Hybrid Decoding Research Articles

Performance Enhancement of MmWave MIMO Systems Using Deep Learning Framework

In order to obtain beamforming gains and prevent high pathloss in millimeter wave (mmWave) systems, large number of antennas is employed. Digital precoders are difficult to implement with many antennas because of hardware constraints, while analog precoders have limited performance. In this paper, hybrid precoding based on a deep learning framework, HybridPrecodingNet, is proposed, which uses large-scale information to predict the parameters of hybrid precoders and decoders. The statistics of the channel covariance matrix are applied to design the hybrid precoders and decoders. The proposed HybridPrecodingNet at the receiver is applied for the channel estimation and design of hybrid decoders. In our proposed framework, the structure of HybridPrecodingNet is trained to learn how to optimize the hybrid precoder and decoder for maximum spectral efficiency. Comparison between different precoding techniques is provided. Results show that HybridPrecodingNet approaches the sub-optimal solution and gives significant spectral efficiency enhancement.

Design of Improved BP Decoders and Corresponding LT Code Degree Distribution for AWGN Channels

This paper presents the performance of a hard decision belief propagation (HDBP) decoder used for Luby transform (LT) codes over additive white Gaussian noise channels; subsequently, three improved HDBP decoders are proposed. We first analyze the performance improvement of the sorted ripple and delayed decoding process in a HDBP decoder; subsequently, we propose ripple-sorted belief propagation (RSBP) as well as ripple-sorted and delayed belief propagation (RSDBP) decoders to improve the bit error rate (BER). Based on the analysis of the distribution of error encoded symbols, we propose a ripple-sorted and threshold-based belief propagation (RSTBP) decoder, which deletes low-reliability encoded symbols, to further improve the BER. Degree distribution significantly affects the performance of LT codes. Therefore, we propose a method for designing optimal degree distributions for the proposed decoders. Through simulation results, we demonstrate that the proposed RSBP and RSDBP decoders provide significantly better BER performances than the HDBP decoder. RSDBP and RSTDP combined with the proposed degree distributions outperformed state-of-the-art degree distributions in terms of the number of encoded symbols required to recover an input symbol correctly (NERRIC) and the frame error rate (FER). For a hybrid decoder formulated by combining RSDBP with a soft decision belief propagation decoder, the proposed degree distribution outperforms the other degree distributions in terms of decoding complexity.

An Unfolded Pipelined Polar Decoder With Hybrid Number Representations for Multi-User MIMO Systems

Next generation wireless communication introduces ultra high speed and low latency requirements to baseband processors, especially for multi-user multiple-input-multiple-output (MU-MIMO) systems. Polar codes, as the channel coding scheme in the control plane of 5G eMBB scenario, still cannot satisfy the hardware implementation requirements of polar decoders for MU-MIMO systems. In this brief, a high throughput and low latency polar decoder is proposed for MU-MIMO systems. The decoding architecture is fully unfolded to maximize hardware parallelism, and is fully pipelined by decoupling the round-trip scheduling datapath to enable simultaneous processing of multiple codewords. Besides, a hybrid decoding datapath is introduced by proposing complementary decoding units with mixed data representations to reduce hardware complexity. For (1024, 512) polar codes, a decoding throughput of 100.6 Gbps is achieved, and the latency is 0.43 us when 36 users are processed simultaneously.

An Area-Efficient Hybrid Polar Decoder With Pipelined Architecture

As the first kind of capacity-achieving forward error correction (FEC) codes, polar codes have attracted much research interest recently. Compared with traditional FEC codes, polar codes show better error correction performance when successive cancellation list (SCL) decoding with cyclic redundancy check is adopted. However, its serial decoding nature and high complexity of list management lead to its low throughput. Though the adaptive SCL decoding and hybrid decoding can improve the throughput, it comes at cost of implementation area. In this paper, we propose a pipelined hybrid decoding procedure and the corresponding hardware architecture to improve the area efficiency. In our design, the idle decoding cores are employed for successive cancellation (SC) decoding when SCL decoding is not working. The SCL decoding will be activated when the SC decoding fails. Different decoding cores work according to their own operation sequences and share one common processing array to improve the utilization ratio of processing elements. Constant receiving interval is supported with the design of input buffer to store all received codewords. A software platform is established to optimize the design parameters for each module of decoder. Moreover, the corresponding architecture is implemented using 65nm technology. Experimental results show that the proposed decoder can achieve a similar error correction performance with the SCL decoding with list size 16. Compared to the state-of-the-art available hybrid decoder, our proposed pipelined hybrid decoder is $3.07\times $ more area efficient.

Approaching the Normal Approximation of the Finite Blocklength Capacity within 0.025dB by Short Polar Codes

In this letter, we explore the performance limits of short polar codes and find that the maximum likelihood (ML) performance of a simple CRC-polar concatenated scheme can approach the normal approximation of the finite blocklength capacity. To explore the performance limits, CRC-polar concatenated codes are first optimized according to the minimum weight distribution. Then, CRC-aided hybrid decoding (CA-HD) algorithm with two steps is proposed to approach the ML performance. In the first step, the received sequence is decoded by the adaptive successive cancellation list (ADSCL) decoding. In the second step, the CRC bits of the survival paths in ADSCL are recalculated and CRC-aided sphere decoding with a reasonable initial radius calculated by the newly survival paths is used. The simulation results show that CRC-polar concatenated code with codeword length 128 and code rate 1/2 can achieve within about 0.025 dB of the normal approximation of the finite blocklength capacity at the block error rate 10−3.

Triple-adjacent-frame generative network for blind video motion deblurring

Photos and videos captured by handheld imaging devices are often accompanied by unwanted blur because of hand jitters and fast movement of objects during the exposure time. Most previous studies discussed single image deblurring and video deblurring but neglected detailed analyses of the spatiotemporal continuity between adjacent frames, which limits the deblurring effect. We propose a novel end-to-end blind video motion deblurring network that takes triple adjacent frames as input to deblur a blurry video frame. In our approach, a bidirectional temporal feature transfer between triple adjacent frames is implemented to pass the latent features of the central frame on to a group encoder of its neighbors. Then, a hybrid decoder decodes group features and estimates a sharper video frame relative to the central frame. Experimental results show that our model outperforms previous excellent methods in terms of traditional metrics (PSNR and SSIM) and visual quality within an acceptable time cost. The code is available at https://github.com/BITLIULONGEE/Triple-Adjacent-Frame-Generative-Network.

A multi-start variable neighbourhood descent algorithm for hybrid flowshop rescheduling

Abstract Hybrid flowshop (HFS) rescheduling has important applications in modern industry. Much of the existing research on HFS rescheduling only consider one type of dynamic event. However, realistic production systems often encounter several types of dynamic events. In this paper, HFS rescheduling considering simultaneously three types of dynamic events (i.e. machine breakdown, new job arrival and job release variation) is studied. The mathematical model of minimizing makespan and system instability is established. The approaches for calculating lower and upper bounds of the two optimization objectives are developed. A Multi-Start Variable Neighbourhood Descent (MSVND) algorithm is proposed for the HFS rescheduling. In the MSVND, a hybrid decoding is developed. To improve the intensification of the MSVND, a Fruit Fly Optimization (FFO)-based local search and an enhanced FFO-based local search are designed to improve the best solution found so far. Moreover, to enhance the diversification, a simulated annealing-like acceptance criterion is employed to determine whether the local optima can be accepted, and a restart strategy with perturbation is devised to guide the search to the so far unexplored area. Extensive experimental comparisons on 150 instances verify the effectiveness of the devised strategies. Further, a comprehensive comparison against seven highly efficient algorithms demonstrates the superiority of the MSVND.

Mathematical Modeling and Optimization of Energy-Conscious Flexible Job Shop Scheduling Problem With Worker Flexibility

This paper addresses the dual-resource constrained flexible job shop scheduling problem (DRCFJSP) with minimizing energy consumption. It is the first to study the energy-conscious DRCFJSP with turn OFF/ ON strategy. Different from the classical FJSP, the worker flexibility is considered in DRCFJSP. First, in order to solve this problem, we propose two mixed integer linear programming (MILP) models based on two modeling ideas, namely, idle time and idle energy. Because DRCFJSP is NP-hard, then we propose an efficient variable neighborhood search (VNS) algorithm. In the proposed VNS algorithm, eight neighborhood structures are designed to generate neighboring solutions. In addition, four energy-saving decoding approaches are specifically designed, in which two energy-saving strategies, namely, postponing strategy and turn OFF/ ON strategy are designed. Finally, the MILP model, the energy-conscious decoding methods, and the VNS are evaluated on numerical tests, whose effectiveness is shown by the experimental results. The experimental results show that the MILP model based on idle energy performs better than the model based on idle time idea, and the greedy hybrid decoding method outperforms the other three decoding methods. Moreover, the proposed VNS with eight neighborhood structures is a very competitive algorithm for the energy-conscious DRCFJSP.

Cascaded Channel Model, Analysis, and Hybrid Decoding for Spin-Torque Transfer Magnetic Random Access Memory

Spin-torque transfer magnetic random access memory (STT-MRAM) is a promising non-volatile memory technology widely considered to replace dynamic random access memory (DRAM). However, there still exist critical technical challenges to be tackled. For example, process variation and thermal fluctuation may lead to both write errors and read errors, severely affecting the reliability of the memory array. In this paper, we first propose a novel cascaded channel model for STT-MRAM that facilitates fast error rate simulations and more importantly the theoretical design and analysis of memory sensing and channel coding schemes. We analyze the raw bit error rate and probabilities of dominant error events, and derive the maximum likelihood decision criterion and the log-likelihood ratio of the cascaded channel. Based on these works, we further propose a two-stage hybrid decoding algorithm for extended Hamming codes for STT-MRAM. Simulation results demonstrate the effectiveness of the proposed hybrid decoding algorithm for improving the reliability of STT-MRAM in the presence of both write errors and read errors and supporting its potential applications, such as replacing DRAM.

Fronthaul-Limited Uplink OFDMA in Ultra-Dense CRAN With Hybrid Decoding

In an ultra-dense cloud radio access network (UD-CRAN), a large number of remote radio heads (RRHs), typically employed as simple relay nodes, are distributed in a target area, which could even outnumber their served users. However, one major challenge is that the large amount of information required to be transferred between each RRH and the central processor (CP) for joint signal processing can easily exceed the capacity of the fronthaul links connecting them. This motivates our study in this paper on a new hybrid decoding scheme where in addition to quantizing and forwarding the received signals for joint decoding at the CP, i.e. forward-and-decode (FaD) as in the conventional CRAN, the RRHs can locally decode-and-forward (DaF) the user messages to save the fronthaul capacity. In particular, we consider the uplink transmission in an orthogonal frequency division multiple access (OFDMA)-based UD-CRAN, where the proposed hybrid decoding is performed on each OFDMA sub-channel (SC). We study a joint optimization of the processing mode selections (DaF or FaD), user-SC assignments and the users' transmit power allocations over all SCs to maximize their weighted-sum-rate subject to the RRHs' individual fronthaul capacity constraints and the users' individual power constraints. Although the problem is non-convex, we propose a Lagrange duality based solution, which can be efficiently computed with good accuracy. Further, we propose a low-complexity greedy algorithm which is shown to achieve close to the optimal performance under practical setups. Simulation results show the promising throughput gains of the proposed designs with hybrid decoding, compared to the existing schemes that perform either DaF or FaD at all SCs.

Efficient Decoding Schemes of LDPC Codes for the Layered-Division Multiplexing Systems in ATSC 3.0

A low-complexity decoding scheme of low-density parity-check codes for the layered-division multiplexing (LDM) systems in ATSC 3.0 is proposed. Based on the unique characteristic of the LDM and the special structure of a parity-check matrix (PCM) for the core layer, the proposed scheme applies the sum-product algorithm (SPA) to only a part of the PCM. For this reason, it is referred to as “the partial SPA,” compared with the conventional scheme, denoted by “the full SPA.” Clearly, it has a significantly lower complexity than the full SPA. Furthermore, we propose hybrid decoders employing both the full SPA and the partial SPA to achieve a better performance, while maintaining their complexity low. Numerical results show that the proposed schemes have a good performance enough to perform successive interference cancellation for the enhanced layer of the LDM systems in ATSC 3.0 as well as they have a low-complexity merit depending on system parameters. Since they only schedule the decoding procedure without modifying the decoder structure, they also have a benefit in the implementation of decoders.

A hybrid decoding of Reed–Muller codes

In this article, a hybrid decoding algorithm for Reed–Muller codes is presented. Unlike the conventional algorithm, the presented algorithm ends recursive decomposition when [Formula: see text] and [Formula: see text] appeared. A simplified maximum-likelihood algorithm based on fast Hadamard transform is also exploited to decode the systematic code through its special structure. As a result, the presented hybrid decoding algorithm reduces the number of floating-point multiplications significantly as compared with the conventional algorithms. In addition, the new algorithm has better error performance than the conventional ones.

A Hardware Decoder Architecture for General String Matching Technique

This paper proposes a low cost hardware decoder design and architecture that supports general string matching (SM) using both a primary reference buffer (PRB) and a secondary reference buffer (SRB). An SM decoding module mainly performs string copy operation. The SM decoding module can also support intra block copy and palette decoding because both are actually two special cases of general string copy. The proposed architecture has four pipeline stages. The theoretical lowest system clock frequency required to guarantee worst case string copy operation using single-port SRAM is lower than the video output pixel rate, which is the product of frame resolution and frame rate. The design uses four pieces of SRAMs with size of 24K bytes, 16.5K bytes, 4K bytes, and 3K bytes, respectively. The first one is specific to SM and the other three are shared by traditional hybrid decoding operations. The SM decoding module has 69.5K logic gate count and can achieve 60 fps real time SM decoding for 4K ( $4096\times 2160$ ) video at 218 MHz working frequency.

Switching Markov decoders for asynchronous trajectory reconstruction from ECoG signals in monkeys for BCI applications

Brain-Computer Interfaces (BCIs) are systems which translate brain neural activity into commands for external devices. BCI users generally alternate between No-Control (NC) and Intentional Control (IC) periods. NC/IC discrimination is crucial for clinical BCIs, particularly when they provide neural control over complex effectors such as exoskeletons. Numerous BCI decoders focus on the estimation of continuously-valued limb trajectories from neural signals. The integration of NC support into continuous decoders is investigated in the present article. Most discrete/continuous BCI hybrid decoders rely on static state models which don’t exploit the dynamic of NC/IC state succession. A hybrid decoder, referred to as Markov Switching Linear Model (MSLM), is proposed in the present article. The MSLM assumes that the NC/IC state sequence is generated by a first-order Markov chain, and performs dynamic NC/IC state detection. Linear continuous movement models are probabilistically combined using the NC and IC state posterior probabilities yielded by the state decoder. The proposed decoder is evaluated for the task of asynchronous wrist position decoding from high dimensional space-time-frequency ElectroCorticoGraphic (ECoG) features in monkeys. The MSLM is compared with another dynamic hybrid decoder proposed in the literature, namely a Switching Kalman Filter (SKF). A comparison is additionally drawn with a Wiener filter decoder which infers NC states by thresholding trajectory estimates. The MSLM decoder is found to outperform both the SKF and the thresholded Wiener filter decoder in terms of False Positive Ratio and NC/IC state detection error. It additionally surpasses the SKF with respect to the Pearson Correlation Coefficient and Root Mean Squared Error between true and estimated continuous trajectories.

GLDPC-Staircase AL-FEC codes: a fundamental study and new results

This paper provides fundamentals in the design and analysis of Generalized Low-Density Parity Check (GLDPC)-Staircase codes over the erasure channel. These codes are constructed by extending an LDPC-Staircase code (base code) using Reed-Solomon (RS) codes (outer codes) in order to benefit from more powerful decoders. The GLDPC-Staircase coding scheme adds, in addition to the LDPC-Staircase repair symbols, extra-repair symbols that can be produced on demand and in large quantities, which provides small rate capabilities. Therefore, these codes are extremely flexible as they can be tuned to behave either like predefined rate LDPC-Staircase codes at one extreme, or like a single RS code at another extreme, or like small rate codes. Concerning the code design, we show that RS codes with “quasi” Hankel matrix-based construction fulfill the desired structure properties, and that a hybrid (IT/RS/ML) decoding is feasible that achieves maximum likelihood (ML) correction capabilities at a lower complexity. Concerning performance analysis, we detail an asymptotic analysis method based on density evolution (DE), extrinsic information transfer (EXIT), and the area theorem. Based on several asymptotic and finite length results, after selecting the optimal internal parameters, we demonstrate that GLDPC-Staircase codes feature excellent erasure recovery capabilities, close to that of ideal codes, both with large and very small objects. From this point of view, they outperform LDPC-Staircase and Raptor codes and achieve correction capabilities close to those of RaptorQ codes. Therefore, all these results make GLDPC-Staircase codes a universal Application-Layer FEC (AL-FEC) solution for many situations that require erasure protection such as media streaming or file multicast transmission.

Noninvasive EEG correlates of overground and stair walking.

Automated walking intention detection remains a challenge in lower-limb neuroprosthetic systems. Here, we assess the feasibility of extracting motor intent from scalp electroencephalography (EEG). First, we evaluated the corticomuscular coherence between central EEG electrodes (C1, Cz, C2) and muscles of the shank and thigh during walking on level ground and stairs. Second, we trained decoders to predict the linear envelope of the surface electromyogram (EMG). We observed significant EEG-led corticomuscular coupling between electrodes and sEMG (tibialis anterior) in the high delta (3-4 Hz) and low theta (4-5 Hz) frequency bands during level walking, indicating efferent signaling from the cortex to peripheral motor neurons. The coherence was increased between EEG and vastus lateralis and tibialis anterior in the delta band (<; 2 Hz) during stair ascent, indicating a task specific modulation in corticomuscular coupling. However, EMG was the leading signal for biceps femoris and gastrocnemius coherence during stair ascent, possibly representing afferent feedback loops from periphery to the motor cortex. Decoder validation showed that EEG signals contained information about the sEMG patterns during over ground walking, however, the accuracy of the predicted sEMG patterns decreased during the stair condition. Overall, these initial findings support the feasibility of integrating sEMG and EEG into a hybrid decoder for volitional control of lower limb neuroprostheses.

Channel quality indicator decoding for talk‐around direct communications based on IEEE 802.16.1a

Codeword decoding schemes are proposed for the channel quality indicator (CQI) of talk-around direct communications based on IEEE 802.16.1a that supports public protection and disaster relief operations. Since a CQI payload is encoded by using near-orthogonal block encoding, a non-coherent correlation method is employed at the receiver to find the CQI index with maximum correlation. To enhance the decoding performance in high mobility environments, a differential decoding scheme is developed. By combining the correlation method and the differential decoder, a new hybrid decoding scheme is proposed. Since the proposed hybrid decoder uses the correlation approach in slow fading channels and exploits the differential decoding in fast fading channels, its decoding performance is robust to the rate of channel variation. Through theoretical analysis and numerical simulations in direction communication environments, it is shown that the proposed non-coherent decoders are advantageous than a coherent decoding scheme, and the performance of the proposed non-coherent decoders is compared for varying channel variation rate.

Hybrid multiplicative multi-watermarking in DWT domain

Multiwatermarking embeds multiple watermarks into the media content imperceptibly. It is regarded as the potential means to protect the media contents' copyright and/or trace illegal redistributors in multi-user environments, e.g., collaborative media content production or layered media content distribution. However, there are still some open issues in designing a good multiwatermarking scheme, including the combination between multiple embedding steps, the combination between multiple watermarks and the security of embedding schemes. In this paper, novel multiwatermarking schemes are proposed, which are based on hybrid multi-bit multiplicative rules controlled by secret keys. Two hybrid multiplicative multiwatermarking decoders, i.e., optimum and locally optimum, are proposed, which are based on the minimum Bayesian risk criterion and the DWT coefficients are modeled as the generalized Gaussian distribution. The BER (average bit error rate) as the evaluation index of the performance of optimum hybrid decoders is exactly analyzed. Finally, experimental results are shown to confirm the validity of the theoretical and empirical analysis.

A novel scalable video broadcasting method over MIMO-OFDM systems

A hybrid decoding scheme for the scalable video codec (SVC) coded video wireless broadcasting system is proposed. The SVC coded video layers are grouped into various groups and each group is encoded independently into a signal layer, which are then transmitted from allocated antennas over certain time-frequency blocks. Each receiver employs the successive group decoder to decode the signal streams of interest by treating other signal streams as interference. The transmitter performs adaptive coding and modulation, and transmission antenna and subcarrier allocation, based on the rate feedback from the receivers. The proposed hybrid receiver switches to the minimum mean square error (MMSE) decoder if the rate can be achieved by the MMSE decoding. Extensive simulations are provided to demonstrate the performance gain of the proposed SVC broadcasting scheme over the conventional MMSE decoding and the successive interference cancelation algorithm.

A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes

Objective. Brain–machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. Approach. Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. Main results. LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. Significance. These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.