Noisy Channel Research Articles

Adaptive tunicate swarm optimization with partial transmit sequence for phase optimization in MIMO-OFDM

<span>Multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) are widely utilized in wireless systems and maximum data rate communications. The MIMO-OFDM technology increases the efficiency of spectrum utilization. The peak-to-average-power ratio (PAPR) minimization in MIMO-OFDM is a complex task in wireless communications systems. In this research, an adaptive tunicate swarm optimization with partial transmit sequence (ATSO-PTS) algorithm is proposed for a reduction of PAPR in MIMO-OFDM. The nonsquare-matrix-based differential space time coding (N-DSTC) scheme is used for the encoding and decoding process of MIMO-OFDM. The N-DSTC encoding and decoding are linear error-correcting codes that are utilized for message transmission over noisy channels. The pre-specified quadrate phase shift keying (QPSK) symbol is deployed for the modulation and demodulation scheme. On the receiver side, the serial to parallel (S/P) conversion, and fast Fourier transform (FFT) are accomplished, alongside the received data bits being demodulated to obtain the output bits. The proposed ATSO-PTS method achieves better results according to performance metrices PAPR, bit error rate (BER) and signal-to-noise-ratio (SNR), with values of about 2.9, 0.01 and 0.025, respectively. This ensures superior results when compared to the existing methods of twin symbol hybrid optimization applied to partial transmit sequence (TSHO-PTS), selective level mapping and PTS (SLM-PTS), and particle swarm and grey wolf (PS-GW) with PTS, respectively.</span>

Examining the quantum fisher information in the interaction of a dirac system with a squeezed generalized amplitude damping channel.

The inherent association between real quantum systems and their surrounding environment invariably results in decoherence, leading to the loss of entanglement. This diminution in entanglement coincides with a decline in the fidelity of transmitted information using the entangled quantum resource. This study scrutinizes the impact of the squeezed generalized amplitude damping (SGAD) channel on quantum Fisher information (QFI) parameters. The SGAD channel model, a versatile framework, is also employed to simulate other dissipative channels, including amplitude damping (AD) and generalized amplitude damping (GAD). Kraus operators facilitate the modeling of noisy channels. The results reveal that, within the SGAD channel, the QFI remains impervious to the squeezing variables (r and ). In the GAD channel, undergoes enhancement to a constant value with an upswing in temperature (T), while the parameter in the GAD channel, , akin to the SGAD channel, surges around T = 2 before complete loss ensues. Concerning the AD channel, the component of the QFI initially experiences decoherence with an augmentation in the AD noise parameter ( ). Subsequently, it is restored to its initial value with a further escalation in . Conversely, the component of the QFI in the AD channel experiences decoherence with an elevation in the AD noise parameter ( ).

Bidirectional quantum controlled teleportation in multi-hop networks: a generalized protocol for the arbitrary n-qubit state through the noisy channel

Bidirectional quantum controlled teleportation in multi-hop networks: a generalized protocol for the arbitrary n-qubit state through the noisy channel

Performance Comparison of Selected Filters in Fast Denoising of Oil Palm Hyperspectral Data

Usually, hyperspectral data captured from an airborne UAV or satellite contain some noise that can be severe in some channels. Often, channels that are badly affected by the noise are discarded. This is because the corrupted channels cannot be reclaimed by common filtering techniques, making important information in the affected channels different from those of field spectroscopy of similar wavelengths. In this study, a fast-denoising method is implemented on some channels of oil palm hyperspectral data that are badly affected by noise. The amount of noise is unknown, and it varies across the noisy channels from bad to severe. This is different from the data normally used by many studies, which are essentially clean data spiked with mild noise of known variance. The process starts by identifying which noisy channels to filter based on the level of the estimated noise in them. Then, filtering is conducted within each channel and across channels. Once the noise is removed, the improvement in signal-to-noise ratio (SNR) is calculated for each channel. The performance of Kalman, Wiener, Savitzky–Golay, wavelet, and cosine filters is tested in the same framework and the results are compared in terms of execution time, signal-to-noise ratio, and visual quality. The results show that the Kalman filter slightly outperformed the other filters. The proposed scheme was implemented using MATLAB R2023b running on an Intel i7 processor, and the average execution time was less than 1 second for each channel. To the best of our knowledge, this is the first attempt to filter real oil palm hyperspectral data containing speckle noise using a Kalman filter. This technique can be a useful tool to those working in the oil palm industry.

Extending the understanding of Shannon’s safe stimulation limit for platinum electrodes: biphasic charge-balanced pulse trains in unbuffered saline at pH = 1 to pH = 12

Objective.In neural electrical stimulation, safe stimulation guidelines are essential to deliver efficient treatment while avoiding neural damage and electrode degradation. The widely used Shannon's limit,k, gives conditions on the stimulation parameters to avoid neural damage, however, underlying damage mechanisms are not fully understood. Moreover, the translation from bench testing toin vivoexperiments still presents some challenges, including the increased polarisation observed, which may influence charge-injection mechanisms. In this work, we studied the influence on damage mechanisms of two electrolyte parameters that are differentin vivocompared to usual bench tests: solution pH and electrolyte gelation.Approach.The potential of a platinum macroelectrode was monitored in a three-electrode setup during current-controlled biphasic charge-balanced cathodic-first pulse trains. Maximum anodic and cathodic potential excursions during pulse trains were projected on cyclic voltammograms to infer possible electrochemical reactions.Main results.In unbuffered saline of pH ranging from 1 to 12, the maximum anodic potential was systematically located in the oxide formation region, while the cathodic potential was located the molecular oxygen and oxide reduction region whenkapproached Shannon's damage limit, independent of solution pH. The results support the hypothesis that Shannon's limit corresponds to the beginning of platinum dissolution following repeated cycles of platinum oxidation and reduction, for which the cathodic excursion is a key tipping point. Despite similar potential excursions between solution and gel electrolytes, we found a joint influence of pH and gelation on the cathodic potential alone, while we observed no effect on the anodic potential. We hypothesise that gelation creates a positive feedback loop exacerbating the effects of pH ; however, the extent of that influence needs to be examined further.Significance.This work supports the hypothesis of charge injection mechanisms associated with stimulation-induced damage at platinum electrodes. The validity of a major hypothesis explaining stimulation-induced damage was tested and supported on a range of electrolytes representing potential electrode environments, calling for further characterisation of platinum dissolution during electrical stimulation in various testing conditions.

Preservation of entanglement in local noisy channels

Preservation of entanglement in local noisy channels

Hyperentanglement quantum communication over a 50 km noisy fiber channel

High-dimensional entanglement not only offers a high security level for quantum communication but also promises improved information capacity and noise resistance of the system. However, due to various constraints on different high-dimensional degrees of freedom, whether these advantages can bring improvement to the actual implementation is still not well proven. Here we present a scheme to fully utilize these advantages over long-distance noisy fiber channels. We exploit polarization and time-bin hyperentanglement to achieve high-dimensional coding, and observe significant enhancements in secure key rates and noise tolerance that surpass the capabilities of qubit systems. Moreover, the demonstration achieves a distribution up to 50 km, which is the longest distance for high-dimensional entanglement-based quantum key distribution up to date, to our knowledge. Our demonstration validates the potential of high-dimensional entanglement for quantum communications over long-distance noisy channels, paving the way for a resilient and resource-efficient quantum network.

Quantum state reconstruction in a noisy environment via deep learning

Quantum noise is currently limiting efficient quantum information processing and computation, impacting on the fidelity and reliability of quantum states. In this work, we consider the tasks of reconstructing and classifying quantum states corrupted by the action of an unknown noisy channel using classical feed-forward neural networks. By framing reconstruction as a regression problem, we show how such an approach can be used to recover with fidelities exceeding 99% the noiseless density matrices of quantum states of up to three qubits undergoing noisy evolution, and we test its performance with both single-qubit (bit-flip, phase-flip, depolarizing, and amplitude damping) and two-qubit quantum channels (correlated amplitude damping). Furthermore, a critical aspect of our investigation involves also a comprehensive comparison between mean squared error and infidelity as loss functions. Our findings reveal that these two metrics yield comparable results in the context of state reconstruction. Moreover, we also consider the task of distinguishing between different quantum noisy channels, and show how a neural network-based classifier is able to solve such a classification problem with perfect accuracy.

Probing quantum discord and coherence dynamics in the discrete-time quantum walk under generalized amplitude damping noise channel

We investigate the behaviour of one-dimensional dissipative Hadamard discrete-time quantum walk (DTQW) in the generalized amplitude damping channel. By manipulating the noise intensity, we uncover intriguing dynamics in the coherence of the coin state such as rebounding and freezing phenomena. The entanglement between the quantum walker’ states diminishes at lower dissipation rates despite the pronounced coherence. The non-classical characters of the dissipative DTQW becomes evident through the manifestation of quantum discord. Our study shows that even under maximum dissipation stemming from both zero and finite-temperature environment, the quantum walker retains rudimentary non-classical behaviours. It is shown that fine-tuning the parameter governing the coin rotation angle optimizes the quantum discord of the walker. These results are useful for optimizing quantum walks in the presence of noisy channels.

All-optical correlated noisy channel and its application in recovering quantum coherence

All-optical correlated noisy channel and its application in recovering quantum coherence

Entanglement Protection Using Weak Measurement in Non‐Markovian Environment

AbstractIn this work, a scheme to protect quantum entanglement from a non‐Markovian noisy environment is proposed. By applying two quantum weak measurements before and after sending the quantum state into the noisy channel, the quantum state can be “pushed” closer to a decoherence‐free state, reducing decoherence during the time evolution. Then, the second weak measurement can partially retrieve the initial quantum state from the state corrupted by the noisy environment. The study involves a non‐Markovian dynamic equation to examine the impact of the memory effect on the protection scheme's performance. Various factors affecting the residual entanglement and the success probability are analyzed. The results suggest that two measurement strengths shall be chosen approximately in a linear relation, with the best ratio determined by the memory time of the environment. Additionally, it is demonstrated that the memory effect significantly enhances the protection efficiency. Lastly, the scheme's robustness against systematic errors is evaluated.

Optimized decoder for low-density parity check codes based on genetic algorithms

Low-density parity check (LDPC) codes, are a family of error-correcting codes, their performances close to the Shannon limit make them very attractive solutions for digital communication systems. There are several algorithms for decoding LDPC codes that show great diversity in terms of performance related to error correction. Also, very recently, many research papers involved the genetic algorithm (GA) in coding theory, in particular, in the decoding linear block codes case, which has heavily contributed to reducing the bit error rate (BER). In this paper, an efficient method based on the GA is proposed and it is used to improve the power of correction in terms of BER and the frame error rate (FER) of LDPC codes. Subsequently, the proposed algorithm can independently decide the most suitable moment to stop the decoding process, moreover, it does not require channel information (CSI) making it adaptable for all types of channels with different noise or intensity. The simulations show that the proposed algorithm is more efficient in terms of BER compared to other LDPC code decoders.

Channel-optimized quantum communication scheme based on combination code

In this paper, a channel-optimized quantum communication scheme with imperfect Bell states is being considered, based on quantum teleportation and error correction codes technology. In this scheme, a novel “combination code” technique is proposed to protect the pre-shared Bell states from storage errors and to safeguard the information-encoded states from noisy channels. Ultimately, it is revealed by Monte Carlo simulation results that the channel fidelity of the proposed channel-optimized quantum communication scheme with imperfect Bell states is significantly advantageous over standard quantum error-correcting code (QECC) and entanglement-assisted quantum error-correcting code (EAQECC) communication schemes.

Rate-Distortion-Perception Optimized Neural Speech Transmission System for High-Fidelity Semantic Communications.

We consider the problem of learned speech transmission. Existing methods have exploited joint source-channel coding (JSCC) to encode speech directly to transmitted symbols to improve the robustness over noisy channels. However, the fundamental limit of these methods is the failure of identification of content diversity across speech frames, leading to inefficient transmission. In this paper, we propose a novel neural speech transmission framework named NST. It can be optimized for superior rate-distortion-perception (RDP) performance toward the goal of high-fidelity semantic communication. Particularly, a learned entropy model assesses latent speech features to quantify the semantic content complexity, which facilitates the adaptive transmission rate allocation. NST enables a seamless integration of the source content with channel state information through variable-length joint source-channel coding, which maximizes the coding gain. Furthermore, we present a streaming variant of NST, which adopts causal coding based on sliding windows. Experimental results verify that NST outperforms existing speech transmission methods including separation-based and JSCC solutions in terms of RDP performance. Streaming NST achieves low-latency transmission with a slight quality degradation, which is tailored for real-time speech communication.

Noisy Werner-Holevo channel and its properties

Noisy Werner-Holevo channel and its properties

Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG.

Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain-computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain-computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III-IV(A) and BCI competition IV-I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques.

A decentralized HC-ADMM approach for large antenna arrays

This work addresses the evolving landscape of internet of things (IoT) applications and large antenna array systems, where optimizing spectral efficiency and simplifying design complexities are crucial. Focusing on two key challenges, the study introduces a novel hybrid analog-digital transceiver strategy tailored for frequency-selective channels. By integrating Shannon and Hartley theorems, the approach enhances data transfer rates, thereby optimizing radio frequency (RF) chain utilization in large-scale antennas. To achieve a balance between transceiver performance and hardware complexity, the study employs a decentralized alternating direction method of multipliers (ADMM) framework. The proposed hybrid consensus ADMM algorithm (HC-ADMM) ensures efficient convergence in decentralized optimization scenarios. Comparative analyses with ADMM and existing system transceiver optimization (ESTO) models highlight HC-ADMM's superior performance across key metrics such as spectral efficiency, efficiency per cell, total efficiency, and optimal scheduling of user equipment (UEs). Particularly notable is HC-ADMM's advanced optimization capabilities as the number of transmit antennas increases, positioning it as a promising approach for enhancing overall communication network performance.

Comparative analysis of coding schemes for effective wireless communication

Communication systems have recently focused on sending information efficiently and effectively from one sending point to another across a communication channel in the shortest amount of time. The main objective of this work is to compare the high-range coding scheme types, such as low density parity check (LDPC), turbo, and convolution, to see which works better and is more efficient. to establish a coding system with quadrature amplitude modulation (QAM) modulation and an additive white gaussian noise (AWGN) noisy channel to find which is more reliable and resilient for encoding and decoding. Because of this, digital media has to be sent over wireless channels and through satellites, requiring a connected network all the time, which has become a major concern over time. Furthermore, the high amount of data and efficiency are the focus points. After running the simulation, it was found that 64 QAM with a rate of 0.455 and an efficiency of 2.731 has a bit error rate (BER) of 0.001 and a 7.08 dB energy per bit Eb/No, and the 256 QAM simulation revealed that it has a BER of 0.001 and 11.88 dB Eb/No with a rate of 0.736 and an efficiency of 5.891. Over the AWGN channel noise, the simulation built a standard orthogonal frequency division multiplexing (OFDM) system, which used MATLAB.

Building and evaluating the performance of a digital data transmission system over a noisy Rician fading channel using a 16-QAM modulator, Hamming source encoding, and BCH channel encoding

In today's digital age, digital data transmission methods are becoming popular. In a wireless communication environment, interference can cause data loss and distortion. In this paper, the author builds and evaluates the performance of a data transmission system using a noisy Rician fading channel. This system combines the use of 16-QAM modulators, Hamming source coding, and BCH channel coding to improve the reliability of data transmission. The performance evaluation of the system is conducted under different interference conditions. The results show that the proposed system is capable of detecting and correcting errors in transmitted data and has good anti-interference ability.

A Method of Reducing Errors Due to Sampling in the Measurement of Electric Power

Although data acquisition is a very usual technique, several aspects are not always considered, such as the synchronization of the acquired measures and the evaluation of the resulting errors. This paper aims to highlight this fact by the mathematical determination of the necessary correction and the implementation of software meant to evaluate the performances of acquisition systems. As an example, a three-phased acquisition system was developed in order to monitor the currents and voltages on the three phases. Also, other measures were performed, such as of power and phase. The components on each phase did not have to be fully identified because a whole system calibration could be performed in the first stage. The calibration consisted in finding the weighting coefficients for each measured quantity. The implemented solution for three-phased measure acquisition started from the hypothesis of a sampling frequency that respected the Shannon theorem. The distance between two samples was small enough to consider a linear evolution between two moments for the same measure. Errors that affected the above-mentioned measures, due to the fact that the samples were examined in different moments, were analyzed and brought to the minimum value. Finding a solution to reduce the sampling errors is closely related to reducing the costs.