Iterative joint design of source codes and multiresolution channel codes

We study the performance of joint source and channel codes designed to minimize end-to-end distortion over a Rayleigh fading channel. We consider two joint code designs. The first joint code uses a sequential design: a standard vector quantizer (VQ) source code is designed for a perfect channel (noiseless and distortionless) and then an RCPC channel code is optimized relative to the VQ and the channel statistics. The second design jointly optimizes a channel optimized VQ (COVQ) and an RCPC channel code through an iterative design process. We consider both hard-decision and soft-decision decoding for the channel codes. In both designs the bit allocation between the source and channel codes is optimized. At this optimal bit allocation, the performance of the iterative joint design and the simpler sequential design are nearly the same over the range of SNR values that we considered. Both code designs outperform standard COVQ and by up to 6 dB, and this performance improvement is most pronounced at low SNRs.

Concurrent genetic optimization for joint design of source and channel codes

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.

Joint source and channel coding (JSCC) for image transmission has attracted increasing attention due to its robustness and high efficiency. However, the existing deep JSCC research mainly focuses on minimizing the distortion between the transmitted and received information under a fixed number of available channels. Therefore, the transmitted rate may be far more than its required minimum value. In this paper, an adaptive information bottleneck (IB) guided joint source and channel coding (AIB-JSCC) method is proposed for image transmission. The goal of AIB-JSCC is to reduce the transmission rate while improving the image reconstruction quality. In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate. A mathematically tractable lower bound on the proposed objective is derived, and then, adopted as the loss function of AIB-JSCC. To trade off compression and reconstruction quality, an adaptive algorithm is proposed to adjust the hyperparameter of the proposed loss function dynamically according to the distortion during the training. Experimental results show that AIB-JSCC can significantly reduce the required amount of transmitted data and improve the reconstruction quality and downstream task accuracy.

An improved MIMO system implementing joint source and channel coding in the form of Rate Compatible Punctured Convolutional Code (RCPC Code) and Unequal Error Protection (UEP) is proposed. To ensure a multiplexing gain, a layered architecture, V-BLAST, is employed. Analytical BER is presented and numerical simulations are done to analyze the system performance. The proposed coding scheme allows for consistently higher error-protection level as the code rate decreases. It is also shown that whilst the lower code rates outperforms the higher ones, in terms of larger free distance and lower BER, the SNR range covered is narrower at decreasing levels of BER. This implies the potential bandwidth efficiency to be gained if the higher code rates are used for lower-priority data bits.

In this paper, we present an unequal error protection method for packet-loss resilient transmission of progressively compressed 3D meshes. The proposed method is based on a source and channel coding approach where we set up a theoretical framework for the overall system by which the channel packet loss behavior and the channel bandwidth can be directly related to the decoded mesh quality at the receiver. In particular, we develop a statistical distortion measure and optimize it to compute the best combination of (i) the number of triangles to transmit, (ii) the total number of channel coding bits, and (iii) the distribution of these error-protection bits among the transmitted layers in order to maximize the expected decoded mesh quality at the receiver. The proposed method differs from the earlier approaches in two major aspects: (i) determination of the number of channel coding bits (C) and (ii) the approach of reducing the source rate in order to accommodate for channel coding bits. When the proposed method is used to transmit a typical 3D mesh over a channel with a 10% packet loss rate, the distortion (measured using the Hausdorff distance between the original and the decoded overly sampled meshes) is reduced by 50% compared to the case when no error protection is applied.

This paper considers the joint optimization of the source and channel coders over a Rayleigh fading channel. The system being investigated consists of a vector quantizer (VQ) whose output indices are mapped directly into points in the modulation signal space. The modulation signal is then transmitted over a Rayleigh fading channel. A decoder based on a nonlinear estimator is used to reconstruct the source signal from the received signal samples and from the channel state information (CSI). The paper presents an iterative algorithm that jointly optimizes the vector quantizer and the modulation signal set using as optimality criterion the minimum mean-square error (MSE) between the original and the reconstructed signals. It is shown that a jointly optimized system based on average channel characteristics significantly outperforms a reference system based on a VQ designed for the given source and a standard QAM modulation signal set. >

Joint source and channel decoding of compressed still images

Joint source and channel coding (JSCC) has attracted increasing attention in semantic communications. However, JSCC is vulnerable to privacy issues due to the high relevance between the source image and channel input. In this paper, we propose a disentangled information bottleneck guided privacy-protective JSCC (DPJSCC) for image transmission, which aims at protecting private information and achieving superior image transmission performance. In particular, we propose a disentangled information bottleneck objective to compress the private information in public subcodewords and improve the reconstruction quality simultaneously. To optimize JSCC neural networks using the proposed objective, we derive a differentiable estimation based on variational approximation and the density-ratio trick. Additionally, we design a password-based privacy-protective algorithm that encrypts the private subcodewords, achieving joint optimization with JSCC neural networks. The proposed algorithm involves an encryptor for encrypting private information and a decryptor for recovering it at the legitimate receiver. A loss function is derived based on the maximum entropy principle for jointly training the encryptor, decryptor, and JSCC decoder to maximize eavesdropping uncertainty and improve reconstruction quality. Experimental results show that DPJSCC reduces eavesdropping accuracy on private information by up to 18% and decreases inference time by 10%.

In recent years, due to the rapid convergence of multimedia services, Internet and wireless communications, there has been a growing trend of heterogeneity (in terms of channel bandwidths, mobility levels of terminals, end-user quality-of-service (QoS) requirements) for emerging integrated wired/wireless networks. Moreover, in nowadays systems, a multitude of users coexists within the same network, each of them with his own QoS requirement and bandwidth availability. In this framework, embedded source coding allowing partial decoding at various resolution is an appealing technique for multimedia transmissions. This dissertation includes my PhD research, mainly devoted to the study of embedded multimedia bitstreams in heterogenous networks, developed at the University of Bologna, advised by Prof. O. Andrisano and Prof. A. Conti, and at the University of California, San Diego (UCSD), where I spent eighteen months as a visiting scholar, advised by Prof. L. B. Milstein and Prof. P. C. Cosman. In order to improve the multimedia transmission quality over wireless channels, joint source and channel coding optimization is investigated in a 2D time-frequency resource block for an OFDM system. We show that knowing the order of diversity in time and/or frequency domain can assist image (video) coding in selecting optimal channel code rates (source and channel code rates). Then, adaptive modulation techniques, aimed at maximizing the spectral efficiency, are investigated as another possible solution for improving multimedia transmissions. For both slow and fast adaptive modulations, the effects of imperfect channel estimation errors are evaluated, showing that the fast technique, optimal in ideal systems, might be outperformed by the slow adaptive modulation, when a real test case is considered. Finally, the effects of co-channel interference and approximated bit error probability (BEP) are evaluated in adaptive modulation techniques, providing new decision regions concepts, and showing how the widely used BEP approximations lead to a substantial loss in the overall performance.

Multimedia encoders typically generate symbols having a wide range of legitimate values. In practical mobile wireless scenarios, the transmission of these symbols is required to be bandwidth efficient and error resilient, motivating both source coding and channel coding. However, separate source and channel coding (SSCC) schemes are typically unable to exploit the residual redundancy in the source symbols, which cannot be totally reduced by finite-delay finite-complexity schemes, hence resulting in a capacity loss. Until recently, none of the existing joint source and channel codes (JSCCs) were suitable for this application since their decoding complexity rapidly increases with the size of the symbol alphabet. Motivated by this, we proposed a novel JSCC referred to as the unary error correction (UEC) code, which is capable of exploiting all residual redundancy and eliminating any capacity loss, while imposing only a moderate decoding complexity. In this paper, we show that the operation of the UEC decoder can be dynamically adapted to strike an attractive tradeoff between its decoding complexity and its error correction capability. Furthermore, we conceive the corresponding 3-D EXtrinsic Information Transfer (EXIT) charts for controlling this dynamic adaptation, as well as the decoder activation order, when the UEC code is serially concatenated with a turbo code. In this way, we expedite iterative decoding convergence, facilitating a gain of up to 1.2 dB compared with both SSCC and to its nonadaptive UEC benchmarkers, while maintaining the same transmission bandwidth, duration, energy, and decoding complexity.

The invention of Fountain codes is a major advance in the field of error correcting codes. The goal of this work is to study and develop algorithms for source and channel coding using a family of Fountain codes known as Raptor codes. From an asymptotic point of view, the best currently known sum-product decoding algorithm for non binary alphabets has a high complexity that limits its use in practice. For binary channels, sum-product decoding algorithms have been extensively studied and are known to perform well. In the first part of this work, we develop a decoding algorithm for binary codes on non-binary channels based on a combination of sum-product and maximum-likelihood decoding. We apply this algorithm to Raptor codes on both symmetric and non-symmetric channels. Our algorithm shows the best performance in terms of complexity and error rate per symbol for blocks of finite length for symmetric channels. Then, we examine the performance of Raptor codes under sum-product decoding when the transmission is taking place on piecewise stationary memoryless channels and on channels with memory corrupted by noise. We develop algorithms for joint estimation and detection while simultaneously employing expectation maximization to estimate the noise, and sum-product algorithm to correct errors. We also develop a hard decision algorithm for Raptor codes on piecewise stationary memoryless channels. Finally, we generalize our joint LT estimation-decoding algorithms for Markov-modulated channels. In the third part of this work, we develop compression algorithms using Raptor codes. More specifically we introduce a lossless text compression algorithm, obtaining in this way competitive results compared to the existing classical approaches. Moreover, we propose distributed source coding algorithms based on the paradigm proposed by Slepian and Wolf.

Joint source and channel trellis coding of line spectrum pair parameters

Scalable video coding technique has been desired for many years to fulfill the requirement of reliable transmission of video over heterogeneous networks. It allows for simple solutions in adaptation to network constraint and user capabilities by providing a full scalability including temporal, spatial and quality scalability. Various coding structures will dramatically affect the overall performance of the system. In this paper, we develop a new joint source and channel coding scheme for wireless broadcasting making use of H.264/SVC considering the various clients' statistics such as the accessing bandwidth and the error rate. First, an effective algorithm is carried out to distribute proper amount of source codes and channel codes to the base layer to guarantee that all the clients can receive the video with a minimum quality. Second, to deal with the burst packet loss, we propose to apply the unequal error protection (UEP) scheme on the enhancement layers using the interleaved packetization to make sure that all the lower layers can be decoded if an enhancement layer is correctly received. To maximize the system-defined utility function, fast algorithms are designed to dynamically allocate the source coding rate and the channel coding rate for the scalable bit-stream given the clients' distribution. Experiments are conducted to verify the performance of the proposed algorithm against the other two schemes, where equal error protection (EEP) and UEP schemes with different coding structures are employed. The results demonstrate the superiority of the proposed scheme and the improvement is up to 2 dB.

Probabilistic amplitude shaping (PAS) has proved to be a promising way to achieve the shaping gain for an additive white Gaussian noise channel with a distribution matcher (DM). However, the DM schemes may suffer a rate loss and increase both complexity and latency, when they are not suited for the input bit stream with redundancy. In this paper, a novel PAS strategy is proposed for a joint source and channel coded modulation system, where the residual source redundancy after source coding can be exploited to obtain both shaping and coding gains. It is shown that the residual source redundancy can be controlled by choosing the row weight distribution for source code, thus making the probability distribution of the modulated symbols be optimized under an appropriate interleaver design. To guarantee the error-floor performance, the source code is constrained by predicting the probability distribution of source bits within target finite-length frames. By jointly designing source and channel codes, the residual source redundancy can also be exploited to achieve the coding gain. Compared with the state-of-the-art code pairs, the proposed code pairs have better error-floor performance, and achieve higher coding and shaping gains without suffering from the rate-loss and the latency caused by DM.