Channel Errors Research Articles

Coherent errors and readout errors in the surface code

We consider the combined effect of readout errors and coherent errors, i.e., deterministic phase rotations, on the surface code. We use a recently developed numerical approach, via a mapping of the physical qubits to Majorana fermions. We show how to use this approach in the presence of readout errors, treated on the phenomenological level: perfect projective measurements with potentially incorrectly recorded outcomes, and multiple repeated measurement rounds. We find a threshold for this combination of errors, with an error rate close to the threshold of the corresponding incoherent error channel (random Pauli-Z and readout errors). The value of the threshold error rate, using the worst case fidelity as the measure of logical errors, is 2.6%. Below the threshold, scaling up the code leads to the rapid loss of coherence in the logical-level errors, but error rates that are greater than those of the corresponding incoherent error channel. We also vary the coherent and readout error rates independently, and find that the surface code is more sensitive to coherent errors than to readout errors. Our work extends the recent results on coherent errors with perfect readout to the experimentally more realistic situation where readout errors also occur.

Insdel codes from subspace and rank-metric codes

Insertion and deletion (insdel in short) codes are designed to deal with synchronization errors in communication channels caused by insertions and deletions of message symbols. These codes have received a lot of attention due to their applications in diverse areas such as computational biology, DNA data storage, race-track memory error corrections, language processing, and synchronous digital communication networks. In the present work, we study constructions and limitations of insdel codes from rank metric and subspace codes. This paper studies and improves the idea of the work [4] by Hao Chen on the connection between insdel codes and subspace codes.We discuss why subspace code is a natural choice for constructing insdel codes and show that the interleaved Gabidulin codes can be used to construct nonlinear insdel codes approaching the Singleton bound. Then we show that the indexing scheme of transforming efficient Hamming metric codes to efficient insdel codes can be adapted for the class of rank metric codes. And that improves the base field size of the construction of insdel codes from lifted rank-metric codes. It is also shown that the size of the insdel code from a subspace code can be improved significantly than in the previously proposed construction. We give an algebraic condition for a linear Gabidulin rank metric code to be optimal insdel code adapting the condition proved for Reed-Solomon codes. Moreover, we give constructions of linear and nonlinear insdel codes from Sidon spaces.

Energy‐efficient selection of relay for UWSNs in the Internet of underwater things

SummaryInternet of underwater things (IoUT) for underwater monitoring is known worldwide for smart interlinked underwater things that exhibit the capacity to monitor the vast unexplored waters of the oceans. Concept of IoUT has been derived from Internet of Things (IoT) in order to acquire the exquisite benefits of networking in underwater environment. IOT standards and technologies do not work well in underwater environment, such as infrared, Wi‐Fi, and radio frequency (RF) due to high channel errors and limited range up to few meters. Acoustic waves, however, can be used to communicate both in shallow and deep oceans due to their low frequency (kHZ) signal. In context of IoUT, communication based on acoustic links enables different applications such as underwater exploration, environmental monitoring, and disaster prevention even without availability of GPS facility like free space environment. In unpredictable and changing underwater environment, energy efficiency becomes a major challenge during data routing along multiple devices. Batteries of the sensor nodes, autonomous underwater vehicle (AUV), and remotely operated vehicle (ROV) cannot be removed with easiness and difficult to recharge, and the only way out is efficient sensor node selection for relaying to save massive amount of energy. Energy aware channel routing protocol (ECARP) does not consider the depth of the node while selecting the relay nodes to forward the data. Relay node selection in underwater Internet of things (IOUT) is a primary problem addressed in this research based on channel state information (CSI) for establishing best path to relay information among IOUT devices. Our major focus was to develop better technique for the relay node selection using a CSI and select relay node by looking at its depth from ocean surface and residual energy in the proposed ED‐CARP. Simulation results validate that proposed ED‐CARP can decrease the communication cost and increase the network lifetime.

Estimating parameters in multichannel fundamental frequency with harmonics model

In this paper, we introduce a special multichannel model in the class of multichannel sinusoidal model. In multichannel sinusoidal model, the inherent frequencies from distinct channels are the same with different amplitudes. The underlying assumption here is that there is a fundamental frequency that is the same in each channel and the effective frequencies are harmonics of this fundamental frequency. We name this model as multichannel fundamental frequency with harmonics model. It is assumed that the errors in individual channel are independently and identically distributed whereas the signal from different channels are correlated. We propose generalized least squares estimators which become the maximum likelihood estimators also, when the error distribution of the different channels follows a multivariate Gaussian distribution. The proposed estimators are strongly consistent and asymptotically normally distributed. We have provided the implementation of the generalized least squares estimators in practice. Special attention has been taken when the number of channels is two and both have equal number of components. Simulation experiments have been carried out to observe the performances of the proposed estimators. Real data sets have been analysed using a two-channel fundamental frequency model.

Robust beamforming for dual‐function radar‐communication with imperfect channel state information

AbstractDual‐function radar and communication (DFRC) have recently received significant attention as an effective way to alleviate spectrum congestion and reduce system costs. Joint transmit beamforming plays a critical role in DFRC systems, as it can effectively reduce mutual interference and improve the performance of both the radar and communication systems. This paper investigates the problem of robust beamforming design with imperfect channel state information (CSI) to ensure communication performance while optimizing radar performance. Considering elliptically bounded CSI errors, the objective function is designed to minimize the radar loss function, which is related to the radar transmit beam pattern, subject to worst‐case signal‐to‐interference‐plus‐noise ratio constraints at each user, which ensure fairness between users. An efficient algorithm based on semidefinite relaxation and the S‐procedure is proposed to solve the non‐convex problem. Simulation results are presented to illustrate the effectiveness of the proposed robust beamforming scheme, which can reduce the impact of channel errors.

Quantum error correction from complexity in Brownian SYK

We study the robustness of quantum error correction in a one-parameter ensemble of codes generated by the Brownian SYK model, where the parameter quantifies the encoding complexity. The robustness of error correction by a quantum code is upper bounded by the “mutual purity” of a certain entangled state between the code subspace and environment in the isometric extension of the error channel, where the mutual purity of a density matrix ρAB is the difference {mathcal{F}}_{rho}left(A:Bright)equiv textrm{Tr} {rho}_{AB}^2-textrm{Tr} {rho}_A^2 textrm{Tr} {rho}_B^2 . We show that when the encoding complexity is small, the mutual purity is O(1) for the erasure of a small number of qubits (i.e., the encoding is fragile). However, this quantity decays exponentially, becoming O(1/N) for O(log N) encoding complexity. Further, at polynomial encoding complexity, the mutual purity saturates to a plateau of O(e−N). We also find a hierarchy of complexity scales associated to a tower of subleading contributions to the mutual purity that quantitatively, but not qualitatively, adjust our error correction bound as encoding complexity increases. In the AdS/CFT context, our results suggest that any portion of the entanglement wedge of a general boundary subregion A with sufficiently high encoding complexity is robustly protected against low-rank errors acting on A with no prior access to the encoding map. From the bulk point of view, we expect such bulk degrees of freedom to be causally inaccessible from the region A despite being encoded in it.

Performance Analysis of Classical Data Transmission Over a Quantum Channel in the Presence of Atmospheric Turbulence

This paper presents the performance analysis of classical data transmission over a quantum channel in the presence of atmospheric turbulence. To transmit classical data, quantum coherent states are modulated over a log-normal turbulent channel for satellite-to-ground applications. The expression for the number of photons in the turbulent coherent state is approximated. Performance analysis is done in terms of error probability, channel capacity, and quantum bit error rate (QBER). The simulation results are presented using the Monte-Carlo simulation method. Numerical results show that the error performance improves with increasing the number of signal photons. However, for turbulence strength greater than 0.8, QBER increases more rapidly with a higher number of signal photons. Hence, a trade-off is essential between an achievable QBER and the number of signal photons.

Covert Communication With Mismatched Decoders

This paper considers the problem of covert communication over binary-input discrete memoryless channels with mismatched decoding, in which a sender wishes to reliably communicate with a receiver whose decoder is fixed and possibly sub-optimal, and simultaneously to ensure that the communication is covert with respect to a warden. We present a single-letter lower bound and two single-letter upper bounds on the information-theoretically optimal throughput as a function of the given decoding metric, channel laws, and the desired level of covertness. These bounds match for a variety of scenarios of interest, such as (i) when the channel between the sender and receiver is a binary-input binary-output channel, and (ii) when the decoding metric is particularized to the so-called erasures-only metric. The lower bound is obtained based on a modified random coding union bound with pulse position modulation (PPM) codebooks, coupled with a non-standard expurgation argument. The upper bounds are based on the idea of translating the mismatched-decoding error of the original channel to the decoding error of an auxiliary channel.

BIC Codes: Bit Insertion-Based Constrained Codes With Error Correction for DNA Storage

In this paper, we propose a new coding algorithm for DNA storage over both error-free and error channels. For the error-free case, we propose a constrained code called bit insertion-based constrained (BIC) code. BIC codes convert a binary data sequence to multiple oligo sequences satisfying the maximum homopolymer run (i.e., run-length (RL)) constraint by inserting dummy bits. We show that the BIC codes nearly achieves the capacity in terms of information density while the simple structure of the BIC codes allows linear-time encoding and fast parallel decoding. Also, by combining a balancing technique with the BIC codes, we obtain the constrained coding algorithm to satisfy the GC-content constraint as well as the RL constraint. Next, for DNA storage channel with errors, we integrate the proposed constrained coding algorithm with a rate-compatible low-density parity-check (LDPC) code to correct errors and erasures. Specifically, we incorporate LDPC codes adopted in the 5G new radio standard because they have powerful error-correction capability and appealing features for the integration. Simulation results show that the proposed integrated coding algorithm outperforms existing coding algorithms in terms of information density and error correctability.

Robust WMMSE Precoder With Deep Learning Design for Massive MIMO

In this paper, we investigate the downlink robust precoding with imperfect channel state information (CSI) for massive multiple-input-multiple-output (MIMO) communications. With the estimated channel and channel error statistics, the general design of the robust precoder is to maximize the ergodic sum rate subject to the total transmit power constraint. To make the problem more tractable, we find a lower bound of the ergodic sum rate and propose the robust weighted minimum mean-squared-error (WMMSE) precoder to maximize the bound. We characterize the structure of the precoding vectors by low-dimensional parameters, which are learned directly from the available CSI through a neural network. As such, the precoding vectors can be immediately computed without iterations. To extend the deep learning design to multi-antennas users, we present a flexible approach that allows the various antenna configurations at the user side to be handled. Simulation results show that the deep learning design can significantly reduce the computational complexity compared with the existing precoder designs while achieving near optimal performance.

Sum Rate Analysis for Massive MIMO-NOMA Uplink System With Group-Level Successive Interference Cancellation

The deep integration between non-orthogonal multiple access (NOMA) and massive multiple-input multiple-output (MIMO) technology is a promising way to achieve massive connectivity and high capacity in B5G/6G wireless communication system. In this letter, a novel signal processing framework for massive MIMO-NOMA data transmission is proposed based on group-level successive interference cancellation (GLSIC), in which the users are grouped according to the distances to the base station (BS) and NOMA is applied among different groups by employing GLSIC to reduce the inter-group interference. The uplink sum rate is derived for the proposed GLSIC-based massive MIMO-NOMA system with minimum mean square error channel estimator and maximal ratio combiner at BS, and the effects of channel estimation error, inter-group pilot contamination, and imperfect GLSIC are analytically quantified in the derivation. Theoretical analysis and simulation results show that, under the same conditions, the proposed GLSIC-based system achieves higher uplink rate than the existing cluster-based MIMO-NOMA system with user-level SIC.

Evaluating efficient SENSE algorithms to deblur spiral MRI with fat/water separation.

The combination of SENSE and spiral imaging with fat/water separation enables high temporal efficiency. However, the corresponding computation increases due to the blurring/deblurring operation across the multi-channel data. This study presents two alternative models to simplify computational complexity in the original full model (model 1). The performances of the models are evaluated in terms of the computation time and reconstruction error. Two approximated spiral MRI reconstruction models were proposed: the comprehensive blurring before coil operation (model 2) and the regional blurring before coil operation (model 3), respectively, by altering the order of coil-sensitivity encoding process to distribute signals among the multi-channel coils. Four subjects were recruited for scanning both fully sampled T1 - and T2 -weighted brain image data with simulated undersampling for testing the computational efficiency and accuracy on the approximation models. Based on the examples, the computation time can be reduced to 31%-47% using model 2, and to 39%-56% using model 3.The quality of the water image remains unchanged among the three models, whereas the primary difference in image quality is in the fat channel. The fat images from model 3 are consistent with those from model 1, but those from model 2 have higher normalized error, differing by up to 4.8%. Model 2 provides the fastest computation but exhibits higher error in the fat channel, particularly in the high field and with long acquisition window. Model 3, an abridged alternative, is also faster than the full model and can maintain high accuracy in reconstruction.

Process optimization of chain-die forming for asymmetric channels by an image-based machine learning method

The coupled springback of twist and angle is a major shape error for high-strength asymmetric channels in the automotive industry. In the process design stage of sheet metal forming, scalar-based surrogate models such as artificial neural networks, are the common optimization method to predict springback and reduce shape error. However, such models fail to learn and utilize the location information of the formed part, which will result in poor prediction accuracy and compensation effect for the coupled springback in asymmetric channels. Therefore, in this paper, based on the U-net convolutional neural network (CNN), an image-based surrogate model is established to predict the dimensional deviation full field of the formed part. An attention-based ResNet layer is proposed and replaced with the bottleneck of the U-net-style base network to enhance the sensitivity of the network for different regions of die surface in the sheet metal forming. An automatic data preprocessing method is developed to convert the design parameters and simulation results into unified image data. After training, the proposed CNN surrogate model shows a highly accurate prediction for the dimensional deviation full field in real-time. Furthermore, an image-based optimization architecture, in conjunction with a differential evolution algorithm with self-adaptive factors, the automatic data preprocessing method, and the CNN surrogate model, is developed to minimize the dimensional deviations over the entire region of the formed part. Finally, the validation experiments of chain-die forming are carried out by using optimized design parameters and the forming accuracy has been significantly improved.

Common lossless compression algorithms and their error resiliency performance

With the development of communication technology and computer technology, many related industries, such as multimedia entertainment, put forward higher requirements for storing and transmitting information data. The research of data compression technology has attracted more and more attention. Therefore, the error resiliency ability of data compression algorithm is particularly important. How to enhance the error resiliency of data compression communication systems has been a hot topic for researchers. This paper mainly introduces the lossless data compression technology and its basic principle and performance index. Two typical lossless compression codes, Huffman and Arithmetic coding are deeply studied, including the principle of coding and the problem of error resiliency. Huffman coding and Arithmetic coding are two very important lossless compression codes widely used. The ability to resist channel error is an important index for data compression in communication. It is of great significance to further improve the channel adaptability of data compression to study the above two kinds of codes and their ability to resist channel error.

Channel Estimation for RIS-Assisted MIMO Systems in Millimeter Wave Communications

The large number of estimated parameters in a reconfigurable intelligent surface (RIS) makes it difficult to achieve accurate channel estimation accuracy in 6G. Therefore, we suggest a novel two-phase channel estimation framework for uplink multiuser communication. In this context, we propose an orthogonal matching pursuit (OMP)-based linear minimum mean square error (LMMSE) channel estimation approach. The OMP algorithm is used in the proposed algorithm to update the support set and pick the columns of the sensing matrix that are most correlated with the residual signal, which successfully reduces pilot overhead by removing redundancy. Here, we use LMMSE’s advantages for handling noise to address the problem of inadequate channel estimation accuracy when the signal-to-noise ratio (SNR) is low. Simulation findings demonstrate that the proposed approach outperforms least-squares (LS), traditional OMP, and other OMP-based algorithms in terms of estimate accuracy.

Performance evaluation of hard‐decision and soft‐data aided cooperative spectrum sensing over Nakagami‐m fading channel

AbstractThe secondary users can opportunistically utilize the spectrum holes left by the licensed users (also called primary users [PUs]) with the help of cognitive radio (CR) technology. The cooperative spectrum sensing (CSS) is crucial for the implementation of CR algorithms in the next‐generation wireless networks, for the specific identification of spectral gaps of PUs under any fading/shadowing channel. Accurate spectrum sensing reveals that the CUs only provide data when the PUs report being idle. Yet, there is always a possibility that CU transmission will cause interference with PU communications, leading to inefficient spectrum use. So, this work explores the performance of a proposed CSS by considering such factors with the key objective of enhancing spectrum utilisation efficiency. This paper evaluates the role of the proposed CSS with a variety of hard decision fusion rules (HDFR) schemes as well as soft data fusion schemes (SDFS) methods. Toward this end, for the proposed system, closed‐form expressions for the evaluation of various performance metrics such as the false alarm, miss‐detection probabilities, total error probability (TEP), and throughput/energy efficiency are derived by taking into consideration the Nakagami‐m fading environment using different hard and soft algorithms and are validated by means of numerical simulations. Furthermore, by taking into account various network specifications, such as channel error probability (q), fading severity parameter (m), number of samples (M), number of CR nodes (N), average sensing (S) channel signal‐to‐noise ratio, and sensing threshold (λ), a performance evaluation between the SDFS and HDFR schemes has been carried out. Furthermore, using HDFR/SDFS schemes, analytical models are formulated for determining an optimal number of CR nodes () and optimal threshold () values for achieving the lowest TEP and highest energy efficiency.

Decision Algorithm Based on the Modified Bellman Equation to Deal With EMI-Induced Errors in Hamming-Based Communications

This article presents a decision-making algorithm based on a modified version of the Bellman equation to deal with electromagnetic interference errors in a communication channel inside a harsh electromagnetic reverberant environment. The Bellman equation is a fundamental concept in decision-making problems such as Markov decision processes. Such processes model decision making in situations where the outcomes are partly random and partly controlled by an agent (decision maker). Recent studies have implemented Markov decision processes as a tool for risk assessment in areas such as robotics or aviation. However, so far, no research has been reported that uses the Bellman equation or Markov decision processes to deal with risks related to electromagnetic disturbances. In our study, a wired communication channel that uses non-return-to-zero-level data encoding and Hamming code for error detection and correction is disturbed. First, the packet error rate (PER) is calculated and compared with and without the proposed algorithm for different electromagnetic disturbance frequencies and bit rates. The gain is compared at different PERs when the algorithm's parameters (called rewards) are optimized and when these are set as random. Finally, the influence of the rewards and the maximum number of resends on the algorithm's performance is also studied.

IRS-Assisted MISO System With Phase Noise: Channel Estimation and Power Scaling Laws

Recent studies have shown that intelligent reflecting surfaces (IRS) can significantly improve the spectral and energy efficiency of wireless communication links. However, most works assume perfect transceivers and IRS, which is impractical in real communication systems. In this work, we study the effect of the hardware impairments in IRS-assisted MISO systems with single user, where we consider both phase noise caused by the imperfect transceivers and IRS. To this end, we propose a linear minimum mean square error (LMMSE) channel estimation algorithm that takes the phase noise into account. Furthermore, we study the impact of phase noise on the downlink performance of an IRS-assisted system. Both analytical and numerical results are presented, where we prove that the transceiver phase noise can be compensated with the optimized IRS, while the IRS phase noise remains but is not exacerbated. As the number of reflective elements <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> approaches infinity, the IRS phase noise results in a constant loss in terms of the ergodic rate. Moreover, we find that the phase noise has no impact on the scaling laws. If the direct channel is blocked and as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> → ∞, the transmit power can be scaled down by 1/ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> and 1/ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively, for random and optimized IRS, without compromising the received signal to noise ratio (SNR).

On the Viability of Using a Subset of Transmitter- Observation Receivers for Training a Common DPD in Fully Digital MIMO Transmitters

This letter lays the foundation for reducing the required number of transmitter-observation receivers (TORs) for training digital predistortion (DPD) in fully digital massive multiple-input, multiple-output (MIMO) transmitters. Specifically, it investigates the viability of applying the same, common set of DPD coefficients to linearize all RF chains in fully digital massive MIMO transmitters. First, it is shown that if all RF chains are operated at the same output power, the common set of DPD coefficients can be found by simply averaging the coefficients obtained by training each RF chain on its own. This suggests that only a few chains may be needed for training provided the chains are a representative sample. Experimental results are then conducted where one and three chains are used for training. It is found that when training for one chain, significant variations in normalized mean square error (NMSE) and adjacent channel power ratio (ACPR) of up to 9 dB across the chains are realized. For training with three chains, the common set of DPD coefficients can reduce the variation to 1–2 dB. Finally, after over-the-air (OTA) combining, excellent linearization performance is found for three chains.

Subspace-Based Pilot Decontamination in User-Centric Scalable Cell-Free Wireless Networks

We consider a cell-free wireless system operated in Time Division Duplex (TDD) mode with user-centric clusters of remote radio units (RUs). Since the uplink pilot dimensions per channel coherence slot is limited, co-pilot users might incur mutual pilot contamination. In the current literature, it is assumed that the long-term statistical knowledge of all user channels is available. This enables Minimum Mean-Square Error channel estimation or simplified dominant subspace projection, which achieves significant pilot decontamination under certain assumptions on the channel covariance matrices. However, estimating the channel covariance matrix or even just its dominant subspace at all RUs forming a user cluster is not an easy task. In fact, if not properly designed, a piloting scheme for such long-term statistics estimation will also be subject to the contamination problem. In this paper, we propose a new channel subspace estimation scheme explicitly designed for cell-free wireless networks. Our scheme is based on 1) a sounding reference signal (SRS) using latin squares wideband frequency hopping, and 2) a subspace estimation method based on robust Principal Component Analysis (R-PCA). The SRS hopping scheme ensures that for any user and any RU participating in its cluster, only a few pilot measurements will contain strong co-pilot interference. These few heavily contaminated measurements are (implicitly) eliminated by R-PCA, which is designed to regularize the estimation and discount the “outlier” measurements. Our simulation results show that the proposed scheme achieves almost perfect subspace knowledge, which in turns yields system performance very close to that with ideal channel state information, thus essentially solving the problem of pilot contamination in cell-free user-centric TDD wireless networks.