Multistage Decoding Research Articles

Audio-visual saliency prediction with multisensory perception and integration

Audio-visual saliency prediction (AVSP) is a task that aims to model human attention patterns in the perception of auditory and visual scenes. Given the challenges associated with perceiving and combining multi-modal saliency features from videos, this paper presents a multi-sensory framework for AVSP. This framework is designed to extract audio, motion and image saliency features and integrate them effectively, which can then serve as a general architecture for the AVSP task. To obtain multi-sensory information, we develop a three-stream encoder that extracts audio, motion and image saliency features. In particular, we utilize a pre-trained encoder with knowledge related to image saliency to extract saliency features for each frame. The image saliency features are then incorporated with motion features using a spatial attention module. For motion features, 3D convolutional neural networks (CNNs) like S3D are commonly used in AVSP models. However, these networks are unable to effectively capture the global motion relationship in videos. To tackle this problem, we incorporate Transformer- and MLP-based motion encoders into the AVSP models. To learn joint audio-visual representations, an audio-visual fusion block is exploited to enhance the correlation between audio and visual motion features under the supervision of a cosine similarity loss in a self-supervised manner. Finally, a multi-stage decoder integrates audio, motion and image saliency features to generate the final saliency map. We evaluate our methods on six audio-visual eye-tracking datasets. Experimental results demonstrate that our method achieves compelling performance compared to the state-of-the-art methods. The source code is available at https://github.com/oraclefina/MSPI.

Multi-Stage Decoding in Dense Distributed Networks for MTC

Dense distributed networks have been proposed to fulfil the wireless connectivity demands in the 5th generation (5 G) and beyond. In this paper, a multi-stage decoding (MSD) scheme is proposed to enhance access connectivity for machine-type communication (MTC) in dense distributed networks. Different from the conventional single-stage decoding (SSD) scheme that performs local decoding at each access point (AP) independently, the MSD scheme exploits the local decoding information exchange between the APs and a central processing unit (CPU) to resolve access collisions and enhance access capacity with successive interference cancellation (SIC). To show the performance superiority of MSD to SSD, we analytically evaluate and compare the error probabilities of both the schemes by leveraging on techniques from stochastic geometry. Simulation results verify our analysis and indicate that the MSD scheme is able to effectively reduce the error probability and afford much higher access load compared to the SSD scheme, which makes it more suitable for massive MTC.

A no-tardiness job shop scheduling problem with overtime consideration and the solution approaches

In make-to-order manufacturing environments, overtime work is one of the effective and commonly used resources for expanding production capacity to ensure orders on-time delivery. However, if overtime work is not used reasonably and optimally, not only it cannot expedite the order completion, but also may lead to an increase in manufacturing costs. In order to use overtime work optimally, this paper presents a no-tardiness job shop scheduling problem with overtime work consideration (NTJSSP-OW) to minimize the total earliness inventory and overtime work costs simultaneously. A mathematical model is formulated and a hybrid genetic algorithm with simulated annealing (GASA) is proposed to solve it. Nine algorithms are also selected for performance comparisons. Unlike the traditional job shop scheduling problem, when solving NTJSSP-OW by heuristics and meta-heuristics, no-tardiness constraint is more likely to lead to infeasible solutions. So a multi-stage decoding scheme with a reconstructing rule is developed to ensure feasible solution. In order to extend the search space, a dispatching rule-based population initialization procedure and a repairing mechanism are provided. Comprehensive experiments are conducted on 14 modified benchmark problems, and non-parametric statistical tests like the Friedman test and post-hoc Nemenyi test are performed for the experimental results. Further systematic analyses indicate that GASA has significantly faster convergence on 90% of all test instances, and its global search ability outperforms other competing algorithms for 12 out of 14 instances.

Continuous-variable quantum key distribution with low-complexity information reconciliation.

Information reconciliation is an essential step of continuous-variable quantum key distribution (CV-QKD) because its efficiency affects both the maximum transmission distance and the final secret key rate of the whole system. Slice reconciliation can achieve high secret key rates for the CV-QKD systems under high signal-to-noise ratios (SNRs). However, it suffers from the high designing cost of the error-correcting codes in the multi-level coding and multi-stage decoding step. To reconcile one set of data with fixed SNR, multiple error-correcting codes with different code rates need to be designed. If the channel environment changed, the code rates need to change along to maintain high reconciliation efficiency, making it complicated and costly. In this paper, we introduce polar codes into slice reconciliation and proposed a scheme that is simply constructed, effectively performed, and well adapted to the systems with wide-range and variational SNRs. Simulation results show that the proposed scheme offers reconciliation efficiency over 95% for the systems with SNRs range from 1 to 10, which can support the systems with high secret key rates. The decrease of complexity and the increase of secret key rate have great significance for a practical CV-QKD system.

Multiedge-type low-density parity-check codes for continuous-variable quantum key distribution

Continuous-variable quantum key distribution utilizes an ensemble of coherent states of light to distribute secret encryption keys between two parties. One of the challenges is thereby the requirement of capacity approaching error correcting codes in the low signal-to-noise (SNR) regime (SNR < 0 dB). Multilevel coding (MLC) combined with multistage decoding (MSD) can solve this challenge in combination with multi-edge-type low-density parity-check (MET-LDPC) codes which are ideal for low code rates in the low SNR regime due to degree-one variable nodes. However, the complexity of designing such highly efficient codes remains an open issue. Here, we introduce the concept of generalized extrinsic information transfer (G-EXIT) charts for MET-LDPC codes and demonstrate how this tool can be used to analyze their convergence behavior. We calculate the capacity for each level in the MLC-MSD scheme and use G-EXIT charts to exemplary find codes for some given rates which provide a better decoding threshold compared to previously reported codes. In comparison to the traditional density evolution method, G-EXIT charts offer a simple and fast asymptotic analysis tool for MET-LDPC codes.

Cube-Split: A Structured Grassmannian Constellation for Non-Coherent SIMO Communications

In this paper, we propose a practical structured constellation for non-coherent communication with a single transmit antenna over Rayleigh flat and block fading channel without instantaneous channel state information. The constellation symbols belong to the Grassmannian of lines and are defined up to a complex scaling. The constellation is generated by partitioning the Grassmannian of lines into a collection of bent hypercubes and defining a mapping onto each of these bent hypercubes such that the resulting symbols are approximately uniformly distributed on the Grassmannian. With a reasonable choice of parameters, this so-called cube-split constellation has higher packing efficiency, represented by the minimum distance, than the existing structured constellations. Furthermore, exploiting the constellation structure, we propose low-complexity greedy symbol decoder and log-likelihood ratio computation, as well as an efficient way to associate it to a multilevel code with multistage decoding. Numerical results show that the performance of the cube-split constellation is close to that of a numerically optimized constellation and better than other structured constellations. It also outperforms a coherent pilot-based scheme in terms of error probability and achievable data rate in the regime of short coherence time and large constellation size.

Spatially Modulated Integer-Forcing Transceivers With Practical Binary Codes

A new practical multiple-input multiple-output (MIMO) transceiver technique called spatially modulated integer-forcing (SM-IF) is proposed for hybrid beamforming array systems, which combines generalized spatial modulation (GSM) with integer-forcing (IF) MIMO multiplexing transmission to achieve improved spectral efficiency with low system complexity. In the proposed scheme, the transmitter first activates a subset of antennas using analog beamforming and delivers an SM information stream via the index of the activated antenna group. Then MIMO digital multiplexing is applied to send multiple MIMO information streams via multilevel coding (MLC) with binary channel codes through the activated antennas. At the receiver side, the receiver first estimates the index of the activated antenna group to recover the SM stream and then recovers the MIMO streams by IF sum decoding in conjunction with multi-stage decoding. The proposed scheme provides a unified coding framework to achieve a synergistic gain by integrating off-the-shelf practical binary codes and low complexity IF decoding into hybrid beamforming array systems. By extensive link-level numerical simulations, the proposed scheme is shown to outperform both the conventional plain IF MIMO and plain SM approaches. The results demonstrate that the proposed SM-IF can be a promising transceiver technique implementable in practical hybrid beamforming array systems.

Multilevel Constructions: Coding, Packing and Geometric Uniformity

Lattice and special nonlattice multilevel constellations constructed from binary codes, such as Constructions A, C, and D, have relevant applications in Mathematics (sphere packing) and in Communication (multi-stage decoding and efficient vector quantization). In this work, we explore some properties of Construction C, in particular its geometric uniformity. We then propose a new multilevel construction, inspired by bit interleaved coded modulation (BICM), that we call Construction $C^\star $ . We investigate the geometric uniformity, latticeness, and minimum distance properties of Construction $C^\star $ , and discuss its superior packing efficiency when compared to Construction C.

Space–Time Signal Design for Multilevel Polar Coding in Slow Fading Broadcast Channels

Slow fading broadcast channels can model a wide range of applications in wireless networks. Due to delay requirements and the unavailability of the channel state information at the transmitter (CSIT), these channels for many applications are non-ergodic. The appropriate measure for designing signals in non-ergodic channels is the outage probability. In this paper, we provide a method to optimize space-time block codes (STBCs) based on the outage probability at moderate SNRs. Multilevel polar coded-modulation is a new class of coded-modulation techniques that benefits from low-complexity decoders and simple rate matching. In this paper, we derive the outage optimality condition for multistage decoding and propose a rule for determining component code rates. We also derive an upper bound on the outage probability of STBCs for designing the set-partitioning-based labeling. Finally, due to the optimality of the outage-minimized STBCs for long codes, we introduce a novel method for the joint optimization of short-to-moderate length polar codes and STBCs.

Multilevel LDPC Lattices With Efficient Encoding and Decoding and a Generalization of Construction $\text{D}'$

Lattice codes are elegant and powerful structures that not only can achieve the capacity of the AWGN channel but are also a key ingredient to many multiterminal schemes that exploit linearity properties. However, constructing lattice codes that can realize these benefits with low complexity is still a challenging problem. In this paper, efficient encoding and decoding algorithms are proposed for multilevel binary LDPC lattices constructed via Construction D' whose complexity is linear in the total number of coded bits. Moreover, a generalization of Construction D' is proposed that relaxes some of the nesting constraints on the parity-check matrices of the component codes, leading to a simpler and improved design. Based on this construction, low-complexity multilevel LDPC lattices are designed whose performance under multistage decoding is comparable to that of polar lattices and close to that of low-density lattice codes (LDLC) on the power-unconstrained AWGN channel.

Improving electron micrograph signal-to-noise with an atrous convolutional encoder-decoder

We present an atrous convolutional encoder-decoder trained to denoise electron micrographs. It consists of a modified Xception backbone, atrous convoltional spatial pyramid pooling module and a multi-stage decoder. Our neural network was trained end-to-end using 512 × 512 micrographs created from a large dataset of high-dose ( > 2500 counts per pixel) micrographs with added Poisson noise to emulate low-dose ( ≪ 300 counts per pixel) data. It was then fine-tuned for high dose data (200–2500 counts per pixel). Its performance is benchmarked against bilateral, Gaussian, median, total variation, wavelet, and Wiener restoration methods with their default parameters. Our network outperforms their best mean squared error and structural similarity index performances by 24.6% and 9.6% for low doses and by 43.7% and 5.5% for high doses. In both cases, our network’s mean squared error has the lowest variance. Source code and links to our high-quality dataset and pre-trained models are available at https://github.com/Jeffrey-Ede/Electron-Micrograph-Denoiser.

Throughput-Based Design for Polar-Coded Modulation

Typically, forward error correction (FEC) codes are designed based on the minimization of the error rate for a given code rate. However, for applications that incorporate hybrid automatic repeat request (HARQ) protocol and adaptive modulation and coding, the throughput is a more important performance metric than the error rate. Polar codes, a new class of FEC codes with simple rate matching, can be optimized efficiently for maximization of the throughput. In this paper, we aim to design HARQ schemes using multilevel polar-coded modulation (MLPCM). Thus, we first develop a method to determine a set-partitioning-based bit-to-symbol mapping for high-order quadrature amplitude modulation (QAM) constellations. We simplify the log-likelihood ratio estimation of set-partitioned QAM constellations for a multistage decoder, and we introduce a set of algorithms to design throughput-maximizing MLPCM for the successive cancellation decoding (SCD). These codes are specifically useful for non-combining, chase-combining, and incremental redundancy HARQ protocols. Furthermore, since optimized codes for SCD are not optimal for SC list decoders (SCLDs), we propose a rate matching algorithm to find the best rate for SCLD while using the polar codes optimized for SCD. The resulting codes provide throughput close to the capacity with low decoding complexity when used with HARQ protocols.

High-Rate Regular APSK Constellations

The majority of modern communication systems adopt quadrature amplitude modulation (QAM) constellations as transmission schemes. Due to their square structure, however, QAM do not provide satisfying protection to phase noise effects, as the number of constellation points grows, increasing at the same time their peak-to-average-power ratio. This requires an expensive power amplifier and an oscillator at the transmitter to guarantee low distortion, complicating the adoption of dense transmission schemes in practical high-data rate systems. In this paper, we construct a coded modulation scheme based on regular amplitude and phase shift keying modulations. We propose a novel multilevel coding labeling for the constellation points separating the amplitude and phase domains. We provide a novel multistage decoding scheme allowing for a low-complexity log-likelihood ratio calculation for soft-input decoding of component codes, along with a suitable rate design. Finally, we compare the proposed scheme with the state-of-the-art QAM constellations and optimize the constellations in the presence of phase noise.

Adaptive Modulation and Superposition Coding for MIMO Data Transmission Using Unequal Error Protection and Ordered Successive Interference Cancellation Techniques

This paper proposes and discusses an efficient multiple input multiple output (MIMO) system for adaptive modulation coding (AMC) based on unequal error protection (UEP) and superposition coding (SPC) by exploiting the features of sub channel partitioning. Using AMC, an appropriate order of modulation and code rate to suit the channel state information (CSI) can be selected. The realization of UEP is achieved through adaptive allocation and transmission of high and low-priority data signals over high and low quality sub channels respectively. A multistage decoding (MSD) receiver with the ordered successive interference cancellation (OSIC) technique is employed in the receiver by using two linear receiver signal combiner (RSC) techniques: zero-forcing (ZF) and minimum mean square error (MMSE). The data priority can be distinguished by the signal-to-noise ratio (SNR) and can be categorized as mode-B and mode-G. The simulation results show that the SNR performance enhancement for the UEP- MIMO scheme is approximately 9.4 dB compared to the others schemes. In addition, for the UEP-MIMO scheme, the overall data system transmission from mode-B outperforms that of mode-G with SNR gains of 8.2 dB compared to the 7.5 dB for the UEP-SISO scheme. Also, MMSE-SPC also outperforms the ZF-SPC with a 3 dB SNR at a bit error rate (BER) of 10-3.

TRELLIS-CODED MODULATION FOR LINEAR DISTORTION CHANNELS

Introduction: Real communication channels used for high-speed data transmission are characterized by substantial linear distortions leading to a significant decrease in the effectiveness of the known coded modulation techniques developed for distortion-free channels. This means that a modified coded modulation technique should be created, which would take into account the channel distortions. Purpose: Using multidimensional signal sets to build up multilevel trellis-coded modulation constructions with multidimensional constellations and multi-stage decoding, matched to the channels with linear distortions, and study of their main characteristics. Results: A new approach was developed to the design of trellis-coded modulation constructions for channels with linear distortions, based on the combination of two techniques: decomposition of the channel into a set of independent memoryless subchannels and time-frequency multilevel coding. The high level of flexibility of the proposed trellis-coded modulation constructions makes it possible to effectively take into account linear channel distortions and compensate for them, due to the rational choice of the number of the subchannels and optimization of the energy distribution between the subchannels. It also provides a wide-range tradeoff between performance and decoding complexity. The relationships between the basic parameters of the proposed class of trellis-coded modulation constructions were obtained in an explicit form. These relationships allow you to provide tradeoffs between the transmission rate, the minimum distance, and the decoding complexity. Examples of multilevel trellis-coded modulation constructions based on multidimensional Barnes — Wall lattices are given. A comparative study showed that as the value of the permissible decoding complexity increases, constructions with larger values of both the number of subchannels and the cardinality of the signal constellation become more preferable. Practical significance: The proposed class of trellis-coded modulation constructions allows you to effectively take into account and to overcome the influence of linear channel distortions common in communication channels used for high-speed data transmission. An additional advantage of these constructions is a relatively small value of the crest factor of transmitted signals and thereby more efficient use of the transmitter's power.

Low-Complexity Soft-Decision Detection of Coded OFDM With Index Modulation

In this paper, the low-complexity soft-decision detectors of the orthogonal frequency division multiplexing with index modulation (OFDM-IM) in both single- and two-level coded modulation systems are studied. In the single-level coded OFDM-IM, the bitwise Markov chain Monte Carlo (b-MCMC) detector can overcome the drawback of the conventional symbolwise MCMC detector that the signal vectors obtained by Gibbs sampling are trapped in the activation pattern (AP) of the initial vector. By integrating with the randomized step further to alleviate the stalling problem, the randomized b-MCMC detector is proposed. In the two-level coded OFDM-IM, the framework of iterative multistage decoding (IMSD) with two component detectors for AP and symbol information, respectively, is employed. To reduce the complexity of IMSD, the efficient processing of the multiplicative interference in the component detectors is proposed. Finally, the effectiveness of the proposed detectors is verified by computer simulations.

Channel decomposition for multilevel codes over multilevel and partial erasure channels

We introduce the Multilevel Erasure Channel (MEC) for binary extension field alphabets. The channel model is motivated by applications such as non-volatile multilevel read storage channels. Like the recently proposed \begin{document}$q$\end{document} -ary partial erasure channel (QPEC), the MEC is designed to capture partial erasures. The partial erasures addressed by the MEC are determined by erasures at the bit level of the \begin{document}$q$\end{document} -ary symbol representation. In this paper we derive the channel capacity of the MECand give a multistage decoding scheme on the MEC using binary codes. We also present a low complexity multistage \begin{document}$p$\end{document} -ary decoding strategy for codes on the QPEC when \begin{document}$q = p^k$\end{document} .We show that for appropriately chosen component codes, capacity on the MEC and QPEC may be achieved.

Multilevel Coding Scheme for Integer-Forcing MIMO Receivers With Binary Codes

An integer-forcing (IF) linear multiple-input multiple-output (MIMO) receiver has recently been proposed, which is theoretically shown to achieve near capacity with almost the same complexity as that of conventional linear receivers. The key idea is that the receiver attempts to directly decode integer-linear combinations of codewords. To ensure that this sum-decoding operation is feasible, in previous works, lattice codes over $\mathbb {Z}_{q}$ were employed. Although those codes can attain good theoretical performance, however, its implementation complexity can be considerably high in practice, especially when $q$ is large to support high-order modulations. In this paper, we propose a practical multilevel coding scheme for IF MIMO, in which multilevel encoding composed of binary linear codes $(q = 2)$ in conjunction with the natural mapping is employed on the transmitter side and multistage decoding adapted to the IF operation is employed on the receiver side. The performance of the proposed scheme is extensively evaluated both analytically and numerically, showing that the gain of IF over conventional receivers is indeed achievable in practical settings with almost the same complexity. Our results imply that the proposed IF MIMO can be an attractive solution for the 5G communications due to its ability of supporting high spectral efficiency with low complexity.

High-efficiency reconciliation for continuous variable quantum key distribution

Quantum key distribution (QKD) is the most mature application of quantum information technology. Information reconciliation is a crucial step in QKD and significantly affects the final secret key rates shared between two legitimate parties. We analyze and compare various construction methods of low-density parity-check (LDPC) codes and design high-performance irregular LDPC codes with a block length of 106. Starting from these good codes and exploiting the slice reconciliation technique based on multilevel coding and multistage decoding, we realize high-efficiency Gaussian key reconciliation with efficiency higher than 95% for signal-to-noise ratios above 1. Our demonstrated method can be readily applied in continuous variable QKD.

Optimal probabilistic dense coding schemes

Dense coding with non-maximally entangled states has been investigated in many different scenarios. We revisit this problem for protocols adopting the standard encoding scheme. In this case, the set of possible classical messages cannot be perfectly distinguished due to the non-orthogonality of the quantum states carrying them. So far, the decoding process has been approached in two ways: (i) The message is always inferred, but with an associated (minimum) error; (ii) the message is inferred without error, but only sometimes; in case of failure, nothing else is done. Here, we generalize on these approaches and propose novel optimal probabilistic decoding schemes. The first uses quantum-state separation to increase the distinguishability of the messages with an optimal success probability. This scheme is shown to include (i) and (ii) as special cases and continuously interpolate between them, which enables the decoder to trade-off between the level of confidence desired to identify the received messages and the success probability for doing so. The second scheme, called multistage decoding, applies only for qudits ($d$-level quantum systems with $d>2$) and consists of further attempts in the state identification process in case of failure in the first one. We show that this scheme is advantageous over (ii) as it increases the mutual information between the sender and receiver.