Channel Noise Level Research Articles

Maximum Achievable Rate of Resistive Random-Access Memory Channels by Mutual Information Spectrum Analysis

The maximum achievable rate is derived for resistive random-access memory (ReRAM) channel with sneak-path interference. Based on the mutual information spectrum analysis, the maximum achievable rate of ReRAM channel with independent and identically distributed (i.i.d.) binary inputs is derived as an explicit function of channel parameters such as the distribution of cell selector failures and channel noise level. Due to the randomness of cell selector failures, the ReRAM channel demonstrates multi-status characteristic. For each status, it is shown that as the array size is large, the fraction of cells affected by sneak paths approaches a constant value. Therefore, the mutual information spectrum of the ReRAM channel is formulated as a mixture of multiple stationary channels. Maximum achievable rates of the ReRAM channel with different settings, such as single- and across-array codings, with and without data shaping, and optimal and treating-interference-as-noise (TIN) decodings, are compared. These results provide valuable insights on the code design for ReRAM.

Minimum Energy Analysis for Robust Gaussian Joint Source-Channel Coding With a Distortion-Noise Profile

Minimum energy required to achieve a distortion-noise profile, i.e., a function indicating the maximum allowed distortion value for each channel noise level, is studied for transmission of Gaussian sources over Gaussian channels. A general coding scheme is proposed to upper bound the minimum energy needed for any distortion-noise profile. Conversely, utilizing a family of lower bounds originally derived for broadcast channels with power constraints, the minimum required energy is lower bounded for any profile. As examples, linear, exponential, square-law, and staircase profiles are studied. It is shown that for the linear profile, as well as for any concave profile, uncoded transmission is optimal. As a negative result, it is shown that exponential profiles are not achievable with finite energy. For square-law and staircase profiles, upper bounds and lower bounds are derived and the gaps between them are studied.

Sample-Grouping-Based Vector Quantization for Secret Key Extraction From Atmospheric Optical Wireless Channels

Vector quantization is a viable solution to the problem as to full utilization of randomness provided by statistically dependent channel measurements in secret key extraction from randomly fluctuating channels. By aiming at atmospheric optical wireless channels, new vector quantization schemes based on sample grouping (SG) are proposed by using different ways, specifically, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K\text {-dimensional}$ </tex-math></inline-formula> tree (KDT) partition and random placement, to separate samples of the dimension-reduced channel coefficient vector into a given number of groups, with each containing equal number of samples. Use of the KDT partition leads to both the basic KDT-partition-based quantization (B-KDTPQ) and improved KDT-partition-based quantization (I-KDTPQ) schemes. On the other hand, utilization of the random placement brings forth the random equisized grouping based quantization (REGQ) scheme. Performance evaluation shows that, due to use of quantization symbol modification, the I-KDTPQ scheme always has lower quantization symbol disagreement rate (QSDR) than the B-KDTPQ scheme; compared with the I-KDTPQ scheme, the REGQ scheme has more flexibility in accommodating itself to various conditions of channel coefficient fluctuations and noise levels.

Adaptive MIMO Detector Based on Hypernetwork: Design, Simulation, and Experimental Test

Algorithm unfolding, which provides a systematic connection between conventional model-based algorithms and modern data-based deep learning, has exhibited great empirical success for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. However, existing unfolding-based MIMO detectors have difficulties adapting to the high discrepancy in channel and noise conditions. In this study, we present a novel unfolding-based framework for MIMO detectors, which can automatically determine internal parameters of an unfolding-based MIMO detector to adapt to the varying conditions. A key part of our approach is to develop a hypernetwork that can effectively learn to generate the internal parameters in the sophisticated expectation propagation-based MIMO detector. In particular, we design long short-term memory-based hypernetwork to ensure the flexibility of the layers of the unfolded algorithm. The proposed framework is also extended to a coded MIMO turbo receiver to adapt to the different feedback beliefs from the decoder. Numerical results demonstrate that the proposed MIMO detectors have excellent adaptation capability to different channel environments and noise levels. Compared with the existing unfolded algorithm that is an optimal reference, the proposed framework avoids frequent retraining and presents the nearly optimal performance in uncoded and coded MIMO systems. An over-the-air platform is presented as well to demonstrate the significant robustness of the proposed receivers in practical deployment.

Algorithm for Estimation of Signal Carrier Frequency by Distributed Sync Combination, Optimal According to the Criterion of the Minimum Mean Square Error

An algorithm for accurate estimation of the carrier frequency, which is optimal by the criterion of the min-imum mean square error, is developed which is implemented using relatively short sync combinations distributed over the duration of the packet. The results of the dependence of the accuracy of estimating the carrier frequency on the level of channel noise are presented. Analytical expressions are obtained for an accurate estimate of the frequency and phase errors obtained on the basis of sufficiently short sync combinations. The results of simulation are demon-strated. The research results can be used in satellite radio links with packet data transmission.

Neural network-featured timing systems for radiation detectors: performance evaluation based on bound analysis

Waveform sampling systems are used pervasively in the design of front end electronics for radiation detection. The introduction of new feature extraction algorithms (eg. neural networks) to waveform sampling has the great potential to substantially improve the performance and enrich the capability. To analyze the limits of such algorithms and thus illuminate the direction of resolution optimization, in this paper we systematically simulate the detection procedure of contemporary radiation detectors with an emphasis on pulse timing. Neural networks and variants of constant fraction discrimination are studied in a wide range of analog channel frequency and noise level. Furthermore, we propose an estimation of multivariate Cramér Rao lower bound within the model using intrinsic-extrinsic parametrization and prior information. Two case studies (single photon detection and shashlik-type calorimeter) verify the reliability of the proposed method and show it works as a useful guideline when assessing the abilities of various feature extraction algorithms.

Application of DCT to problem of collision resolution in RFID SAW systems by using correlation method

The article is devoted to SAW RFID systems. A problem of collision resolution by using a correlation method is under consideration. Collision is resolves by means of two correlation features, by the correlation function of the signal and by the correlation function of the DCT from the signal. A program, which is implements such collision resolution algorithm, was written. A collision is creating by using a simulation model of RFID tag. The model can generate a response from tag with «own» or with a random code, or can generate a collision of responses with random codes, which may contain or not contain an «own» code. The results of the algorithm’s operation are presented for the cases of reading from two to ten tags simultaneously. In addition to the main function, the algorithm implements error-correcting coding and decoding functions. It allows eliminating errors in case when there is no collision of tags, but a level of channel noise is high. The error-correcting coding is implemented by Reed-Solomon codes.

Application of the generalized contrast-to-noise ratio to assess photoacoustic image quality.

The generalized contrast-to-noise ratio (gCNR) is a relatively new image quality metric designed to assess the probability of lesion detectability in ultrasound images. Although gCNR was initially demonstrated with ultrasound images, the metric is theoretically applicable to multiple types of medical images. In this paper, the applicability of gCNR to photoacoustic images is investigated. The gCNR was computed for both simulated and experimental photoacoustic images generated by amplitude-based (i.e., delay-and-sum) and coherence-based (i.e., short-lag spatial coherence) beamformers. These gCNR measurements were compared to three more traditional image quality metrics (i.e., contrast, contrast-to-noise ratio, and signal-to-noise ratio) applied to the same datasets. An increase in qualitative target visibility generally corresponded with increased gCNR. In addition, gCNR magnitude was more directly related to the separability of photoacoustic signals from their background, which degraded with the presence of limited bandwidth artifacts and increased levels of channel noise. At high gCNR values (i.e., 0.95-1), contrast, contrast-to-noise ratio, and signal-to-noise ratio varied by up to 23.7-56.2 dB, 2.0-3.4, and 26.5-7.6×1020, respectively, for simulated, experimental phantom, and in vivo data. Therefore, these traditional metrics can experience large variations when a target is fully detectable, and additional increases in these values would have no impact on photoacoustic target detectability. In addition, gCNR is robust to changes in traditional metrics introduced by applying a minimum threshold to image amplitudes. In tandem with other photoacoustic image quality metrics and with a defined range of 0 to 1, gCNR has promising potential to provide additional insight, particularly when designing new beamformers and image formation techniques and when reporting quantitative performance without an opportunity to qualitatively assess corresponding images (e.g., in text-only abstracts).

The Effectiveness of Distortion-Type PAPR and Total Degradation Reduction in OFDM Systems

Conventional peak-to-average power ratio (PAPR) reduction metrics including bit error rate (BER) and complementary cumulative distribution function (CCDF) can hardly reveal the overall effectiveness of a distortion-type PAPR technique. To remedy this drawback, a new overall performance index, BER at various power consumption of a given HPA and channel noise level, is proposed. Furthermore, a novel triangular companding (TC) technique is also introduced to improve the system performance. Simulations demonstrate that the TC scheme outperforms other schemes in case of the proposed overall performance index and the conventional performance metrics for PAPR reduction.

ViLDAR—Visible Light Sensing-Based Speed Estimation Using Vehicle Headlamps

The introduction of light emitting diodes (LED) in automotive exterior lighting systems provides opportunities to develop viable alternatives to conventional communication and sensing technologies. Most of the advanced driver-assist and autonomous vehicle technologies are based on Radio Detection and Ranging (RADAR) or Light Detection and Ranging (LiDAR) systems that use radio frequency or laser signals, respectively. While reliable and real-time information on vehicle speeds is critical for traffic operations management and autonomous vehicles safety, RADAR or LiDAR systems have some deficiencies especially in curved road scenarios where the incidence angle is rapidly varying. In this paper, we propose a novel speed estimation system so-called the Visible Light Detection and Ranging (ViLDAR) that builds upon sensing visible light variation of the vehicle's headlamp. We determine the accuracy of the proposed speed estimator in straight and curved road scenarios. We further present how the algorithm design parameters and the channel noise level affect the speed estimation accuracy. For wide incidence angles, the simulation results show that the ViLDAR outperforms RADAR/LiDAR systems in both straight and curved road scenarios.

Semiquantum key distribution with high quantum noise tolerance

Semi-quantum key distribution protocols are designed to allow two parties to establish a shared secret key, secure against an all-powerful adversary, even when one of the users is restricted to measuring and preparing quantum states in one single basis. While interesting from a theoretical standpoint, these protocols have the disadvantage that a two-way quantum communication channel is necessary which generally limits their theoretical efficiency and noise tolerance. In this paper, we construct a new semi-quantum key distribution (SQKD) protocol which actually takes advantage of this necessary two-way channel, and, after performing an information theoretic security analysis against collective attacks, we show it is able to tolerate a channel noise level higher than any prior SQKD protocol to-date. We also compare the noise tolerance of our protocol to other two-way fully quantum protocols, along with BB84 with Classical Advantage Distillation (CAD). We also comment on some practical issues involving semi-quantum key distribution (in particular, concerning the potential complexity in physical implementation of our protocol as compared with other standard QKD protocols). Finally, we develop techniques that can be applied to the security analysis of other (S)QKD protocols reliant on a two-way quantum communication channel.

Multi-channel expected patch log likelihood for color image denoising

Due to its flexibility and good restoration performance, the Expected Patch Log Likelihood (EPLL) method has attracted extensive attention and has been further developed. However, the basic EPLL method is mainly applied for gray image restoration. For color image denoising with different channel noise levels, concatenating the RGB values into a vector and applying the basic EPLL directly can produce false colors and artifacts. In this paper, a Multi-Channel Expected Patch Log Likelihood (MC-EPLL) method is proposed for color image denoising with different channel noise levels. Considering the within and between channel correlation, the noise model of the concatenated vector of RGB channels can be constructed as a Matrix Normal Distribution. Under the KL divergence framework, the MC-EPLL model can be derived by combining the noise model and Gaussian Mixture Model (GMM) based patch prior. Based on the half quadratic splitting (HQS) strategy, the MC-EPLL model is decomposed into two sub-minimization problems and the closed-form solution of each sub-problem can be obtained. Experiments show the feasibility and superiority of the proposed MC-EPLL over the compared denoising methods.

Enhancing ambient noise correlation processing using vector sensors.

Ambient noise cross-correlations between separated sensors can yield estimates of the Green's function between them. Vector sensors (which record both pressure and acoustic velocity vector components) can leverage their directionality to reject ambient noise sources that do not contribute to the emergence of the Green's function, thus improving performance over standard omnidirectional hydrophones. To quantify this performance gain, a time-domain analytical expression for the correlation between each component of a vector sensor in the presence of an isotropic ambient noise field is derived. Improvement of the velocity channel correlations relative to pressure channel correlations is examined for varying bandwidth, sensor separation distance, and additive channel noise levels. Last, the experimentally measured reduction in variance for the velocity channels correlations vs pressure correlations, using drifting vector sensors deployed in the Long Island Sound, were found to be comparable to the theoretical prediction. Overall, both theoretical and experimental results indicate modest gains are obtained when extracting the Green's function from velocity correlations over using pressure correlations. Thus, vector sensors can be used to reduce the required averaging time for this noise correlation processing, which may be especially useful, for instance, in a fluctuating environment or for drifting sensors.

MMP-DCD-CV based sparse channel estimation algorithm for underwater acoustic transform domain communication system

In this paper, we propose a computationally efficient multipath matching pursuit (MMP) channel estimation algorithm for underwater acoustic (UWA) transform domain communication systems (TDCSs). The algorithm, referred to as the MMP-DCD-CV algorithm, is based on the dichotomous coordinate descent (DCD) iterations and cross validation (CV). The MMP-DCD-CV sparse channel estimator in each iteration searches for multiple promising path candidates most relevant to a residual vector and chooses the best candidate. The DCD iterations are used to solve the corresponding least squares problem with low complexity and numerical stability. The CV provides a stopping criterion of the algorithm without a priori information on the channel sparsity and noise level and examines whether the algorithm overfits its data, thus improving the estimation accuracy. The performance of the proposed algorithm is evaluated under simulated sparse UWA channels. The numerical results show that the algorithm achieves better performance than the original MMP algorithm, has lower complexity, and does not require prior knowledge on the channel sparsity and noise level. We also propose an UWA TDCS with sparse channel estimation based on the proposed MMP-DCD-CV algorithm. The proposed UWA communication system is tested by the Waymark simulator, providing the virtual signal transmission in the UWA channel, with a measured Sound Speed Profile and bathymetry. Numerical results demonstrate that the UWA TDCS with the proposed sparse channel estimator offers considerable improvement in system performance compared to other TDCS schemes.

Towards Higher Spectral Efficiency: Spatial Path Index Modulation Improves Millimeter-Wave Hybrid Beamforming

The combination of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems and index modulation (IM) technique has recently constituted a novel form of mmWave hybrid beamforming. Lots of studies have been conducted to show that such system has the potential to outperform conventional mmWave-MIMOs with respect to spectral efficiency (SE). Most of the current works only focused on designing hybrid beamforming structures to empirically achieve higher SE performance. However, a fundamental question that whether IM technique can truly improve the SE of current mmWave hybrid beamforming is still left unanswered. Against such background, in this work we firstly extend the IM-assisted mmWave system to a more generalized version, i.e. a spatial path index modulation aided mmWave (SPIM-mmWave) system, which subsumes both IM-assisted and conventional mmWave-MIMO systems as its special cases. Based on the framework of SPIM-mmWave, a fundamental study is conducted towards a theoretic condition under which SPIM-mmWave guarantees to outperform conventional mmWave-MIMO schemes. It is demonstrated that, under a specific channel condition and noise level (which will be explicitly given by our work), SPIM-mmWave guarantees to outperform conventional mmWave-MIMO systems.

Understanding Normalized Mean Squared Error in Power Amplifier Linearization

This letter provides a detailed analysis of the normalized mean squared error (NMSE) of an ideal orthogonal frequency-division multiplexing (OFDM) system, subject to soft clipping of the power amplifier (PA) output. Based on the central limit theorem, the OFDM baseband input is modeled as white Gaussian noise, and based on the Bussgang theorem, the NMSE is studied in some detail. Asymptotes describing the NMSE for the considered system are derived, when the system is working in the linear operation mode and when working under compression, respectively. Detailed yet simple expressions between the NMSE, average output power, saturation voltage of the PA, and the noise level of the additive channel or thermal noise are derived, which are believed to provide insight into the system's behavior.

Experimental Realization of the Coupling Function Secure Communications Protocol and Analysis of Its Noise Robustness

There is an increasing need for everyday communications to be both secure and resistant to external perturbations. We have therefore created an experimental implementation of the coupling-function-based secure communication protocol, in order to assess its robustness to channel noise. The transmitter and receiver are implemented on single-board computers, thereby facilitating communication of the analog electronic signals. The information signals are encrypted at the transmitter as the timevariability of nonlinear coupling functions between dynamical systems. This results in a complicated nonlinear mixing and scrambling of the information. To replicate the channel noise, analog white noise is added to the encrypted signals. After digitization at the receiver, the decryption is performed with dynamical Bayesian inference to take account of time-varying dynamics in the presence of noise. The dynamical Bayesian approach effectively separates the deterministic information signals from the perturbations of dynamical channel noise. The experimental realization has demonstrated the feasibility, and established the performance, of the protocol for secure, reliable, communication even with high levels of channel noise.

Semi-fragile Watermarking Scheme for H.264/AVC Video Content Authentication Based on Manifold Feature

Authentication of videos and images based on the content is becoming an important problem in information security. Unfortunately, previous studies lack the consideration of Kerckhoffss principle in order to achieve this (i.e., a cryptosystem should be secure even if everything about the system, except the key, is public knowledge). In this paper, a solution to the problem of finding a relationship between a frame’s index and its content is proposed based on the creative utilization of a robust manifold feature. The proposed solution is based on a novel semi-fragile watermarking scheme for H.264/AVC video content authentication. At first, the input I-frame is partitioned for feature extraction and watermark embedding. This is followed by the temporal feature extraction using the Isometric Mapping algorithm. The frame index is included in the feature to produce the temporal watermark. In order to improve security, the spatial watermark will be encrypted together with the temporal watermark. Finally, the resultant watermark is embedded into the Discrete Cosine Transform coefficients in the diagonal positions. At the receiver side, after watermark extraction and decryption, temporal tampering is detected through a mismatch between the frame index extracted from the temporal watermark and the observed frame index. Next, the feature is regenerate through temporal feature regeneration, and compared with the extracted feature. It is judged through the comparison whether the extracted temporal watermark is similar to that of the original watermarked video. Additionally, for spatial authentication, the tampered areas are located via the comparison between extracted and regenerated spatial features. Experimental results show that the proposed method is sensitive to intentional malicious attacks and modifications, whereas it is robust to legitimate manipulations, such as certain level of lossy compression, channel noise, Gaussian filtering and brightness adjustment. Through a comparison between the extracted frame index and the current frame index, the temporal tempering is identified. With the proposed scheme, a solution to the Kerckhoffss principle problem is specified.

The ISI distribution of the stochastic Hodgkin-Huxley neuron.

The simulation of ion-channel noise has an important role in computational neuroscience. In recent years several approximate methods of carrying out this simulation have been published, based on stochastic differential equations, and all giving slightly different results. The obvious, and essential, question is: which method is the most accurate and which is most computationally efficient? Here we make a contribution to the answer. We compare interspike interval histograms from simulated data using four different approximate stochastic differential equation (SDE) models of the stochastic Hodgkin-Huxley neuron, as well as the exact Markov chain model simulated by the Gillespie algorithm. One of the recent SDE models is the same as the Kurtz approximation first published in 1978. All the models considered give similar ISI histograms over a wide range of deterministic and stochastic input. Three features of these histograms are an initial peak, followed by one or more bumps, and then an exponential tail. We explore how these features depend on deterministic input and on level of channel noise, and explain the results using the stochastic dynamics of the model. We conclude with a rough ranking of the four SDE models with respect to the similarity of their ISI histograms to the histogram of the exact Markov chain model.

Self-Consistent Learning of the Environment

We study the Bayesian process to estimate the features of the environment. We focus on two aspects of the Bayesian process: how estimation error depends on the prior distribution of features and how the prior distribution can be learned from experience. The accuracy of the perception is underestimated when each feature of the environment is considered independently because many different features of the environment are usually highly correlated and the estimation error greatly depends on the correlations. The self-consistent learning process renews the prior distribution of correlated features jointly with the estimation of the environment. Here, maximum a posteriori probability (MAP) estimation decreases the effective dimensions of the feature vector. There are critical noise levels in self-consistent learning with MAP estimation, that cause hysteresis behaviors in learning. The self-consistent learning process with stochastic Bayesian estimation (SBE) makes the presumed distribution of environmental features converge to the true distribution for any level of channel noise. However, SBE is less accurate than MAP estimation. We also discuss another stochastic method of estimation, SBE2, which has a smaller estimation error than SBE without hysteresis.