Noise Channel Research Articles

Design optimization of noise reduction for labyrinth control valve in secondary circuit flow regulation system

The labyrinth control valve has been widely used in the secondary circuit flow regulation system of nuclear power field for good pressure reduction and cavitation suppression characteristics. The design of the labyrinth channel is a key to realizing pressure regulation and cavitation suppression for the labyrinth control valve. However, an unreasonable labyrinth flow channel design will be challenging to meet the requirements and aggravate the noise problem under severe operating conditions. This paper proposes a hybrid labyrinth flow channel structure (HLS) that combines the traditional labyrinth flow channel (TLS) and orifice plate structure. The cavitation volume and maximum flow velocity are used as evaluation parameters of the labyrinth flow channel noise. The flow characteristics analysis show that compared with the TLS, the HLS can reduce flow velocity, decrease cavitation volume, improve transmission loss, and lower sound power levels. In addition, the influence between the HLS structural parameters and the noise evaluation parameters is clarified by correlation coefficient analysis. A low noise HLS based on the adaptive NSGA-II algorithm is established. The experimental results indicate that the optimized HLS reduces air noise by 4.21 dB(A) compared to the TLS. The optimized HLS can effectively reduce noise, and help the design of low noise labyrinth flow channels for nuclear power field.

Quantum differential privacy under noise channels

Quantum differential privacy is a way to protect privacy in quantum computing. It perturbs the final state of quantum computing by a specific quantum noise channel and makes the output useless to the attackers, so as to achieve privacy protection. In this paper, we study the quantum differential privacy under three channels: bit flip channel, phase flip channel and depolarizing channel. We calculate the privacy parameters for these three channels based on the proportional distance. We show the privacy protection capability by calculating the decay rates of the trace distances of two arbitrary quantum states before and after passing through the channels. Under the same privacy budget, we compare the fidelities, the trace distances between the real outputs and the outputs after entering the channels, and analyze the privacy protection performance of different quantum channels in detail.

Protecting quantum correlations of negative quantum states using weak measurement under non-Markovian noise

The weak measurement (WM) and quantum measurement reversal (QMR) are crucial in protecting the collapse of quantum states. The idea of WM and QMR has recently been used to protect and enhance quantum correlations and universal quantum teleportation (UQT) protocols. Here, we study the quantum correlations, maximal fidelity, and fidelity deviation of the two-qubit negative quantum states developed using discrete Wigner functions with(without) WM and QMR. To take into account the effect of a noisy environment, we evolve the states via non-Markovian amplitude damping and random telegraph noise quantum channels. To benchmark the performance of negative quantum states, we calculate their success probability. We compare our results with the two-qubit maximally entangled Bell state. Interestingly, we observe that some negative quantum states perform better with WM and QMR than the Bell state for different cases under evolution via noisy quantum channels.

New Golay decoding method using auto-encoder and OSD

In this study, we explore the potential of leveraging machine learning techniques, specifically auto-encoders (AE), for the decoding of linear block codes. Our findings suggest that this approach can outperform the conventional ordered statistics decoding (OSD) method, especially in a Rayleigh fading channel environment. We have rigorously trained the AE under both additive white Gaussian noise and Rayleigh fading channel conditions to ensure robustness in its performance. The output of the AE is combined with the received vector in a suitable manner to perform OSD. Through our experiments, we demonstrate that this proposed decoding approach yields better results than the conventional OSD method in Rayleigh fading channel when we used (23,12) Golay code.

Design of Indoor Visible Light Communication PAM4 System

Four-level pulse amplitude modulation (PAM4) can transmit more information in the same symbol interval, effectively improving the information transmission rate and frequency band utilization of visible light communication (VLC). This paper proposes a method for encoding and decoding the PAM4 signal based on FPGA. The PAM4 signal is studied and analyzed under different channel noise models. The research results show that the bit error rate (BER) performance of the PAM4 signal is the best under the additive noise model. The influence of the multiplicative noise on the BER of the PAM4 signal is much greater than that of the additive noise. The BER performance of the PAM4 signal depends on the variance of the noise data. The greater the variance, the worse the BER performance. At the same time, an indoor VLC system based on PAM4 is designed. The simulation results verify that the system scheme is feasible and provides a way to further realize high-speed signal transmission.

Finite blocklength feedback approach for multi-antenna wireless channels.

Ultra-reliable low-latency communication (URLLC) is a key technology in future wireless communications, and finite blocklength (FBL) coding is the core of the URLLC. In this paper, FBL coding schemes for the wireless multi-antenna channels are proposed, which are based on the classical Schalkwijk-Kailath scheme for the point-to-point additive white Gaussian noise channel with noiseless feedback. Simulation examples show that the proposed feedback-based schemes almost approach the corresponding channel capacities.

Contour wavelet diffusion – a fast and high-quality facial expression generation model

Facial expressions are important for conveying information in human interactions. The diffusion model can generate high-quality images for clearer and more discriminative faces, but its training and inference time is often prolonged, hampering practical application. Latent space diffusion models have shown promise in speeding up training by leveraging feature space parameters, but they require additional network structures. To address these limitations, we propose a contour wavelet diffusion model that accelerates both training and inference speeds. We use a contour wavelet transform to extract components from images and features, achieving substantial acceleration while preserving reconstruction quality. A normalised random channel attention module enhances the quality of generated images by focusing on high-frequency information. We also include a reconstruction loss function to enhance convergence speed. Experimental results demonstrate the effectiveness of our approach in boosting the training and inference speeds of diffusion models without sacrificing image quality. Fast generation of facial expressions can provide a smoother and more natural user experience, which is important for real-time applications. In addition, the increase in inference speed can save the use of computational resources, reduce system cost and improve energy efficiency, which is conducive to promoting the development and application of this technology.

On the performance of power-sensing RIS-SM: Effects of improper Gaussian noise and Nakagami-m fading channel

The innovative idea of data transmission through a reconfigurable intelligent surface (RIS) is a promising and exciting solution to the ever-present problems in wireless communications, such as energy consumption and hardware cost. The soft-controlled dynamic shaping of the electro-magnetic waves, which enables utilizing the randomness of the propagation medium to achieve more efficacious wireless channels, makes RIS a key enabler of 6G technology. Lately, space modulation techniques (SMTs) have often been discussed within the realm of the RIS-aided technologies due to their unique transmission characteristics. However, RIS-SMTs require more generalized and in-depth investigations to fully understand their potential in future generations. Accordingly, in this study, a recently proposed RIS-based SMT is generalized to inherently increase the spectral efficiency by utilizing spatial modulation (SM) procedure instead of space shift keying (SSK). The novel system called power-sensing RIS-SM is analyzed in the presence of improper Gaussian noise (IGN) to discover system behavior in the case of being corrupted by an improper interfering signal over Nakagami-m channels, which adapt closely to the most of measured fading channels. The success of the proposed RIS-aided SMT is assessed in terms of the average bit error rate (ABER), which is both calculated by analytical derivations and verified by computer simulations.

Performance analysis of GFSK modulation over AWGN channel

This paper analyses the performance of Gaussian Frequency Shift Keying (GFSK) modulation in Additive White Gaussian Noise (AWGN) channels. Using MATLAB 2022b for simulations, the study explores GFSK modulation principles, the role of Gaussian low-pass filters, the characteristics reflected by Power Spectral Density (PSD), and the influence of Bit Time-Bandwidth Product (BT) on Bit Error Rate (BER). It has been observed that the Gaussian filter restricts sidelobe amplitudes and enhances spectral efficiency. Through varying BT values, it is observed that higher BT values correlate with lower BER. Additionally, the study successfully reconstructs original baseband signals through sampling decisions, confirming GFSK modulation effectiveness.

Primitivity for random quantum channels

Primitivity for random quantum channels

Iterative learning control for differential inclusion systems with random fading channels by varying average technique.

The aim of this paper is to study iterative learning control for differential inclusion systems with random fading channels between the plant and the controller. In reality, the phenomenon of fading will inevitably occur in network transmission, which will greatly affect the tracking ability of output trajectory. This study discusses the impact of fading channel on tracking performance at the input and output sides, respectively. First, a set-valued mapping in a differential inclusion system with uncertainty is converted into a single-valued mapping by means of a Steiner-type selector. Then, to offset the effect of the fading channel and improve the tracking ability, a variable local average operator is constructed. The convergence of the learning control algorithm designed by the average operator is proved. The results show that the parameters in the varying local average operator can be adjusted to trade-off between the learning rate and the fading offset rate. Finally, the theoretical results are verified by numerical simulation of the switched reluctance motors.

Tracking Performance of Feedback Systems Over a Fading Channel With Limited Bandwidth.

In this study, we investigated the optimal tracking performance (OTP) of feedback control systems with limited bandwidth and colored noise in a fading channel. For the steady state of the feedback control systems, an equivalent average channel (EAC) model was developed by retaining the effects of the first and second moments of the multiplicative channel output, and on the basis of the coprime decomposition, all-pass factorization, and Youla parameterization of controllers, exact expressions for the OTP were derived by designing two compensators. The expressions quantitatively show the relationship between the OTP and inherent features of the plant. Specifically, the directions and locations of unstable poles (UPs) and nonminimum phase (NMP) zeros adversely affect the tracking performance. Furthermore, the bandwidth limitation and the presence of colored noise also degrade the tracking performance. Finally, our conclusions were verified by considering a numerical arithmetic example.

Numerical approach to compound quantum repeater scheme with coherent states

A numerical model of a quantum repeater operating with Schrödinger cat states is constructed. The model describes the performance of such a system in the presence of decoherence effects, namely, noise in the quantum channel and the efficiency of the photon-number-resolving detector. In the framework of the numerical model, a theoretical analysis of the system functioning is carried out for the elementary link by calculating its performance characteristics. Namely, we calculate photodetector click probabilities and fidelity for various sets of decoherence parameters. These estimates are necessary in the context of further experimental research at the junction with other branches of quantum communications, so that to use the entanglement distribution when it comes to operating quantum teleportation and quantum key distribution protocols based on entanglement. The model will be developed further as a versatile drag-and-drop software simulating the full-fledged entanglement swapping protocol operation.

Optimal Tracking Performance of Networked Control Systems Under Communication Channel Noise

Optimal Tracking Performance of Networked Control Systems Under Communication Channel Noise

A denoiser for correlated noise channel decoding: Gated-neural network

A denoiser for correlated noise channel decoding: Gated-neural network

Deep-learning-based airborne transient electromagnetic inversion providing the depth of investigation

We develop an integrated workflow that uses deep-learning (DL)-based approaches for processing and inverting airborne transient electromagnetic (ATEM) data. Our novel workflow automates these preprocessing steps and enables real-time inversion in the field. Thus, we develop an entire inversion workflow using three DL networks that cover all steps from preprocessing to imaging. The preprocessing DL network performs interpolation to discard data that are severely noise contaminated and suppress the effects of noise in a late-time channel. We use an inversion DL network and a depth of investigation (DOI) network to generate images of subsurface resistivities exclusively within the DOI range where reliable predictions can be made. To optimize the inversion process, our approach focuses on designing the inversion DL network to simultaneously minimize data misfit and model misfit. By addressing these two aspects, we ensure a more robust outcome in the final resistivity images. The practical applicability of the workflow is verified by comparing the imaging results of the field data with those of conventional inversion and geologic interpretation. Each workflow is nearly automatic and very fast; we expect that our workflow will contribute to the development of real-time imaging software for the ATEM survey, which expands the applications of the ATEM survey in various fields.

Performance Analysis of Subcarrier Index Modulation OFDM in a DWDM-FSO link

Free Space Optics (FSO) is a wireless optical communication system using light to establish high-capacity links between points without the need for physical cables. However, its susceptibility to atmospheric conditions such as fog, rain, and turbulence presents challenges to signal quality maintenance. To address this, Dense Wavelength Division Multiplexing (DWDM), traditionally employed in fiber optics, is leveraged to optimize bandwidth utilization and scalability in high-capacity communications. The integration of DWDM into FSO systems offers redundancy and resilience against signal degradation induced by atmospheric conditions. To the best of our knowledge, this is the first time a paper explores the performance of the Subcarrier Index Modulation Orthogonal Frequency Division Multiplexing (SIM-OFDM) technique within a DWDM-FSO system. The objective is to reinforce FSO link robustness and improve transmission rates. Through a comparative analysis with conventional OFDM in an Additive White Gaussian Noise (AWGN) channel, the study reveals a notable Signal-to-Noise Ratio (SNR) gain of at least 3dB with enhanced SIM-OFDM (ESIM-OFDM). Simulations, conducted with realistic parameters within the DWDM-FSO architecture, demonstrate that ESIM-OFDM modulation enables a transmission range of 2 km with an aggregated rate of 10.1 Gb/s. These results underscore the potential of SIM-OFDM in enhancing FSO system performance through the integration of DWDM technology. The proposed ESIM-OFDM DWDM-FSO scenario could represent a highly interesting solution for establishing 5G Backhaul links.

Phase noise of a microwave photonic channel: direct-current versus external electro-optic modulation

We characterize the phase noise of a microwave photonic channel, where a 10 GHz signal is carried by an intensity-modulated light beam over a short optical fiber, and detected. Two options are compared: (i) an electro-optic modulator (EOM), and (ii) the direct modulation of the laser current. The 1.55 µm laser and the detector are the same. The effect of experimental parameters is investigated, the main being the microwave power and the laser bias current. The main result is that the upper bound of the phase flicker is −117dBrad2 in the case of the EOM, limited by the background noise of the setup. In contrast, with direct modulation of the laser, the flicker is of −114 to −100dBrad2, depending on the laser bias current (50–90 mA), and the highest noise occurs at the lowest bias. Our results are of interest in communications, radar systems, instrumentation, and metrology.

Quantum Secure Multi-Party Summation with Graph State.

Quantum secure multi-party summation (QSMS) is a fundamental problem in quantum secure multi-party computation (QSMC), wherein multiple parties compute the sum of their data without revealing them. This paper proposes a novel QSMS protocol based on graph state, which offers enhanced security, usability, and flexibility compared to existing methods. The protocol leverages the structural advantages of graph state and employs random graph state structures and random encryption gate operations to provide stronger security. Additionally, the stabilizer of the graph state is utilized to detect eavesdroppers and channel noise without the need for decoy bits. The protocol allows for the arbitrary addition and deletion of participants, enabling greater flexibility. Experimental verification is conducted to demonstrate the security, effectiveness, and practicality of the proposed protocols. The correctness and security of the protocols are formally proven. The QSMS method based on graph state introduces new opportunities for QSMC. It highlights the potential of leveraging quantum graph state technology to securely and efficiently solve various multi-party computation problems.

Low-Frequency Blast Detection Using a Large-N Dark Fiber in Noisy Environments: Template Matching and Optimal Channel Selection

Abstract Distributed acoustic sensing (DAS), deployed on dark telecom fiber, is well-positioned to play a significant role in seismic monitoring networks because of the combination of a large aperture, fine spatial resolution, broadband sensitivity, and the ubiquitous presence of unused telecommunication fibers in many areas of the world. In this study, we explore the feasibility of dark-fiber array deployed in a noisy environment for detecting small explosions. We test the effectiveness of template matching for the detection of low-frequency blasts generated by mining activities in the Imperial Valley, California. We first evaluate dark-fiber detection performance by analyzing the relationship between detection threshold (DT) and the number of DAS channels used. We find that although, as expected, increasing the number of channels yields higher detection significance and lowers DT, the gain in performance is far from linear, with local anomalies across the DAS cable associated with zones of higher noise. We focus on investigating the types of noise affecting template matching and practical approaches mitigating anthropogenic noise that lower detection performance. Using median absolute deviation, we identify two types of noise sources affecting detection performance. Next, we design a voting scheme that selects DAS channels contributing to lowering of the DT and ensures improvement in detection when adding sequential channels. Finally, we compare dark-fiber detection performance with nearby conventional seismometers and find that a single station can outperform up to ∼10 DAS channels. However, using the full aperture of our dark-fiber transect allows to obtain ∼10% lower DT and yields fewer false-positive detections than an array of four seismometers. Methodological solutions for noise assessment and channel selection allow us to fully benefit from the large aperture and dense sampling offered by dark fiber. The findings of this study are a step toward incorporating existing telecom fibers into novel explosion-monitoring workflows.