ARQ protocol for joint source and channel coding and its applications

Shannon\u27s separation theorem states that for transmission over noisy channels, approaching channel capacity is possible with the separation of source and channel coding. Practically, the situation is different. Infinite size blocks are needed to achieve this theoretical limit. Also, time-varying channels require a different approach. This leads to many approaches for source and channel coding. This dissertation will address a joint source and channel coding that suits Automatic Repeat Request (ARQ) application and applies it to packet switching networks. Following aspects of the proposed joint source and channel coding approach will be presented: The design of the proposed joint source and channel coding scheme. The approach is based on a variable length coding scheme which adapts the arithmetic coding process for joint source and channel coding. The protocol using this joint source and channel coding scheme in communication systems. The error recovery technique of the proposed scheme is presented. The application of the scheme and protocol. The design is applied to wireless TCP network and real-time video transmissions. The coding scheme embeds the redundancy needed for error detection in source coding stage. The self-synchronization property of lossless compression is utilized by decoder to detect channel errors. With this approach, error detection may be delayed. The delay in detection is referred to as error propagation distance. This work analyzes the distribution of error propagation distance. The error recovery technique of this joint source and channel coding for ARQ (JARQ) protocol is analyzed. Throughput is studied using signal flow graph for both independent channel and nonindependent channels. A packet combining technique is presented which utilizes the non-uniform distribution of error propagation distance to increase the throughput. The proposed scheme may be applied to many areas. In particular, two applications are discussed. A TCP/JARQ protocol stack is introduced and the coordination between TCP and JARQ layers is discussed to maximize system performance. By limiting the number of retransmission, the proposed scheme is applied to real-time transmission to meet timing requirement

Rate-distortion performance for joint source and channel coding of images

Caption title.Includes bibliographical references (p. 31-32).Supported by the German Educational Exchange Service (DAAD) as part of the HSP II-program, and in part by ARPA. F30602-92-C-0030 Supported by the Laboratory for Information and Decision Systems, Massachusetts Institute of Technology. DAAH04-95-1-0103Michael J. Ruf, James W. Modestino

connection optimization of joint source and channel coding based on protograph LDPC codes

香农的经典分离定理已证明，信源和信道编码分别达到最优时，整个系统的性能仍能保持最优。然而分离系统适用于码长无限长的点对点传输系统，并不适用于实际的传输系统中，联合信源信道编译码系统因为其良好的传输性能和低功耗的特点吸引了更多的关注。 目前存在很多联合信源信道编译码系统的设计方案，其中低密度奇偶校验码（LDPC码）由于其良好的逼近香农限的特点且较低的译码复杂度，被引入到信源和信道的编译码联合系统（JSCC）中，称之为D-LDPC的JSCC系统，为了更好的改进联合系统的性能和降低译码复杂度，原模图LDPC码被引入到联合信源信道编译码系统中，即DP-LDPC的JSCC系统，较之D-LDPC系统，D...According to Shannon’s classical separation theorem, the source and channel coding can be individually optimized while still maintaining the optimality of the whole system. However, the theorem holds only for the end-to-end communication systems with infinite channel code block length, which are seldom met in practical applications. Joint source-channel coding (JSCC) schemes draw much attention fo...学位：工学硕士院系专业：信息科学与技术学院_工程硕士(电子与通信工程)学号：2332013115324

Semantic Communications for Image Recovery and Classification via Deep Joint Source and Channel Coding

With the recent advancements in edge artificial intelligence (AI), future sixth-generation (6G) networks need to support new AI tasks such as classification and clustering apart from data recovery. Motivated by the success of deep learning, the semantic-aware and task-oriented communications with deep joint source and channel coding (JSCC) have emerged as new paradigm shifts in 6G from the conventional data-oriented communications with separate source and channel coding (SSCC). However, most existing works focused on the deep JSCC designs for one task of data recovery or AI task execution independently, which cannot be transferred to other unintended tasks. Differently, this paper investigates the JSCC semantic communications to support multi-task services, by performing the image data recovery and classification task execution simultaneously. First, we propose a new end-to-end deep JSCC framework by unifying the coding rate reduction maximization and the mean square error (MSE) minimization in the loss function. Here, the coding rate reduction maximization facilitates the learning of discriminative features for enabling to perform classification tasks directly in the feature space, and the MSE minimization helps the learning of informative features for high-quality image data recovery. Next, to further improve the robustness against variational wireless channels, we propose a new gated deep JSCC design, in which a gated net is incorporated for adaptively pruning the output features to adjust their dimensions based on channel conditions. Finally, we present extensive numerical experiments to validate the performance of our proposed deep JSCC designs as compared to various benchmark schemes

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 a capacity loss, which is found to have a value of 1.11 dB in a particular practical scenario. 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

On the joint source and channel coding of atomic image streams

This paper presents an error resilient coding scheme for atomic image bitstreams, as generated by Matching Pursuit encoders. A joint source and channel coding algorithm is proposed, that takes benefit of both the flexibility in the image representation, and the progressive nature of the bitstream, in order to finely adapt the channel rate to the relative importance of the bitstream components. An optimization problem is proposed, and a fast search algorithm determines the best rate allocation for given bit budget and loss process parameters. Simulation results show that the unequal error protection is quite efficient, even in very adverse conditions, and it clearly outperforms simple FEC schemes

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 (ExpG) Error Correction (RiceEC and ExpGEC) 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 trade-offs 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 transmissionenergy, -duration and -bandwidth as a Variable Length Error- Correction (VLEC) 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 (SSCC) benchmarkers, our codes consistently offer E_b/N_0 gains of between 0.5 dB and 1.0 dB which only appear to be 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, -duration, -bandwidth or decoding complexity