Low complexity joint source-channel decoding for transmission of wavelet compressed images

To utilize residual redundancy to reduce the error induced by fading channels and decrease the complexity of the field model to describe the probability structure for residual redundancy, a simplified statistical model for residual redundancy and a low complexity joint source-channel decoding（JSCD） algorithm are proposed. The complicated residual redundancy in wavelet compressed images is decomposed into several independent 1-D probability check equations composed of Markov chains and it is regarded as a natural channel code with a structure similar to the low density parity check （LDPC） code. A parallel sum-product （SP） and iterative JSCD algorithm is proposed. Simulation results show that the proposed JSCD algorithm can make full use of residual redundancy in different directions to correct errors and improve the peak signal noise ratio （PSNR） of the reconstructed image and reduce the complexity and delay of JSCD. The performance of JSCD is more robust than the traditional separated encoding system with arithmetic coding in the same data rate.

JOINT SOURCE-CHANNEL DECODING OF HUFFMAN CODES WITH LDPC CODES

In this paper, we present a Joint Source-Channel Decoding algorithm (JSCD) for Low-Density Parity Check (LDPC) codes by modifying the Sum-Product Algorithm (SPA) to account for the source redun-dancy, which results from the neighbouring Huffman coded bits. Simulations demonstrate that in the presence of source redundancy, the proposed algorithm gives better performance than the Separate Source and Channel Decoding algorithm (SSCD).

Integrated Joint Source-Channel Decoding for H.264 Video Transmission

This paper proposes an integrated joint source-channel decoder （I-JSCD） using Max-Log-MAP method for sources encoded with exp-Golomb codes and convolutional codes, and proposes a system applying this method to decoding the VLC data, e.g. motion vector differences （MVDs）, of H.264 across an AWGN channel. This method combines the source code state-space and the channel code state-space together to construct a joint state-space, develops a 3-D trellis and a maximum a-posterior （MAP） algorithm to estimate the source sequence symbol by symbol, and then uses max-log approximation to simplify the algorithm. Experiments indicate that the proposed system gives significant improvements on peak signal-to-noise ratio （PSNR） （maximum about 15 dB） than a separate scheme. This also leads to a higher visual quality of video stream over a highly noisy channel.

Joint Source-Channel Decoding Scheme for Image Transmission over Wireless Channel

We improve the iterative decoding algorithm by utilizing the “leaked” residual redundancy at the output of the source encoder without changing the encoder structure for the noisy channel. The experimental results show that using the residual redundancy of the compressed source in channel decoding is an effective method to improve the error correction performance.

Realtime robust speech communication based on iterative joint source-channel decoding and demodulation algorithm for MELP vocoder

Realtime speech communications require high efficient compression algorithms to encode speech signals. As the compressed speech parameters are highly sensitive to transmission errors, robust source and channel decoding and demodulation schemes are both important and of practical use. In this paper, an it- erative joint souree-channel decoding and demodulation algorithm is proposed for mixed excited linear pre- diction （MELP） vocoder by both exploiting the residual redundancy and passing soft information through- out the receiver while introducing systematic global iteration process to further enhance the performance. Being fully compatible with existing transmitter structure, the proposed algorithm does not introduce addi- tional bandwidth expansion and transmission delay. Simulations show substantial error correcting perfor- mance and synthesized speech quality improvement over conventional separate designed systems in delay and bandwidth constraint channels by using the joint source-channel decoding and demodulation （JSCCM） algorithm.

Integrated Joint Source-Channel Symbol-by-Symbol Decoding of Variable-Length Codes Using 3-D MAP Sequence Estimation

Most of multimedia schemes employ variable-length codes （VLCs） like Huffman code as core components in obtaining high compression rates. However VLC methods are very sensitive to channel noise. The goal of this paper is to salvage as many data from the damaged packets as possible for higher audiovisual quality. This paper proposes an integrated joint source-channel decoder （I-JSCD） at a symbol-level using three-dimensional （3-D） trellis representation for first-order Markov sources encoded with VLC source code and convolutional channel code. This method combines source code and channel code state-spaces and bit-lengths to construct a two-dimensional （2-D） state-space, and then develops a 3-D trellis and a maximum a-posterior （MAP） algorithm to estimate the source sequence symbol by symbol. Experiment results demonstrate that our method results in significant improvement in decoding performance, it can salvage at least half of （50%） data in any channel error rate, and can provide additional error resilience to VLC stream like image, audio, video stream over high error rate links.

JOINT ITERATIVE DECODING FOR SIMPLE-ENCODING SYSTEMATIC IRREGULAR-LDPC-BASED CODED COOPERATION IN NON-IDEAL RELAY CHANNEL

In this paper, a new kind of simple-encoding irregular systematic LDPC codes suitable for one-relay coded cooperation is designed, where the proposed joint iterative decoding is effectively performed in the destination which is in accordance with the corresponding joint Tanner graph characterizing two different component LDPC codes used by the source and relay in ideal and non-ideal relay cooperations. The theoretical analysis and simulations show that the coded cooperation scheme obviously outperforms the coded non-cooperation one under the same code rate and decoding complex. The significant performance improvement can be virtually credited to the additional mutual exchange of the extrinsic information resulted by the LDPC code employed by the source and its counterpart used by the relay in both ideal and non-ideal cooperations.

Joint Iterative Decoding for Network-Coding-Based Multisource LDPC-Coded Cooperative MIMO

A network-coding-based multisource LDPC-coded cooperative MIMO scheme is proposed,where multiple sources transmit their messages to the destination with the assistance from a single relay.The relay cooperates with multiple sources simultaneously via network-coding.It avoids the issues of imperfect frequency/timing synchronization and large transmission delay which may be introduced by frequency-division multiple access(FDMA)/code-division multiple access(CDMA)and time-division multiple access(TDMA)manners.The proposed joint″Min-Sum″iterative decoding is effectively carried out in the destination.Such a decoding algorithm agrees with the introduced equivalent joint Tanner graph which can be used to fully characterize LDPC codes employed by the sources and relay.Theoretical analysis and numerical simulation show that the proposed scheme with joint iterative decoding can achieve significant cooperation diversity gain.Furthermore,for the relay,compared with the cascade scheme,the proposed scheme has much lower complexity of LDPC-encoding and is easier to be implemented in the hardware with similar bit error rate(BER)performance.

NEW ITERATIVE SUPER-TRELLIS DECODING WITH SOURCE A PRIORI INFORMATION FOR VLCS WITH TURBO CODES

A novel Joint Source and Channel Decoding (JSCD) scheme for Variable Length Codes (VLCs) concatenated with turbo codes utilizing a new super-trellis decoding algorithm is presented in this letter. The basic idea of our decoding algorithm is that source a priori information with the form of bit transition probabilities corresponding to the VLC tree can be derived directly from sub-state transitions in new composite-state represented super-trellis. A Maximum Likelihood (ML) decoding algorithm for VLC sequence estimations based on the proposed super-trellis is also described. Simu-lation results show that the new iterative decoding scheme can obtain obvious encoding gain especially for Reversible Variable Length Codes (RVLCs),when compared with the classical separated turbo decoding and the previous joint decoding not considering source statistical characteristics.

Joint Source-Channel Bit Allocation Based on Expected End-to-End Distortion Model

An adaptive joint source channel bit allocation method for video communications over error-prone channel is proposed.To protect the bit-streams from the channel bit errors,the rate compatible punctured convolution(RCPC)code is used to produce coding rates varying from 4/5 to 1/2 using the same encoder and the Viterbi decoder.An expected end-to-end distortion model was presented to estimate the distortion introduced in compressed source coding due to quantization and channel bit errors jointly.Based on the proposed end-to-end distortion model,an adaptive joint source-channel bit allocation method was proposed under time-varying error-prone channel conditions.Simulated results show that the proposed methods could utilize the available channel capacity more efficiently and achieve better video quality than the other fixed coding-based bit allocation methods when transmitting over error-prone channels.

A New Arithmetic Coding System Combining Source Channel Coding and MAP Decoding

A new arithmetic coding system combining source channel coding and maximum a posteriori decoding were proposed. It combines source coding and error correction tasks into one unified process by introducing an adaptive forbidden symbol. The proposed system achieves fixed length code words by adaptively adjusting the probability of the forbidden symbol and adding tail digits of variable length. The corresponding improved MAP decoding metric was derived. The proposed system can improve the performance. Simulations were performed on AWGN channels with various noise levels by using both hard and soft decision with BPSK modulation.The results show its performance is slightly better than that of our adaptive arithmetic error correcting coding system using a forbidden symbol.

面向大规模MIMO的分块空间调制技术研究

广义空间调制(Generalized Spatial Modulation,GSM)是一种基于多输入多输出(Multiple-Input Multiple-Output,MIMO)系统的高效的数字调制技术,在传统调制符号之外同时通过激活天线模式组合发送信息比特。针对大规模MIMO系统中发送天线数量过多导致的激活天线模式组合的数量过多的问题,提出了一种分块空间调制算法,将每个发送天线子块的激活模式组合构成完整的激活天线模式组合,可有效降低大规模MIMO系统中发送端的调制复杂度。在接收端使用球形译码方法实现逐个子块的天线激活模式组合的解调,可以大幅降低接收端的计算复杂度。仿真分析表明,提出的球形译码算法可以在大幅降低计算复杂度的情况下,实现接近最大似然(Maximum Likelihood,ML)接收机算法的误比特率(Bit Error Rate,BER)性能,且可实现接收端检测性能和计算复杂度之间的最佳折衷。

基于DNN的低复杂度联合解调译码迭代同步算法

在无线通信的诸多场景,如卫星通信、深空通信和隐蔽通信中,受限于发射功率、传输距离等因素,接收信号非常微弱。现有联合解调译码迭代同步算法,将信道编码增益作用于信号接收全过程,可有效降低接收机的同步门限,但是计算复杂度较高。利用迭代接收目标函数的形态一致特性,提出一种基于深度神经网络(deep neural network,DNN)的同步优化策略。该策略与传统的迭代同步方法相比,可在1e-5误码率下降低24%的计算复杂度。这一研究成果为迭代接收技术在更高数据速率场景下的工程应用提供了新的发展方向,同时展现出深度学习在解决复杂通信环境问题中的潜力。

融合实体语义的实体关系抽取联合解码

针对复杂语境中存在多义词或上下文联系不强的实体,导致模型难以正确识别其关系的问题,提出一种基于BERT和联合解码的实体关系抽取模型。该模型首先采用BERT对实体进行语义编码,提取出实体的上下文信息;然后,利用自注意力机制标记出头实体,并对尾实体进行预测;最后,设计联合解码机制,结合实体语义信息和关系抽取任务进行联合解码。实验结果表明,与基准模型相比,所提模型在纽约时报(NYT)数据集和WebNLG数据集上的准确率和F1值均有所提高,能够有效地提高实体关系提取的准确性。

Task-Oriented Semantic Communication with Foundation Models

In the future development direction of the sixth generation(6G)mobile communication,several communication models are proposed to face the growing challenges of the task.The rapid development of artificial intelligence(AI)foundation models provides significant support for efficient and intelligent communication interactions.In this paper,we propose an innovative semantic communication paradigm called task-oriented semantic communication system with foundation models.First,we segment the image by using task prompts based on the segment anything model(SAM)and contrastive language-image pretraining(CLIP).Meanwhile,we adopt Bezier curve to enhance the mask to improve the segmentation accuracy.Second,we have differentiated semantic compression and transmission approaches for segmented content.Third,we fuse different semantic information based on the conditional diffusion model to generate high-quality images that satisfy the users'specific task requirements.Finally,the experimental results show that the proposed system compresses the semantic information effectively and improves the robustness of semantic communication.

A Video Captioning Method by Semantic Topic-Guided Generation

In the video captioning methods based on an encoder-decoder,limited visual features are extracted by an encoder,and a natural sentence of the video content is generated using a decoder.However,this kind ofmethod is dependent on a single video input source and few visual labels,and there is a problem with semantic alignment between video contents and generated natural sentences,which are not suitable for accurately comprehending and describing the video contents.To address this issue,this paper proposes a video captioning method by semantic topic-guided generation.First,a 3D convolutional neural network is utilized to extract the spatiotemporal features of videos during the encoding.Then,the semantic topics of video data are extracted using the visual labels retrieved from similar video data.In the decoding,a decoder is constructed by combining a novel Enhance-TopK sampling algorithm with a Generative Pre-trained Transformer-2 deep neural network,which decreases the influence of“deviation”in the semantic mapping process between videos and texts by jointly decoding a baseline and semantic topics of video contents.During this process,the designed Enhance-TopK sampling algorithm can alleviate a long-tail problem by dynamically adjusting the probability distribution of the predicted words.Finally,the experiments are conducted on two publicly used Microsoft Research Video Description andMicrosoft Research-Video to Text datasets.The experimental results demonstrate that the proposed method outperforms several state-of-art approaches.Specifically,the performance indicators Bilingual Evaluation Understudy,Metric for Evaluation of Translation with Explicit Ordering,Recall Oriented Understudy for Gisting Evaluation-longest common subsequence,and Consensus-based Image Description Evaluation of the proposed method are improved by 1.2%,0.1%,0.3%,and 2.4% on the Microsoft Research Video Description dataset,and 0.1%,1.0%,0.1%,and 2.8% on the Microsoft Research-Video to Text dataset,respectively,compared with the existing video captioning methods.As a result,the proposed method can generate video captioning that is more closely aligned with human natural language expression habits.

不同解码方案下的星地融合网络混合多址接入性能分析

该文针对地面中继辅助的星地融合网络(ISTNs),分析了卫星采用不同解码方案时系统性能的差异。首先,提出一种新颖的用户配对方案,通过位置信息将卫星波束覆盖范围内的多个用户分为若干个组。接着,为了提高传输可靠性以及频谱利用率,用户采用混合多址接入技术与卫星进行通信。进一步,在考虑用户-地面中继链路服从Nakagami-m分布,地面中继-卫星链路服从相关阴影莱斯分布的情况下,分别推导出基于连续干扰消除(SIC)和联合解码(JD)两种解码方案的系统中断概率和吞吐量的闭合表达式。最后,计算机仿真验证了理论分析的正确性和所提方案相比正交多址(OMA)方案的优越性,并揭示了JD解码技术在ISTNs中的可行性。

SNR-adaptive deep joint source-channel coding scheme for image semantic transmission with convolutional block attention module

With the development of deep learning(DL),joint source-channel coding(JSCC)solutions for end-to-end transmission have gained a lot of attention.Adaptive deep JSCC schemes support dynamically adjusting the rate according to different channel conditions during transmission,enhancing robustness in dynamic wireless environment.However,most of the existing adaptive JSCC schemes only consider different channel conditions,ignoring the different feature importance in the image processing and transmission.The uniform compression of different features in the image may result in the compromise of critical image details,particularly in low signal-to-noise ratio(SNR)scenarios.To address the above issues,in this paper,a dual attention mechanism is introduced and an SNR-adaptive deep JSCC mechanism with a convolutional block attention module(CBAM)is proposed,in which matrix operations are applied to features in spatial and channel dimensions respectively.The proposed solution concatenates the pooling feature with the SNR level and passes it sequentially through the channel attention network and spatial attention network to obtain the importance evaluation result.Experiments show that the proposed solution outperforms other baseline schemes in terms of peak SNR(PSNR)and structural similarity(SSIM),particularly in low SNR scenarios or when dealing with complex image content.

一种非二进制LDPC与SCMA系统的联合检测译码方案

针对现有的一些低密度奇偶校验码(low-density parity code,LDPC)与稀疏码多址(sparse code multiple access,SCMA)系统联合检测方案复杂度高、误码率高、传输时延大、收敛速度慢的问题,提出一个非二进制低密度奇偶校验码(non-binary lowdensity parity code,NB-LDPC)与SCMA系统的联合检测译码(joint detection decoding,JDD)方案。在SCMA多用户检测部分改进基于阈值辅助的期望传播算法(expect propagation algorithm,EPA),在LDPC部分采用NB-LDPC并且在两节点更新过程选取部分消息状态值来改进译码算法,同时,利用联合因子图在联合检测译码信息交互时加入一种消息阻尼因子来提高收敛速度,最终完成联合检测译码过程。通过多角度仿真发现,该方案降低了复杂度和误码率,减小了传输时延,提高了收敛速度,并且在不同码本下均验证了所提方案的适用性。

融合额外实体信息的层级联合抽取

目前,基于长文本的实体关系抽取研究倾向于对实体和关系隐式建模,未能有效利用实体间、实体与关系间的隐含联系,无法获取两个相距较远实体之间的依赖关系。为此,提出一种融合额外实体信息的层级联合抽取模型J-AH,基于注意力建模实体间依赖特征,通过拼接融合实体类型和依赖特征得到实体聚合表示,计算文本聚合表示与关系信息的中间差异因子进行信息层级融合,形成实体间、实体与关系间相互促进、相互增强的正向互动。在英文公开数据集DialogueRE上的实验数据表明,模型的F1值相较于对比模型提升了1.3个百分点,验证了模型的优势。