying singular value decomposition (SVD) to generate the final precoder matrix. This type of SVD-based precoding matrix has been presented and simulated under various circumstances. The resulting precoding matrix is employed in two different OFDM systems to estimate the channel frequency response vector up to scalar ambiguity. The proof of the precoding matrix full rank property is presented, along with other criteria that help with the development of various precoding matrices.

A new X‐band frequency up‐converter design for LEO satellite ground station utilizing a single superheterodyne

AbstractIn this work, the advancing wireless revolution (AWR) modeling and Microwave Office tools are used to develop and execute a broadband up‐converter system. Before sending the input frequency to the antenna interface, a radio frequency block upconverts it once to the appropriate frequency level. Mixing blocks, oscillators, amplifiers, step attenuators, isolators, and filters are included. The input frequency (IF) band is MHz, while the output frequency (radio frequency [RF]) band is MHz. The usage of attenuators increases the dramatic range of the circuit. It is a one‐stage up‐conversion circuit that combines the IF band in one pass to create the appropriate RF spectrum. Here, a single phase‐locked oscillator is employed at the local oscillator port of the mixers, while the other is a programmable frequency generator that generates X‐band frequency channels with a 10 MHz spacing.

Speed-enhanced scattering compensation method with sub-Nyquist sampling.

A rapid feedback-based scattering compensation method is particularly important for guiding light precisely within turbid tissues, especially the dynamic tissues. However, the huge number of measurements that come from the underutilization of the signal frequency channel greatly limits the modulation speed. This paper introduces a rapid compensation method with the sub-Nyquist sampling which improves the channel utilization and the speed of wavefront shaping. The number of measurements is reduced to ∼1500 with 32 × 32 freedom, and the PBR of the focus reaches ∼200. The system performances are demonstrated by focusing the light through brain slices of different thicknesses.

A new pilot shared method for saving bandwidth cost of OFDM

The orthogonal frequency division multiplexing (OFDM) system applies coherent demodulation to achieve high spectral efficiency at a bandwidth cost by the pilot tones. Considering the statistical property of the down-link channels to the users, it can be found that there is an opportunity to reduce the pilot number in the conventional designs while maintaining the same signal demodulation performances. The design philosophy involves utilizing the difference of the channel coherent bandwidths (CCBs) by allocating data to appropriate positions upon the fact that different CCBs can tolerate different minimized pilot spacing. The proposed design allows each user’s equipment’s data not to exceed its CCB with the sparser pilots. The theoretical analysis is carried out based on the concept of channel frequency response using linear interpolation with channel estimation employing the least squares (LS) method. The gain of the proposed method is demonstrated in terms of the ergodic capacities and confirmed by the simulations.

Semantic Segmentation of Remote Sensing Data Based on Channel Attention and Feature Information Entropy.

The common channel attention mechanism maps feature statistics to feature weights. However, the effectiveness of this mechanism may not be assured in remotely sensing images due to statistical differences across multiple bands. This paper proposes a novel channel attention mechanism based on feature information called the feature information entropy attention mechanism (FEM). The FEM constructs a relationship between features based on feature information entropy and then maps this relationship to their importance. The Vaihingen dataset and OpenEarthMap dataset are selected for experiments. The proposed method was compared with the squeeze-and-excitation mechanism (SEM), the convolutional block attention mechanism (CBAM), and the frequency channel attention mechanism (FCA). Compared with these three channel attention mechanisms, the mIoU of the FEM in the Vaihingen dataset is improved by 0.90%, 1.10%, and 0.40%, and in the OpenEarthMap dataset, it is improved by 2.30%, 2.20%, and 2.10%, respectively. The proposed channel attention mechanism in this paper shows better performance in remote sensing land use classification.

Alpha Oscillations Create the Illusion of Time.

Recent neuroscience experiments have brought inconsistent findings to light about the influence of neural activity in the alpha-frequency band (at ≈10 Hz) on the temporal dynamics of visual perception. Whereas strong alpha effects were found when perception was more based on endogenous factors, there were null-effects for alpha when perception relied more on objective physical parameters. In this Perspective, I open up a new view on neural alpha activity that resolves some important aspects of this controversy by interpreting alpha not as temporal processing of sensory inputs per se but above all as the observer's internal processing dynamics, their so-called perception sets. Perception sets reflect internally stored knowledge for how to organize and build up perceptual processes. They result from previous sensory experiences, are under top-down control to support goal-directed behavior, and root in pre-established neural networks that communicate through alpha frequency channels. I present three example cases from the recent neuroscience literature that show an influence of alpha-driven perception sets on the observer's visual-temporal resolution, object processing, and the processing of behaviorally relevant image content. Because alpha-driven perception sets can structure perception from its high-level aspects, like categories, down to its basic building blocks, like objects and time samples, they may have a fundamental impact on our conscious experience of the sensory world, including our perception of time itself.

A Security Aware Channel Assignment Protocol for Software Defined Networks

Software Defined Networks (SDNs) have a great potential in supporting time-critical data delivery among the Internet of Things (IoT) devices and for emerging applications such as smart cities and automation. A wireless channel (radio) about which we have information is called a software defined radio. However, Software defined radio Networks share common resources such as bandwidth or spectrum among several users or stations. Due to continued sharing of resources, cognitive networks often come under security attacks, most common of which are jamming attacks. In the case of jamming attacks, deliberately designed random jamming signals are added to the channel. These jamming signals along with noise result in packet losses and low throughput, degrading the overall performance of the cognitive network. In this work, a security aware jamming rejection mechanism is proposed which detects suspicious signals in the channel frequency response and employs discrete equalization to recover transmitted data. Moreover, this also reduces the effects of noise in the channel. It has been shown that the proposed system achieves higher throughput compared to previous techniques for low, moderate and high jamming activity. Keywords:- Software defined radio, Internet of Things (IoT), Jamming Activity, Energy Detection, Equalization, Throughput.

A model for the formation of frames of fragmented user data packets with non-standardized protocol block headers at the channel level

The continuous and dynamic development of satellite communication systems actualizes the issues of the efficiency of using the frequency and channel resource of communication channels. In the conditions of using packet types of transmissions, fragmentation of user data packets is one of the ways to increase the efficiency of using a channel resource. A wide variety of approaches to the formation of the channel layer and their functional ambiguity in the conditions of fragmentation increase the level of uncertainty in the choice of the protocol stack used to build a communication channel, which in turn creates a prerequisite for a decrease in the quality of the use of the channel resource. In view of this, the task of modeling the procedure for forming frames of fragmented user data packets is relevant, taking into account possible variants of non-standardized protocol block headers at the channel level. Based on the studied non-standardized headers of channel-level protocol blocks, reflecting the features of their formation, to present a model for the formation of frames of fragmented user data packets. This will make it possible to identify the necessary set of informative features, the study of which will make it possible to efficiently and with high probability recognize frames of fragmented packets in a digital data stream with packet switching in channels of satellite communication networks for various purposes. The article considers the procedure for forming frames of fragmented user data packets with non-standardized headers of protocol blocks at the channel level used in data transmission channels with packet switching of satellite communication systems for various purposes. The structural and structural-linguistic models of frames of a fragmented package of user data in conditions of transmission over a satellite communication channel have been developed. The structure of signals with fragmented packets of user data based on a directed graph is presented and investigated. The service level indicators of the fragmented user data package frame formation model were evaluated according to parameters that allow an objective assessment of the effectiveness and validity of the use of fragmentation in data transmission in satellite communication channels. The data obtained using the developed model allows us to qualitatively assess the indicators of the level of service of the data transmission channel with fragmentation of user message packets and reasonably prove the effectiveness of the fragmentation described by the model when it is necessary to split the desired message into parts when it is transmitted over the CCC channel.

SNR Estimation for 5G Satellite-to-Ground Communication in Ultra-Low SNR Environment Based on Channel Frequency Response Reconstruction

SNR Estimation for 5G Satellite-to-Ground Communication in Ultra-Low SNR Environment Based on Channel Frequency Response Reconstruction

Four-Channel Ultrasonic Sensor for Bulk Liquid and Biochemical Surface Interrogation.

Custom electronics tailored for ultrasonic applications with four ultrasonic transmit-receive channels and a nominal 25 MHz single channel frequency were developed for ultrasound BAW and SAW biosensor uses. The designed integrated microcontroller, supported by Python with a SciPy library, and the developed system measured the time of flight (TOF) and other wave properties to characterize the acoustic properties of a bulk of the liquid in a microchannel or acoustic properties of biological species attached to an analytic surface in real time. The system can utilize both piezoelectric and capacitive micromachined ultrasound transducers. The device demonstrated a linear response to changes in water salinity. This response was primarily attributed to the time-of-flight (TOF) changes related to the varying solution density. Furthermore, real-time DNA oligonucleotide-based interactions between oligonucleotides immobilized on the device's analytical area and oligonucleotides attached to gold nanoparticles (Au NPs) in the solution were demonstrated. The biological interaction led to an exponential decrease in the acoustic interfacial wave propagating across the interface between the solution and the solid surface of the sensor, the TOF signal. This decrease was attributed to the increase in the effective density of the solution in the vicinity of the sensor's analytical area, as Au NPs modified by oligonucleotides were binding to the analytical area. The utilization of Au NPs in oligonucleotide surface binding yields a considerably stronger sensor signal than previously observed in earlier CMUT-based TOF biosensor prototypes.

A Multi-Resolution Denoising Method for Low-Dose CT Based on the Reconstruction of Wavelet High-Frequency Channel.

Computed tomography (CT) is widely applied in contemporary clinic. Due to the radiation risks carried by X-rays, the imaging and post-processing methods of low-dose CT (LDCT) become popular topics in academia and industrial community. Generally, LDCT presents strong noise and artifacts, while existing algorithms cannot completely overcome the blurring effects and meantime reduce the noise. The proposed method enables CT extend to independent frequency channels by wavelet transformation, then two separate networks are established for low-frequency denoising and high-frequency reconstruction. The clean signals from high-frequency channel are reconstructed through channel translation, which is essentially effective in preserving detailed structures. The public dataset from Mayo Clinic was used for model training and testing. The experiments showed that the proposed method achieves a better quantitative result (PSNR: 37.42dB, SSIM: 0.8990) and details recovery visually, which demonstrates our framework can better restore the detailed features while significantly suppressing the noise.

A Chaos-FFH Based Approach Against Illegal Intelligent Reflecting Surface (IIRS) Based Jamming Attacks

Illegal intelligent reflecting surfaces (I- IRSs), i.e., the illegal deployment and utilization of IRSs, impose serious harmful impacts on wireless networks. The existing I-IRS-based illegal jammer (IJ) requires channel state information (CSI) or extra power or both, and therefore, the I-IRS- based IJ seems to be difficult to implement in practical wireless networks. Due to continued sharing of resources, wireless networks often come under security attacks, most common of which are eavesdropping attacks. In the case of eavesdropping attacks, deliberately designed random eavesdropping data is added to the channel. These eavesdropping along with noise result in packet losses and low throughput, degrading the overall performance of the cognitive network. In this work, a security aware jamming and eavesdropping rejection mechanism is proposed which detects suspicious signals in the channel frequency response and employs discrete equalization to recover transmitted data. The proposed approach uses a chaos based FFH protocol for possible adversarial eavesdropping attacks. The error rate and average sum rate show that the proposed system outperforms the exiting systems in terms of the performance metrics. Keywords:- Illegal Intelligent Reflecting Surface (IIRS), Jamming Attack, Packet Intercept. Error Rate, Sum Rate

Time-Allocation Adaptive Data Rate: An Innovative Time-Managed Algorithm for Enhanced Long-Range Wide-Area Network Performance

Currently, a variety of Low-Power Wide-Area Network (LPWAN) technologies offer diverse solutions for long-distance communication. Among these, Long-Range Wide-Area Network (LoRaWAN) has garnered considerable attention for its widespread applications in the Internet of Things (IoT). Nevertheless, LoRaWAN still faces the challenge of channel collisions when managing dense node communications, a significant bottleneck to its performance. Addressing this issue, this study has developed a novel “time allocation adaptive Data Rate” (TA-ADR) algorithm for network servers. This algorithm dynamically adjusts the spreading factor (SF) and transmission power (TP) of LoRa (Long Range) nodes and intelligently schedules transmission times, effectively reducing the risk of data collisions on the same frequency channel and significantly enhancing data transmission efficiency. Simulations in a dense LoRaWAN network environment, encompassing 1000 nodes within a 480 m × 480 m range, demonstrate that compared to the ADR+ algorithm, our proposed algorithm achieves substantial improvements of approximately 30.35% in data transmission rate, 24.57% in energy consumption, and 31.25% in average network throughput.

CT synthesis from MR images using frequency attention conditional generative adversarial network

Magnetic resonance (MR) image-guided radiotherapy is widely used in the treatment planning of malignant tumors, and MR-only radiotherapy, a representative of this technique, requires synthetic computed tomography (sCT) images for effective radiotherapy planning. Convolutional neural networks (CNN) have shown remarkable performance in generating sCT images. However, CNN-based models tend to synthesize more low-frequency components and the pixel-wise loss function usually used to optimize the model can result in blurred images. To address these problems, a frequency attention conditional generative adversarial network (FACGAN) is proposed in this paper. Specifically, a frequency cycle generative model (FCGM) is designed to enhance the inter-mapping between MR and CT and extract more rich tissue structure information. Additionally, a residual frequency channel attention (RFCA) module is proposed and incorporated into the generator to enhance its ability in perceiving the high-frequency image features. Finally, high-frequency loss (HFL) and cycle consistency high-frequency loss (CHFL) are added to the objective function to optimize the model training. The effectiveness of the proposed model is validated on pelvic and brain datasets and compared with state-of-the-art deep learning models. The results show that FACGAN produces higher-quality sCT images while retaining clearer and richer high-frequency texture information.

Image color rendering based on frequency channel attention GAN

Image color rendering based on frequency channel attention GAN

Multi-Frequency Channel Measurement and Characteristic Analysis in Forested Scenario for Emergency Rescue

Wireless communication has been widely used in emergency rescue, including from command vehicles to command vehicles, command vehicles to rescue teams, command vehicles to wireless sensors, and rescue teams to intelligent platforms such as unmanned vehicles. Compared to those used in cities and suburbs, when the same communication equipment is used in forests, its communication performance, such as transmission distance, is entirely different. The main reason for this phenomenon is the extraordinary complexity of wireless signal propagation in forest scenarios. Therefore, in order to accurately and quantitatively describe the wireless channel characteristics in forest scenarios, a frequency channel measurement activity is conducted in a forest and analysis is performed to acquire the channel characteristics in forest scenarios. The measurements are carried out at 380 MHz, 640 MHz, and 1420 MHz in virgin forest. Based on the measurement data, the average power delay profile (APDP) is obtained, and multipath components (MPCs) are extracted. Root mean square (RMS) delay spread and path loss (PL) are analyzed according to MPCs. Furthermore, a new path loss model is proposed. Finally, a new path loss model and relative analysis are provided.

Rolling bearing fault diagnosis based on Gramian angular difference field and improved channel attention model.

Fault diagnosis of rolling bearings is a critical task, and in previous research, convolutional neural networks (CNN) have been used to process vibration signals and perform fault diagnosis. However, traditional CNN models have certain limitations in terms of accuracy. To improve accuracy, we propose a method that combines the Gramian angular difference field (GADF) with residual networks (ResNet) and embeds frequency channel attention module (Fca) in the ResNet to diagnose rolling bearing fault. Firstly, we used GADF to convert the signals into RGB three-channel fault images during data preprocessing. Secondly, to further enhance the performance of the model, on the foundation of the ResNet we embedded the frequency channel attention module with discrete cosine transform (DCT) to form Fca, to effectively explores the channel information of fault images and identifies the corresponding fault characteristics. Finally, the experiment validated that the accuracy of the new model reaches 99.3% and the accuracy reaches 98.6% even under an unbalanced data set, which significantly improves the accuracy of fault diagnosis and the generalization of the model.