Simultaneous Decoding Research Articles

저지연 LDPC 복호기의 하드웨어 구현 및 검증

In this investigation, we developed the hardware for a low-latency low-density parity check (LDPC) decoder and validated its error-correction performance. The LDPC code was chosen as the error-correction code conforming to the international portable Internet standard IEEE 802.16e. The LL-LDPC code, implemented in this study, efficiently minimizes the number of coded calculations through the utilization of a type of Structured-LDPC code defined by the physical layer description in IEEE 802.16e. The low-delay LDPC decoder employs a parallel decoding method to mitigate the decoding delay associated with repetitive decoding. Comprising four SISO modules based on four coding repetitions in the coder, the low-delay LDPC decoder significantly reduces delay times. The four SISO modules operate in parallel, enabling simultaneous decoding and thereby decreasing the delay time by 1/4 compared to alternative approaches.

Optically Tunable Multistable Liquid Crystal Grating for Anti‐Counterfeiting through Multilayer Continuous Phase Analysis

AbstractOptically tunable liquid‐crystal (LC) gratings have remarkable applications in the field of image analysis for anti‐counterfeiting. However, existing tunable LC gratings suffer from limited adjustable ranges and weak stability. The spacing variability and accuracy of the grating cannot satisfy the demands of continuous analysis. This study presents a novel approach to anti‐counterfeiting through multilayer continuous phase analysis (MCPA) based on the continuous multistable states of the LC grating. This optically tunable LC grating featuring continuous multistable states is successfully prepared using a novel optically tunable chiral dopant, tetra(n‐hex‐azo), which exhibits an exceptionally slow conformational relaxation rate (half‐life: 78.02 h). By exposing the LC grating to ultraviolet irradiation with an intensity of 0.1054 mW cm−2, the fringe spacing can be continuously and widely tuned between 0 and 250 µm, which enhances the ultraslow conformational relaxation of tetra(n‐hex‐azo). Four representative spacings (50, 55, 60, and 65 µm) are selected to construct four‐layer MCPA gratings, enabling simultaneous decoding of a four‐layer latent image for the first time. The mechanism of the MCPA grating is investigated through control experiments and theoretical simulations. This study demonstrates a facile, precise, and efficient approach for complex information encryption and anti‐counterfeiting.

On Second Order Rate Regions for the Static Scalar Gaussian Broadcast Channel

This paper considers the single antenna, static, scalar Gaussian broadcast channel in the finite blocklength regime. Second order achievable and converse rate regions are presented. Both a global reliability and per-user reliability requirements are considered. The two-user case is analyzed in detail, and generalizations to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -user case are also discussed. The largest second order achievable regions presented here require both superposition and rate splitting in the code construction, as opposed to the (infinite blocklength, first order) capacity region which does not require rate splitting. Indeed, the finite blocklength penalty causes superposition alone to underperform other coding techniques in some parts of the region. In addition, the proposed scheme uses joint simultaneous decoding as opposed to successive interference cancellation. Interestingly, in the two-user case with per-user reliability requirements, the capacity achieving superposition encoding order (with the codeword intended for the user with the smallest received SNR as cloud center) does not necessarily give the largest second order region. Instead, the message of the user with the smallest point-to-point second order capacity should be encoded in the cloud center in order to obtain the largest second order region for the proposed scheme.

Feasibility of Wearable PPG for Simultaneous Hand Gesture and Force Level Classification

Simultaneous decoding of hand gestures and force levels has the potential to unlock a broad range of home-based application scenarios in post-stroke rehabilitation. However, current wearable approaches cannot struggle to balance wearability and functionality well. We thus propose and investigate the feasibility of wearable photoplethysmography (PPG) for simultaneous hand gesture and force level classification. A customized wristband embedded with green, red, and infrared light PPG sensors was developed. Fourteen subjects wore this wristband and performed four pinching gestures at 10%, 40%, and 70% maximum volunteer contraction (MVC) force levels. The results showed that the proposed method achieved satisfactory recognition accuracy, and the combination of three reflected light PPG signals was more accurate than any single signal or two-signal combinations. Force level classification accuracy within each hand gesture was less robust to the variety of hand gestures, and hand gesture classification accuracy at different force levels decreased as the force level increased. In addition, the proposed method still achieved reasonable recognition accuracy, with a relatively small amount of training data, potentially saving time in practical applications. These results demonstrate the feasibility of a wearable, low-cost PPG approach for simultaneous hand gesture and force level classification, which could facilitate more widespread hand mobility and strength training both inside and outside the clinic.

SWIPT-Enabled Multiple Access Channel: Effects of Decoding Cost and Non-Linear EH Model

We studied power splitting-based simultaneous wireless information and power transfer (PS-SWIPT) in multiple access channels (MACs), considering the decoding cost and non-linear energy harvesting (EH) constraints at the receiving nodes to study practical limitations of an EH communication system. Under these restrictions, we formulated and analyzed the achievable rate and maximum departure regions in two well-studied scenarios, i.e., a classical PS-SWIPT MAC and a PS-SWIPT MAC with user cooperation. In the classical PS-SWIPT MAC setting, closed-form expressions for the optimal values of the PS factors are derived for two fundamental decoding schemes: simultaneous decoding and successive interference cancellation. In the PS-SWIPT MAC with user cooperation, the joint optimal power allocation for users as well as the optimal PS factor are derived. This reveals that one decoding scheme outperforms the other in the classical PS-SWIPT MAC, depending on the function type of the decoding cost. Finally, it is shown that the cooperation between users can potentially boost the performance of a PS-SWIPT MAC under decoding cost and non-linear EH constraints. Moreover, effects of the decoding cost functions, non-linear EH model and channel quality between the users are studied, and performance characteristics of the system are discussed.

Simultaneous decoding of cardiovascular and respiratory functional changes from pig intraneural vagus nerve signals

Objective. Bioelectronic medicine is opening new perspectives for the treatment of some major chronic diseases through the physical modulation of autonomic nervous system activity. Being the main peripheral route for electrical signals between central nervous system and visceral organs, the vagus nerve (VN) is one of the most promising targets. Closed-loop VN stimulation (VNS) would be crucial to increase effectiveness of this approach. Therefore, the extrapolation of useful physiological information from VN electrical activity would represent an invaluable source for single-target applications. Here, we present an advanced decoding algorithm novel to VN studies and properly detecting different functional changes from VN signals. Approach. VN signals were recorded using intraneural electrodes in anaesthetized pigs during cardiovascular and respiratory challenges mimicking increases in arterial blood pressure, tidal volume and respiratory rate. We developed a decoding algorithm that combines discrete wavelet transformation, principal component analysis, and ensemble learning made of classification trees. Main results. The new decoding algorithm robustly achieved high accuracy levels in identifying different functional changes and discriminating among them. Interestingly our findings suggest that electrodes positioning plays an important role on decoding performances. We also introduced a new index for the characterization of recording and decoding performance of neural interfaces. Finally, by combining an anatomically validated hybrid neural model and discrimination analysis, we provided new evidence suggesting a functional topographical organization of VN fascicles. Significance. This study represents an important step towards the comprehension of VN signaling, paving the way for the development of effective closed-loop VNS systems.

Inner bounds via simultaneous decoding in quantum network information theory

We prove new inner bounds for several multiterminal channels with classical inputs and quantum outputs. Our inner bounds are all proved in the one-shot setting and are natural analogues of the best classical inner bounds for the respective channels. For some of these channels, similar quantum inner bounds were unknown even in the asymptotic independent and identically distributed setting. We prove our inner bounds by appealing to a new classical–quantum joint typicality lemma established in a companion paper. This lemma allows us to lift to the quantum setting many inner bound proofs for classical multiterminal channels that use intersections and unions of typical sets.

On the Optimal Precoding for MISO-WSN: One Time Slot Detection of Multiple Binary Data on the Same Frequency Band

A fast multi-target detection technique-using a devised precoding in a wireless sensor network (WSN)-is proposed. The targets are detected by binary wireless sensors which only have one or zero logical outputs, indicating a target's presence or absence. The goal is to simultaneously decode all sensors' data in one time slot at the receiver node, where all sensors transmit data simultaneously on the same frequency band. For this to happen, a specific geometric deployment of the sensors is presented in the paper. The concept in this work might have similarities to the bijective mappings in the superposition modulation. Different channel gains and the additive white Gaussian noise are included in the model. The system model is described in detail and the communication scheme to fulfill a simultaneous decoding is mathematically formulated. Then, the parametric solutions to this general digital modulation problem are studied step by step through several theorems, and the optimal solutions that minimize the detection error are found. The combined effects of the noise and the non-perfect channel state information on the signal constellations are both analytically and numerically investigated. In addition, two other detection scenarios with a priori information about the targets are studied.

Compute-Forward for DMCs: Simultaneous Decoding of Multiple Combinations

Algebraic network information theory is an emerging facet of network information theory, studying the achievable rates of random code ensembles that have algebraic structure, such as random linear codes. A distinguishing feature is that linear combinations of codewords can sometimes be decoded more efficiently than codewords themselves. The present work further develops this framework by studying the simultaneous decoding of multiple messages. Specifically, consider a receiver in a multi-user network that wishes to decode several messages. Simultaneous joint typicality decoding is one of the most powerful techniques for determining the fundamental limits at which reliable decoding is possible. This technique has historically been used in conjunction with random i.i.d. codebooks to establish achievable rate regions for networks. Recently, it has been shown that, in certain scenarios, nested linear codebooks in conjunction with “single-user” or sequential decoding can yield better achievable rates. For instance, the compute–forward problem examines the scenario of recovering $L \le K$ linear combinations of transmitted codewords over a $K$ -user multiple-access channel (MAC), and it is well established that linear codebooks can yield higher rates. This paper develops bounds for simultaneous joint typicality decoding used in conjunction with nested linear codebooks, and applies them to obtain a larger achievable region for compute–forward over a $K$ -user discrete memoryless MAC. The key technical challenge is that competing codeword tuples that are linearly dependent on the true codeword tuple introduce statistical dependencies, which requires careful partitioning of the associated error events.

Simultaneous Decoding of Eccentricity and Direction Information for a Single-Flicker SSVEP BCI

The feasibility of a steady-state visual evoked potential (SSVEP) brain–computer interface (BCI) with a single-flicker stimulus for multiple-target decoding has been demonstrated in a number of recent studies. The single-flicker BCIs have mainly employed the direction information for encoding the targets, i.e., different targets are placed at different spatial directions relative to the flicker stimulus. The present study explored whether visual eccentricity information can also be used to encode targets for the purpose of increasing the number of targets in the single-flicker BCIs. A total number of 16 targets were encoded, placed at eight spatial directions, and two eccentricities (2.5° and 5°) relative to a 12 Hz flicker stimulus. Whereas distinct SSVEP topographies were elicited when participants gazed at targets of different directions, targets of different eccentricities were mainly represented by different signal-to-noise ratios (SNRs). Using a canonical correlation analysis-based classification algorithm, simultaneous decoding of both direction and eccentricity information was achieved, with an offline 16-class accuracy of 66.8 ± 16.4% averaged over 12 participants and a best individual accuracy of 90.0%. Our results demonstrate a single-flicker BCI with a substantially increased target number towards practical applications.

LP Bounds for Rate-Distortion With Variable Side Information

We consider a rate-distortion problem with side information at multiple decoders. Several upper and lower bounds have been proposed for this general problem or special cases of it. We provide an upper bound for general instances of this problem, which takes the form of a linear program, by utilizing random binning and simultaneous decoding techniques [1] and compare it with the existing bounds. We also provide a lower bound for the general problem, which was inspired by a linear-programming lower bound for index coding, and show that it subsumes most of the lower bounds in literature. Using these upper and lower bounds, we explicitly characterize the rate-distortion function of a problem that can be seen as a Gaussian analogue of the “odd-cycle” index coding problem.

Towards a fully spatially coded brain-computer interface: simultaneous decoding of visual eccentricity and direction.

By encoding visual targets with different locations relative to a stimulus, spatially coded brain-computer interface (BCI) has regained interest nowadays. Recent spatially coded BCI studies have demonstrated the feasibility of single-stimulus, multi-target BCIs, suggesting their potentials for simple and efficient applications. However, these studies have only decoded the visual direction information from the neural responses. To fully utilize the visual spatial information, it is necessary to include the visual eccentricity information as well. In the present study, the decodability of visual eccentricity information for BCI application was investigated for the first time. Sixteen targets were encoded simultaneously with eight directions and two eccentricities relative to a visual motion stimulus. Distinct neural spatial patterns and response strengths of motion-onset visual evoked potentials were elicited in the 16 attention conditions. The offline analysis reached an average classification accuracy of 63.1±11.5%, and the best-performing participant achieved an accuracy of 81.9%, well above the chance level (i.e., 6.25%) for 16-target classification. The results suggested the feasibility of simultaneous decoding of visual eccentricity and direction information towards a fully spatially coded BCI.

An LDA-Based Approach for Real-Time Simultaneous Classification of Movements Using Surface Electromyography.

Myoelectric-based decoding strategies offer significant advantages in the areas of human-machine interactions because they are intuitive and require less cognitive effort from the users. However, a general drawback in using machine learning techniques for classification is that the decoder is limited to predicting only one movement at any instant and hence restricted to performing the motion in a sequential manner, whereas human motor control strategy involves simultaneous actuation of multiple degrees of freedom (DOFs) and is considered to be a natural and efficient way of performing tasks. Simultaneous decoding in the context of myoelectric-based movement control is a challenge that is being addressed recently and is increasingly popular. In this paper, we propose a novel classification strategy capable of decoding both the individual and combined movements, by collecting data from only the individual motions. Additionally, we exploit low-dimensional representation of the myoelectric signals using a supervised decomposition algorithm called linear discriminant analysis, to simplify the complexity of control and reduce computational cost. The performance of the decoding algorithm is tested in an online context for the two DOFs task comprising the hand and wrist movements. Results indicate an overall classification accuracy of 88.02% for both the individual and combined motions.

On the near-optimality of one-shot classical communication over quantum channels

We study the problem of transmission of classical messages through a quantum channel in several network scenarios in the one-shot setting. We consider both the entanglement assisted and unassisted cases for the point to point quantum channel, the quantum multiple-access channel, the quantum channel with a state, and the quantum broadcast channel. We show that it is possible to near-optimally characterize the amount of communication that can be transmitted in these scenarios, using the position-based decoding strategy introduced in a prior study (A. Anshu, R. Jain, and N. Warsi, https://ieee.org/document/8399830). In the process, we provide a short and elementary proof of the converse for entanglement-assisted quantum channel coding in terms of the quantum hypothesis testing divergence [obtained earlier in the work of W. Matthews and S. Wehner, IEEE Trans. Inf. Theory 60, 7317–7329 (2014)]. Our proof has the additional utility that it naturally extends to various network scenarios mentioned above. Furthermore, none of our achievability results require a simultaneous decoding strategy, existence of which is an important open question in quantum Shannon theory.

Applications of position-based coding to classical communication over quantum channels

Recently, a coding technique called position-based coding has been used to establish achievability statements for various kinds of classical communication protocols that use quantum channels. In the present paper, we apply this technique in the entanglement-assisted setting in order to establish lower bounds for error exponents, lower bounds on the second-order coding rate, and one-shot lower bounds. We also demonstrate that position-based coding can be a powerful tool for analyzing other communication settings. In particular, we reduce the quantum simultaneous decoding conjecture for entanglement-assisted or unassisted communication over a quantum multiple access channel to open questions in multiple quantum hypothesis testing. We then determine achievable rate regions for entanglement-assisted or unassisted classical communication over a quantum multiple-access channel, when using a particular quantum simultaneous decoder. The achievable rate regions given in this latter case are generally suboptimal, involving differences of Rényi-two entropies and conditional quantum entropies.

Topological Interference Management With Decoded Message Passing

The topological interference management (TIM) problem studies partially-connected interference networks with no channel state information except for the network topology (i.e., connectivity graph) at the transmitters. In this paper, we consider a similar problem in the uplink cellular networks, while message passing is enabled at the receivers (e.g., base stations), so that the decoded messages can be routed to other receivers via backhaul links to help further improve network performance. For this TIM problem with decoded message passing (TIM-MP), we model the interference pattern by conflict digraphs, connect orthogonal access to the acyclic set coloring on conflict digraphs, and show that one-to-one interference alignment boils down to orthogonal access because of message passing. With the aid of polyhedral combinatorics, we identify the structural properties of certain classes of network topologies where orthogonal access achieves the optimal degrees-of-freedom (DoF) region in the information-theoretic sense. The relation to the conventional index coding with simultaneous decoding is also investigated by formulating a generalized index coding problem with successive decoding as a result of decoded message passing. The properties of reducibility and criticality are also studied, by which we are able to prove the linear optimality of orthogonal access in terms of symmetric DoF for the networks up to four users with all possible network topologies (218 instances). Practical issues of the tradeoff between the overhead of message passing and the achievable symmetric DoF are also discussed, in the hope of facilitating efficient backhaul utilization.

Achievable Rates of Gaussian Interference Channel with Multi‐Layer Rate‐Splitting and Successive Simple Decoding

The capacity bound of the Gaussian interference channel (IC) has received extensive research interests in recent years. Since the IC model consists of multiple transmitters and multiple receivers, its exact capacity region is generally unknown. One well‐known capacity achieving method in IC is Han‐Kobayashi (H‐K) scheme, which applies two‐layer rate‐splitting (RS) and simultaneous decoding (SD) as the pivotal techniques and is proven to achieve the IC capacity region within 1 bit. However, the computational complexity of SD grows exponentially with the number of independent signal layers, which is not affordable in practice. To this end, we propose a scheme which employs multi‐layer RS at the transmitters and successive simple decoding (SSD) at the receivers in the two‐transmitter and two‐receiver IC model and then study the achievable sum capacity of this scheme. Compared with the complicated SD, SSD regards interference as noise and thus has linear complexity. We first analyze the asymptotic achievable sum capacity of IC with equal‐power multi‐layer RS and SSD, where the number of layers approaches to infinity. Specifically, we derive the closed‐form expression of the achievable sum capacity of the proposed scheme in symmetric IC, where the proposed scheme only suffers from a little capacity loss compared with SD. We then present the achievable sum capacity with finite‐layer RS and SSD. We also derive the sufficient conditions where employing finite‐layer RS may even achieve larger sum capacity than that with infinite‐layer RS. Finally, numerical simulations are proposed to validate that multi‐layer RS and SSD are not generally weaker than SD with respect to the achievable sum capacity, at least for some certain channel gain conditions of IC.

Serially concatenated belief propagation decoder for low‐density parity‐check codes

A new decoding strategy of belief propagation (BP) for low-density parity-check codes is presented by serially concatenating the traditional simultaneous decoding (also known as flooding) and the informed dynamic decoding (IDD) (e.g. node-wise residual belief propagation (NW RBP) or informed variable-to-check (IVC) RBP). The frame error rates (FER) and bit error rates (BER) of the concatenated BP decoder outperform that of the non-concatenated single decoder. In addition, as the signal-to-noise ratio (SNR) increases, the average number of decoding iterations required for the concatenated BP decoder decreases, and tends to merge with that of the first sub-decoder in the high SNR region. Moreover, since the IDD strategies concerned in this paper require extra effort for the residual computation, and the sequential updating of IDD is much more time consuming than the simultaneous updating of flooding. Therefore, compared with using a single IDD, serially concatenating flooding and IDD reduces both the approximate decoding complexity and the decoding latency because the concatenated decoder is still dominated by the sub-decoder flooding.

EMG-Based Continuous and Simultaneous Estimation of Arm Kinematics in Able-Bodied Individuals and Stroke Survivors.

Among the potential biological signals for human-machine interactions (brain, nerve, and muscle signals), electromyography (EMG) widely used in clinical setting can be obtained non-invasively as motor commands to control movements. The aim of this study was to develop a model for continuous and simultaneous decoding of multi-joint dynamic arm movements based on multi-channel surface EMG signals crossing the joints, leading to application of myoelectrically controlled exoskeleton robots for upper-limb rehabilitation. Twenty subjects were recruited for this study including 10 stroke subjects and 10 able-bodied subjects. The subjects performed free arm reaching movements in the horizontal plane with an exoskeleton robot. The shoulder, elbow and wrist movements and surface EMG signals from six muscles crossing the three joints were recorded. A non-linear autoregressive exogenous (NARX) model was developed to continuously decode the shoulder, elbow and wrist movements based solely on the EMG signals. The shoulder, elbow and wrist movements were decoded accurately based only on the EMG inputs in all the subjects, with the variance accounted for (VAF) > 98% for all three joints. The proposed approach is capable of simultaneously and continuously decoding multi-joint movements of the human arm by taking into account the non-linear mappings between the muscle EMGs and joint movements, which may provide less effortful control of robotic exoskeletons for rehabilitation training of individuals with neurological disorders and arm impairment.

A Bayesian supervised dual-dimensionality reduction model for simultaneous decoding of LFP and spike train signals.

Neuroscientists are increasingly collecting multimodal data during experiments and observational studies. Different data modalities-such as EEG, fMRI, LFP, and spike trains-offer different views of the complex systems contributing to neural phenomena. Here, we focus on joint modeling of LFP and spike train data, and present a novel Bayesian method for neural decoding to infer behavioral and experimental conditions. This model performs supervised dual-dimensionality reduction: it learns low-dimensional representations of two different sources of information that not only explain variation in the input data itself, but also predict extra-neuronal outcomes. Despite being one probabilistic unit, the model consists of multiple modules: exponential PCA and wavelet PCA are used for dimensionality reduction in the spike train and LFP modules, respectively; these modules simultaneously interface with a Bayesian binary regression module. We demonstrate how this model may be used for prediction, parametric inference, and identification of influential predictors. In prediction, the hierarchical model outperforms other models trained on LFP alone, spike train alone, and combined LFP and spike train data. We compare two methods for modeling the loading matrix and find them to perform similarly. Finally, model parameters and their posterior distributions yield scientific insights.