Joint Channel Coding Research Articles

Joint Optimization of Protograph LDPC Code Pair for Joint Source and Channel Coding

Double protograph low-density parity-check code has been shown to be a good solution for joint source-channel coding. Moreover, the source code and channel code (as one code pair) can be jointly optimized to achieve a good bit error rate (BER) performance. In this paper, first, an optimal design criterion of this code pair based on an extended curve-fitting algorithm is proposed. Second, a joint optimization procedure which searches for both the optimal decoding threshold and the optimal code pair is presented. In addition, for short-to-moderate-length codewords, the finite-length effect cannot be ignored and the procedure has to be modified with a new notion named length-N decoding threshold . The protograph extrinsic information transfer analysis and the BER simulation have shown that our optimized code pairs acquire lower decoding thresholds than conventional code pairs. Moreover, the optimized code pairs with short-to-moderate-length codewords can mitigate the finite-length effect and obtain low length-N decoding thresholds .

Joint source and channel coding with systematic polar codes for wireless sensor communication in next generation networks

Polar codes have strongly entered into action within the standardization of the next generation 5G mobile communication systems, which are expected to be an enabling technology for the Internet of Things where networks with a large number of sensors have to handle massive connectivity demands. This paper proposes and investigates the use of systematic polar codes for joint source-channel coding of correlated sources in wireless sensor networks, thus allowing the compression of the volume of data to be transmitted over the network on one hand, and on the other hand, the protection of this data from channel impairments. Results show that systematic polar codes can achieve a distributed compression with rates close to theoretical limits, with better error rates obtained for larger blocks, and a better robustness against transmission errors obtained with stronger compression and shorter block lengths. Furthermore, while the system is able to overcome the effect of noise on parity information with adequate power management, noisy side information significantly degrades system performance with remarkable gaps towards the case of distributed compression with an ideal transmission channel.

Joint Compression, Channel Coding, and Retransmission for Data Fidelity With Energy Harvesting

We consider a monitoring application where sensors periodically report data to a common receiver using time division multiplexing. The sensors are constrained by the limited and unpredictable energy availability provided by energy harvesting (EH), and by the channel impairments. To maximize the quality of the reported data, the packets transmitted contain newly generated data blocks together with up to $r - 1$ previously unsuccessfully delivered ones, where $r$ is a design parameter. These data blocks are compressed, concatenated, and encoded with a channel code. The scheme applies lossy compression, such that the fidelity of the individual blocks is traded off with the reliability provided by the channel code. We show that the proposed strategy outperforms the one in which retransmissions are not allowed. We also investigate the tradeoff between the value of $r$ , the compression and the coding rates, under the constraints of the energy availability, and, once $r$ has been decided, use a Markov decision process (MDP) to optimize the compression/coding rates. Finally, we implement a reinforcement learning algorithm, through which devices can learn the optimal transmission policy without knowing a priori the statistics of the EH process, and show that it indeed reaches the performance obtained via MDP.

JET: Joint source and channel coding for error resilient virtual reality video wireless transmission

Virtual reality (VR) provides users extraordinary viewing experience and draws more and more attentions from both industry and academia. In this paper, we propose JET: Joint source and channel coding for Error resilient virtual reality video wireless Transmission, where we jointly investigate how to conquer the problem of source video’s huge size, how to efficiently satisfy a user’s view switch request and how to handle packet loss. Specifically, we first divide a VR video into smaller video tiles. Upon a user’s view switch request, the tiles corresponding to the part that the user is requesting, referred to as the field of view (FoV), and the part that the user may switch to before new video can be received, are delivered over a wireless network. We consider unequal error protection (UEP) for FoV and the rest part and formulate the inherent error resilient VR video transmission problem into a joint source and channel coding problem. In particular, we optimize the tile partition, quantization parameter and Forward Error Correction (FEC) packet allocation to maximize a user’s received video quality. We also propose a low-complexity heuristic algorithm to solve this optimization problem. Extensive simulations are conducted and simulation results verify the superior performance.

Reliability-Oriented Decoding Strategy for LDPC Codes-Based D-JSCC System

In this letter, a reliability oriented decoding strategy (RODS) for low density parity check codes-based distributed joint source and channel coding system is proposed. During each local decoding iteration, the reliability of each variable node is evaluated based on the sign of received log-likelihood ratios (LLRs) and its oscillation situation. Then, by employing reliability-oriented updating operation, the negative influence of those inaccurate messages is suppressed. Moreover, extra operations are performed on a group of most unreliable source nodes so that more accurate posteriori LLRs can be obtained. During global iteration, offset operation is performed to reduce error propagation. The simulation results illustrate that the proposed RODS outperforms other decoding algorithms at the aspects of error correction performance and convergence speed, especially when the correlation between distributed sources is low.

QoE-Aware Scalable Video Transmission in MIMO Systems

An important concept in wireless systems has been QoE-aware video transmission. Such communications are considered not only connection- based communications but also content- aware communications, since the video quality is closely related to the content itself. It becomes necessary, therefore, for video communications to utilize a cross-layer design (also known as joint source and channel coding). To provide efficient methods of allocating network resources, the wireless network uses its cross-layer knowledge to perform UEP solutions. In this article, we summarize the latest video transmission technologies that are based on SVC over MIMO systems with cross-layer designs. To provide insight into video transmission in wireless networks, we investigate UEP solutions in the delivering of video over massive MIMO systems. Our results show that in terms of QoE, SVC layer prioritization, which was considered important in prior work, is not always beneficial in massive MIMO systems; consideration must be given to the content characteristics.

Flexible iterative receiver architecture for wireless sensor networks: a joint source and channel coding design example

Due to their computational complexity, iterative decoder components such as source and channel decoders are usually implemented using specialised dedicated hardware. This leads to the scenario where each different iterative decoder component of the receiver requires its own hardware. Due to their relatively high complexity, many capacity-approaching techniques proposed in the literature have not yet been invoked in wireless sensor network applications, despite their potential benefits of facilitating a reduced transmission power or extended communication range. Against this background, the authors propose an energy-efficient architecture comprised of multiple computation unit (CU), which is sufficiently flexible for accommodating different iterative decoder components using the same hardware. In this study, the flexible architecture is applied to Joint Source and Channel Coding (JSCC), comprising the Unary Error Correction (UEC) code, a turbo code and an iterative demodulator. The authors conceive a flexible technique for controlling the hardware, which supports a high hardware-exploitation ratio for the CU, reaching a utilisation of 88%, compared with 68% achieved in similar solutions reported in the open literature.

A Quality-of-Content-Based Joint Source and Channel Coding for Human Detections in a Mobile Surveillance Cloud

More than 70% of consumer mobile Internet traffic will be mobile video transmissions by 2019. The development of wireless video transmission technologies has been boosted by the rapidly increasing demand of video streaming applications. Although more and more videos are delivered for video analysis (e.g., object detection/tracking and action recognition), most existing wireless video transmission schemes are developed to optimize human perception quality and are suboptimal for video analysis. In mobile surveillance networks, a cloud server collects videos from multiple moving cameras and detects suspicious persons in all camera views. Camera mobility in smartphones or dash cameras implies that video is to be uploaded through bandwidth-limited and error-prone wireless networks, which may cause quality degradation of the decoded videos and jeopardize the performance of video analyses. In this paper, we propose an effective rate-allocation scheme for multiple moving cameras in order to improve human detection (content) performance. Therefore, the optimization criterion of the proposed rate-allocation scheme is driven by quality of content (QoC). Both video source coding and application layer forward error correction coding rates are jointly optimized. Moreover, the proposed rate-allocation problem is formulated as a convex optimization problem and can be efficiently solved by standard solvers. Many simulations using High Efficiency Video Coding standard compression of video sequences and the deformable part model object detector are carried, and results demonstrate the effectiveness and favorable performance of our proposed QoC-driven scheme under different pedestrian densities and wireless conditions.

A Simplified Joint Source and Channel Coding System for Data Transmission over Fading Channel

The major challenge with multimedia communication lies in the demand for higher data rates with better quality of service and lower latency over the wireless channel. Multimedia content is usually source encoded with variable length codes (VLCs) for optimizing bandwidth utilization. VLCs, even though uniquely decodable, suffer from the problem of error propagation. A single bit error can cause an entire block of data to be decoded wrongly, because the ending of a codeword and the beginning of the next can no longer be determined correctly. Several joint source-channel coding (JSCC) techniques are studied in literature to have efficient source coding with resilience to channel errors, but such methods pose a prohibitively high computational complexity. A variant of JSCC for efficient transmission of information is presented in this paper. The information text is encoded by reversible VLC (RVLC) and corresponding to it, a header sequence is generated. Since the headers are critical in decoding a block, they are channel coded with convolution code to protect from errors. Orthogonal frequency division multiplexing is used to provide high data rate communication and robustness to multipath fading. The performance of the system is analysed over Rayleigh fading channel in terms letter error rate (LER) and computational complexity. The proposed method is seen to provide a good LER performance at moderate to high signal to noise ratio, along with minimal computational complexity, when compared with the contemporary JSCC based schemes. Theoretical performance bounds for the error rate of the proposed system are also derived.

Adaptive error protection coding for wireless transmission of motion JPEG 2000 video

The delivery of video over wireless, error-prone transmission channels requires careful allocation of channel and source code rates, given the available bandwidth. In this paper, we present a theoretical framework to find an optimal joint channel and source code rate allocation, by considering an intra-coded video compression standard such as Motion JPEG 2000 and an error-prone wireless transmission channel. Lagrangian optimization is used to find the optimal code rate allocation, from a PSNR perspective, starting from commonly available source coding outputs, such as intermediate rate-distortion traces. The algorithm is simple and adaptive both on the available bandwidth and on the transmission channel conditions, and it has a low computational complexity. Simulation results, using Reed-Solomon (R-S) coding, show that the achieved performance, in terms of PSNR and MSSIM, is comparable with that of other methods reported in literature. In addition, a simplified and sub-optimal expression for determining the channel code assignment is also provided.

Exponential Golomb and Rice Error Correction Codes for Generalized Near-Capacity Joint Source and Channel Coding

The recently proposed unary error correction (UEC) and Elias gamma error correction (EGEC) codes facilitate the near-capacity joint source and channel coding (JSCC) of symbol values selected from large alphabets at a low complexity. Despite their large alphabet, these codes were only designed for a limited range of symbol value probability distributions. In this paper, we generalize the family of UEC and EGEC codes to the class of rice and exponential Golomb error correction codes, which have a much wider applicability, including the symbols produced by the H.265 video codec, the letters of the English alphabet, and in fact any arbitrary monotonic unbounded source distributions. Furthermore, the practicality of the proposed codes is enhanced to allow a continuous stream of symbol values to be encoded and decoded using only fixed-length system components. We explore the parameter space to offer beneficial tradeoffs between error correction capability, decoding complexity, as well as transmission-energy, -duration, and -bandwidth over a wide range of operating conditions. In each case, we show that our codes offer significant performance improvements over the best of several state-of-the-art benchmarkers. In particular, our codes achieve the same error correction capability, as well as transmission energy, transmission duration, and transmission bandwidth as a variable length error-correction code benchmarker, while reducing the decoding complexity by an order of magnitude. In comparison with the best of the other JSCC and separate source and channel coding benchmarkers, our codes consistently offer $E_{\mathrm {b}}/N_{0}$ gains of between 0.5 and 1.0 dB which only appear to be the modest, because the system operates close to capacity. These improvements are achieved for free, since they are not achieved at the cost of increasing transmission energy, transmission duration, transmission bandwidth, or decoding complexity.

Learning-Aided Unary Error Correction Codes for Non-Stationary and Unknown Sources

Unary error correction (UEC) codes have recently been proposed for the joint source and channel coding of symbol values that are selected from a set having an infinite cardinality. However, the original UEC scheme requires the knowledge of the source probability distribution, in order to achieve near-capacity operation. This limits the applicability of the UEC scheme, since the source probability distribution is typically non-stationary and is unknown in practice. In this paper, we propose a dynamic version of the UEC scheme, which can learn the unknown source statistics and gradually improve its decoding performance during a transient phase, then dynamically adapt to the non-stationary statistics and maintain reliable near-capacity operation during a steady-state phase, at the cost of only a moderate memory requirement at the decoder. Based on the same learning technique, we also propose two separate source and channel coding benchmarkers, namely, a learning-aided Elias gamma-convolutional code (CC) scheme and a learning-aided arithmetic-CC scheme. The simulation results reveal that our proposed learning-aided UEC scheme outperforms the benchmarkers by up to 0.85 dB, without requiring any additional decoding complexity or any additional transmission-energy, -bandwidth, or -duration.

View-Popularity-Driven Joint Source and Channel Coding of View and Rate Scalable Multi-View Video

We study the scenario of multicasting multi-view video content, recorded in the video plus depth format, to a collection of heterogeneous clients featuring Internet access links of diverse packet loss and transmission bandwidth values. We design a popularity-aware joint source-channel coding optimization framework that allocates source and channel coding rates to the captured content, such that the aggregate video quality of the reconstructed content across the client population is maximized, for the given packet loss and bandwidth characteristics of the clients and their view selection preferences. The source coding component of our framework features a procedure for generating a view and rate embedded bitstream that is optimally decodable at multiple data rates and accounts for the different popularity of diverse video perspectives of the scene of interest, among the clients. The channel coding component of our framework comprises an expanding-window rateless coding procedure that optimally allocates parity protection bits to the source encoded layers, in order to address packet loss across the unreliable client access links. We develop an optimization method that jointly computes the source and channel coding decisions of our framework, and also design a fast local-search-based solution that exhibits a negligible performance loss relative to the full optimization. We carry out comprehensive simulation experiments and demonstrate significant performance gains over competitive state-of-the-art methods (based on H.264/AVC and network coding, and H.264/SVC and our own channel coding procedure), across different scenario settings and parameter values.

Codeword averaged density evolution for distributed joint source and channel coding with decoder side information

The authors consider applying the systematic low-density parity-check codes with the parity based approach to the lossless (or near lossless) distributed joint source channel coding (DJSCC) with the decoder side information for the non-uniform sources over the asymmetric memoryless transmission channel. By using an equivalent channel coding model, which consists of two parallel subchannels: a correlation and a transmission sub-channel, respectively, they derive the codeword averaged density evolution (DE) for the DJSCC with the decoder side information for the asymmetrically correlated non-uniform sources over the asymmetric memoryless transmission channel. A new code ensemble definition of the irregular codes is introduced to distinguish between the source and the parity variable nodes, respectively. Extensive simulations demonstrate the effectiveness of the codeword averaged DE.

A Joint Improved Irregular Accumulate Concatenated Tree Coding and Frequency Hopping OFDM Modulation Scheme for Wireless Networks

Joint channel coding and spread spectrum modulation technologies bring to wireless networks the tremendous amount of performance improvement, the elimination, and the mitigation of interference. This paper proposes an improved design for irregular accumulate concatenated tree (IACT) codes, which can be viewed as simply precoded by an accumulator. This class of codes belongs to the special turbo-like codes, which combines the advantages of fast encoder structures of turbo codes, and the advantages of parallel high-speed belief-propagation (BP) iterative decoding algorithm of low-density parity-check (LDPC) codes. The improved IACT codes can be coded via a Tanner graph. With these improved codes, we propose a joint IACT coding and frequency hopping orthogonal frequency division multiplexing (FH-OFDM) modulation scheme for wireless networks. We compare the performance of different coding bases such as the irregular concatenated tree (ICT) codes and turbo codes in the scheme by theoretical analysis and simulation. Numerical results are shown that the proposed scheme with these IACT codes has really good performance.

Near-Capacity Joint Source and Channel Coding of Symbol Values from an Infinite Source Set Using Elias Gamma Error Correction Codes

In this paper we propose a novel low-complexity Joint Source and Channel Code (JSCC), which we refer to as the Elias Gamma Error Correction (EGEC) code. Like the recently-proposed Unary Error Correction (UEC) code, this facilitates the practical near-capacity transmission of symbol values that are randomly selected from a set having an infinite cardinality, such as the set of all positive integers. However, in contrast to the UEC code, our EGEC code is a universal code, facilitating the transmission of symbol values that are randomly selected using any monotonic probability distribution. When the source symbols obey a particular zeta probability distribution, our EGEC scheme is shown to offer a 3.4 dB gain over a UEC benchmarker, when Quaternary Phase Shift Keying (QPSK) modulation is employed for transmission over an uncorrelated narrowband Rayleigh fading channel. In the case of another zeta probability distribution, our EGEC scheme offers a 1.9 dB gain over a Separate Source and Channel Coding (SSCC) benchmarker.

Distributed Joint Source and Channel Coding with Low-Density Parity-Check Codes

Low-density parity-check (LDPC) codes with the parity-based approach for distributed joint source channel coding (DJSCC) with decoder side information is described in this paper. The parity-based approach is theoretical limit achievable. Different edge degree distributions are used for source variable nodes and parity variable nodes. Particularly, the codeword-averaged density evolution (CADE) is presented for asymmetrically correlated nonuniform sources over the asymmetric memoryless transmission channel. Extensive simulations show that the splitting of variable nodes can improve the coding efficiency of suboptimal codes and lower the error floor.

Joint Channel Coding based on LDPC Codes with Gaussian Kernel Reflecton and CS Redundancy

This paper proposes a new joint decoding algorithm frame based on compressed sensing CS and LDPC (Low-Density Parity-Check) codes. Redundant information can be effectively extracted and amplified by CS reconstruction as a compensation to correct decoding of LDPC codes. We adopt Gaussian kernel function of image segmentation as a reflection. Simulation results indicate, compared with LDPC algorithm, the algorithm presented in this paper can obviously make BER (bit error ratio) lower and improve system decoding performance, and different variance of Gaussian kernel function can obtain different results.

A Unary Error Correction Code for the Near-Capacity Joint Source and Channel Coding of Symbol Values from an Infinite Set

A novel Joint Source and Channel Code (JSCC) is proposed, which we refer to as the Unary Error Correction (UEC) code. Unlike existing JSCCs, our UEC facilitates the practical encoding of symbol values that are selected from a set having an infinite cardinality. Conventionally, these symbols are conveyed using Separate Source and Channel Codes (SSCCs), but we demonstrate that the residual redundancy that is retained following source coding results in capacity loss. This loss is found to have a value of 1.11 dB in a particular practical scenario, where Quaternary Phase Shift Keying (QPSK) modulation is employed for transmission over an uncorrelated narrowband Rayleigh fading channel. By contrast, the proposed UEC code can eliminate this capacity loss, or reduce it to an infinitesimally small value. Furthermore, the UEC code has only a moderate complexity, facilitating its employment in practical low-complexity applications.

Decoding Generalized Joint Channel Coding and Physical Network Coding in the LLR Domain

This letter develops a log-likelihood-ratio-based decoder for a generalized joint channel-coding-and-physical-network coding used in a two-way relay system. Assuming that the same low-density parity-check codes are used at the source nodes, the proposed decoder shows identical error performance as the probability-domain-based decoder but with a much lower computational complexity.