Joint Source-channel Decoding Scheme Research Articles

Controversial Variable Node Selection-Based Adaptive Belief Propagation Decoding Algorithm Using Bit Flipping Check for JSCC Systems.

An end-to-end joint source–channel (JSC) encoding matrix and a JSC decoding scheme using the proposed bit flipping check (BFC) algorithm and controversial variable node selection-based adaptive belief propagation (CVNS-ABP) decoding algorithm are presented to improve the efficiency and reliability of the joint source–channel coding (JSCC) scheme based on double Reed–Solomon (RS) codes. The constructed coding matrix can realize source compression and channel coding of multiple sets of information data simultaneously, which significantly improves the coding efficiency. The proposed BFC algorithm uses channel soft information to select and flip the unreliable bits and then uses the redundancy of the source block to realize the error verification and error correction. The proposed CVNS-ABP algorithm reduces the influence of error bits on decoding by selecting error variable nodes (VNs) from controversial VNs and adding them to the sparsity of the parity-check matrix. In addition, the proposed JSC decoding scheme based on the BFC algorithm and CVNS-ABP algorithm can realize the connection of source and channel to improve the performance of JSC decoding. Simulation results show that the proposed BFC-based hard-decision decoding (BFC-HDD) algorithm ( = 1) and BFC-based low-complexity chase (BFC-LCC) algorithm ( = 1, = 3) can achieve about 0.23 dB and 0.46 dB of signal-to-noise ratio (SNR) defined gain over the prior-art decoding algorithm at a frame error rate (FER) = . Compared with the ABP algorithm, the proposed CVNS-ABP algorithm and BFC-CVNS-ABP algorithm achieve performance gains of 0.18 dB and 0.23 dB, respectively, at FER = .

Joint Source-Channel Decoding of Polar Codes for HEVC-Based Video Streaming

Ultra High-Definition (UHD) and Virtual Reality (VR) video streaming over 5G networks are emerging, in which High-Efficiency Video Coding (HEVC) is used as source coding to compress videos more efficiently and polar code is used as channel coding to transmit bitstream reliably over an error-prone channel. In this article, a novel Joint Source-Channel Decoding (JSCD) of polar codes for HEVC-based video streaming is presented to improve the streaming reliability and visual quality. Firstly, a Kernel Density Estimation (KDE) fitting approach is proposed to estimate the positions of error channel decoded bits. Secondly, a modified polar decoder called R-SCFlip is designed to improve the channel decoding accuracy. Finally, to combine the KDE estimator and the R-SCFlip decoder together, the JSCD scheme is implemented in an iterative process. Extensive experimental results reveal that, compared to the conventional methods without JSCD, the error data-frame correction ratios are increased. Averagely, 1.07% and 1.11% Frame Error Ratio (FER) improvements have been achieved for Additive White Gaussian Noise (AWGN) and Rayleigh fading channels, respectively. Meanwhile, the qualities of the recovered videos are significantly improved. For the 2D videos, the average Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM) gains reach 14% and 34%, respectively. For the 360֯ videos, the average improvements in terms of Weighted-to-Spherically-uniform PSNR (WS-PSNR) and Voronoi-based Video Multimethod Assessment Fusion (VI-VMAF) reach 21% and 7%, respectively.

Unequal Error Protection Based on DVFS for JSCD in Low-Power Portable Multimedia Systems

This article presents a low-power decoder design for joint source-channel decoding (JSCD) based on a novel unequal error protection (UEP) scheme over additive white gaussian noise (AWGN) channels. Conventional JSCD schemes, adopting low-density parity check (LDPC) codes for multimedia devices, typically operate at a fixed-time decoding loop, regardless of the quality of data received. We present a JSCD scheme that achieves reduction in power through minimum energy decoding and dynamic voltage and frequency scaling (DVFS). Consequently, up to 39% power reduction is achieved in Foreman, Akiyo, and Mobile video streams without performance degradation in reconstructed video quality.

Hybrid Joint Source Channel Decoding for Progressive JPEG Image Transmission

In this paper, we combine two Joint Source Channel Decoding (JSCD) approaches and apply them to progressive JPEG image transmission. The first JSCD approach consists of optimizing the allocation of the channel code-rates to the different layers of the JPEG image, and is commonly referred as Unequal Error Protection (UEP). The second JSCD strategy exploits a priori probabilities obtained from source statistics as well as the residual redundancy of the source to improve channel decoding. Additionally, we propose an error resilient sub-categorization scheme for the DC layer so as to reduce the effect of the channel-induced distortion. The hybrid JSCD scheme is implemented with Rate Compatible Punctured Turbo Codes and also with the bandwidth efficient Rate Compatible Punctured Turbo Trellis Coded Modulation. Gains of over 10 dB in PSNR are obtained with the hybrid JSCD scheme as compared to a conventional JPEG image transmission scheme.

Joint source channel decoding and iterative symbol combining with turbo trellis-coded modulation

A new joint source channel decoding (JSCD) scheme for turbo trellis-coded modulation is proposed which exploits symbol-a-priori probabilities to improve decoding performance. To obtain symbol-a-priori probabilities, we propose a new method which uses a time-stretched bit-level trellis of a variable length code (VLC) decoder alongside a symbol level trellis. Iterative symbol combining (ISC) is also integrated with the proposed JSCD scheme so that multiple transmissions of the same data packet can be combined to improve decoding. Essentially JSCD with ISC (JSCD-ISC) exploits two types of a-priori information. The first one being the symbol-a-priori probabilities derived from source statistics, and the second one is obtained from the saved extrinsic information of previously transmitted copies of the data. Several simulations with different VLC sources like Huffman and reversible variable length codes (RVLC), and for different number of retransmissions have been performed. The most striking result is obtained with RVLC, whereby using only two retransmissions, the JSCD-ISC scheme outperforms a conventional scheme using three retransmissions by 0.2 dB.

Performance of joint source–channel decoding with iterative bit combining and detection

A joint source–channel decoding scheme (JSCD) with iterative bit combining (IBC) is proposed, which exploits two types of a priori information. The first one is the a priori bit probabilities obtained from source statistics, and the second is the channel a priori probabilities obtained from saved extrinsic information of previous transmissions. The JSCD-IBC scheme also incorporates iterative detection as both a stopping criteria and mechanism for triggering retransmissions. This adds an implicit adaptivity to the system and prevents excess iterations/retransmissions from being effected. The performance of the JSCD-IBC scheme is evaluated with four different iterative detection schemes and also two different types of variable length codes, Huffman and reversible variable length codes. Simulation results show that a significant performance gain in terms of bit error rate, throughput, and number of iterations can be achieved with the JSCD-IBC scheme as compared to a separate decoding scheme.

Iterative joint source-channel decoding of speech spectrum parameters over an additive white Gaussian noise channel

In this paper, we show how the Gaussian mixture modeling framework used to develop efficient source encoding schemes can be further exploited to model source statistics during channel decoding in an iterative framework to develop an effective joint source-channel decoding scheme. The joint probability density function (PDF) of successive source frames is modeled as a Gaussian mixture model (GMM). Based on previous work, the marginal source statistics provided by the GMM is used at the encoder to design a low-complexity memoryless source encoding scheme. The source encoding scheme has the specific advantage of providing good estimates to the probability of occurrence of a given source code-point based on the GMM. The proposed iterative decoding procedure works with any channel code whose decoder can implement the soft-output Viterbi algorithm that uses a priori information (APRI-SOVA) or the BCJR algorithm to provide extrinsic information on each source encoded bit. The source decoder uses the GMM model and the channel decoder output to provide a priori information back to the channel decoder. Decoding is done in an iterative manner by trading extrinsic information between the source and channel decoders. Experimental results showing improved decoding performance are provided in the application of speech spectrum parameter compression and communication.

Repeated joint source-channel decoding with reduced new bit errors

In this study, we describe a repeated joint source-channel decoding scheme in which not only a priori information about the bit to be decoded but also its decoding reliability information are used to adaptively control the decoding process. The channel decoding reliability information obtained for instance from a previous decoding attempt is used to properly weight the a priori information so that the new bit errors caused by wrong “natural” a priori information are reduced. In order to efficiently estimate the a priori residual redundancy, an iterative approach based on the Kalman filter theory is employed. As a result, decoding gains can be obtained with almost no new bit errors introduced. The proposed scheme is successfully applied to the GSM full rate system to achieve a more reliable transmission of speech signals in both noisy and less noisy environments.