Deep learning-based semantic communication has achieved remarkable progress with CNNs and Transformers.However,CNNs exhibit constrained performance in high-resolution image transmission,while Transformers incur high c...Deep learning-based semantic communication has achieved remarkable progress with CNNs and Transformers.However,CNNs exhibit constrained performance in high-resolution image transmission,while Transformers incur high computational cost due to quadratic complexity.Recently,VMamba,a novel state space model with linear complexity and exceptional long-range dependency modeling capabilities,has shown great potential in computer vision tasks.Inspired by this,we propose MNTSCC,an efficient VMamba-based nonlinear joint source-channel coding(JSCC)model for wireless image transmission.Specifically,MNTSCC comprises a VMamba-based nonlinear transform module,an MCAM entropy model,and a JSCC module.In the encoding stage,the input image is first encoded into a latent representation via the nonlinear transformation module,which is then processed by the MCAM for source distribution modeling.The JSCC module then optimizes transmission efficiency by adaptively assigning transmission rate to the latent representation according to the estimated entropy values.The proposedMCAMenhances the channel-wise autoregressive entropy model with attention mechanisms,which enables the entropy model to effectively capture both global and local information within latent features,thereby enabling more accurate entropy estimation and improved rate-distortion performance.Additionally,to further enhance the robustness of the system under varying signal-to-noise ratio(SNR)conditions,we incorporate SNR adaptive net(SAnet)into the JSCCmodule,which dynamically adjusts the encoding strategy by integrating SNRinformationwith latent features,thereby improving SNR adaptability.Experimental results across diverse resolution datasets demonstrate that the proposed method achieves superior image transmission performance compared to existing CNN-and Transformer-based semantic communication models,while maintaining competitive computational efficiency.In particular,under an Additive White Gaussian Noise(AWGN)channel with SNR=10 dB and a channel bandwidth ratio(CBR)of 1/16,MNTSCC consistently outperforms NTSCC,achieving a 1.72 dB Peak Signal-to-Noise Ratio(PSNR)gain on the Kodak24 dataset,0.79 dB on CLIC2022,and 2.54 dB on CIFAR-10,while reducing computational cost by 32.23%.The code is available at https://github.com/WanChen10/MNTSCC(accessed on 09 July 2025).展开更多
Semantic secure communication is an emerging field that combines the principles of source-channel coding with the need for secure data transmission.It is of great significance in modern communications to protect the c...Semantic secure communication is an emerging field that combines the principles of source-channel coding with the need for secure data transmission.It is of great significance in modern communications to protect the confidentiality and privacy of sensitive information and prevent information leaks and malicious attacks.This paper presents a novel approach to semantic secure communication through the utilization of joint source-channel coding,which is based on the design of an automated joint source-channel coding algorithm and an encryption and decryption algorithm based on semantic security.The traditional and state-of-the-art joint source-channel coding algorithms are selected as two baselines for different comparison purposes.Experimental results demonstrate that our proposed algorithm outperforms the first baseline algorithm,the traditional source-channel coding,by 61.21%in efficiency under identical channel conditions(SNR=15 dB).In security,our proposed method can resist 2 more types of attacks compared to the two baselines,exhibiting nearly no increases in time consumption and error rate compared to the state-of-the-art joint source-channel coding algorithm while the secure semantic communication is supported.展开更多
Deep learning-based Joint Source-Channel Coding(JSCC)is a crucial component in semantic communication,and recent research has made significant progress in adapting to different channels.In this paper,we propose a mult...Deep learning-based Joint Source-Channel Coding(JSCC)is a crucial component in semantic communication,and recent research has made significant progress in adapting to different channels.In this paper,we propose a multi-stage progressive technique called Deep learning based Progressive Joint Source-Channel Coding(DP-JSCC).This approach partitions the source into multiple stages and transmits the signals continuously.The receiver gradually enhances the quality of image reconstruction by progressively receiving the signals,offering greater flexibility compared to existing dynamic rate transmission methods.The model adopts a lightweight architectural design,where we introduce an efficient module called the Inverted Shuffle Attention Bottleneck(ISAB)and incorporate self-attention mechanisms in the encoding and decoding process to capture signal correlations and establish long-range dependencies.Additionally,we introduce the Progressive Focus Weight Allocation(PFWA)method to improve the image reconstruction capability in progressive transmission tasks.These design enhance the expressive capacity of the model.Simulation results demonstrate that DP-JSCC can flexibly adjust the transmission rate according to requirements without the need for retraining or deployment,enabling continuous optimization of signals at different rates.Furthermore,compared to stateof-the-art JSCC methods,DP-JSCC exhibits advantages in terms of computational complexity,parameter count,and reconstruction performance.展开更多
This paper proposes an adaptive joint source and channel coding scheme for H.264 video multicast over wireless LAN which takes into account the user topology changes and varying channel conditions of multiple users, a...This paper proposes an adaptive joint source and channel coding scheme for H.264 video multicast over wireless LAN which takes into account the user topology changes and varying channel conditions of multiple users, and dynamically allocates available bandwidth between source coding and channel coding, with the goal to optimize the overall system performance. In particular, source resilience and error correction are considered jointly in the scheme to achieve the optimal performance. And a channel estimation algorithm based on the average packet loss rate and the variance of packet loss rate is proposed also. Two overall performance criteria for video multicast are investigated and experimental results are presented to show the improvement obtained by the scheme.展开更多
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 pape...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.展开更多
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 fr...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).展开更多
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 sh...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.展开更多
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 decod...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.展开更多
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(RCP...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.展开更多
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 accordi...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.展开更多
To improve the performance of MIMO-OFDM video transmission systems on the limitation of wireless bandwidth and transmitting power,we propose an adaptive joint resource allocation algorithm with unequal error protectio...To improve the performance of MIMO-OFDM video transmission systems on the limitation of wireless bandwidth and transmitting power,we propose an adaptive joint resource allocation algorithm with unequal error protection(UEP) based on joint source-channel coding(JSCC) according to H.264 video compression standard and RCPT channel coding.According to different thresholds of the average SNR of subchannels,the algorithm dynamically allocates the source coding parameters of original video data and the channel coding parameters of RCPT,which realizes UEP for the compressed video data of different importance.Through the bit and power allocation based on MQAM modulation and the subspace allocation based on beamforming technology for different subcarriers,an adaptive joint resource allocation making full use of space-frequency domain resources have been realized.The simulation results indicate that the algorithm improves the adaptability of video transmission systems in different wireless environments and the quality of video retrieval.展开更多
For two-way video communications over wireless channels using the automatic repeat request (ARQ) retransmission scheme, TMN8 rate control scheme is not effective in minimizing the number of frames skipped and cannot g...For two-way video communications over wireless channels using the automatic repeat request (ARQ) retransmission scheme, TMN8 rate control scheme is not effective in minimizing the number of frames skipped and cannot guarantee video quality during the retransmissions of error packets. This paper presents a joint source channel bit allocation scheme that allocates target bits according to encoder buffer fullness and estimation of channel condition by retransmission information. The results obtained from implementing our scheme in H.263+coder over wireless channel model show that our proposed scheme encodes the video sequences with lower and steadier buffer delay, fewer frames skipped and higher average PSNR compared to TMN8.展开更多
Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpe...Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes indispensable.In that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information semantics.However,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of IK-HARQ.Therefore,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ.In this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework SC-TDA-HARQ.In particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for re-transmission.Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.展开更多
文摘Deep learning-based semantic communication has achieved remarkable progress with CNNs and Transformers.However,CNNs exhibit constrained performance in high-resolution image transmission,while Transformers incur high computational cost due to quadratic complexity.Recently,VMamba,a novel state space model with linear complexity and exceptional long-range dependency modeling capabilities,has shown great potential in computer vision tasks.Inspired by this,we propose MNTSCC,an efficient VMamba-based nonlinear joint source-channel coding(JSCC)model for wireless image transmission.Specifically,MNTSCC comprises a VMamba-based nonlinear transform module,an MCAM entropy model,and a JSCC module.In the encoding stage,the input image is first encoded into a latent representation via the nonlinear transformation module,which is then processed by the MCAM for source distribution modeling.The JSCC module then optimizes transmission efficiency by adaptively assigning transmission rate to the latent representation according to the estimated entropy values.The proposedMCAMenhances the channel-wise autoregressive entropy model with attention mechanisms,which enables the entropy model to effectively capture both global and local information within latent features,thereby enabling more accurate entropy estimation and improved rate-distortion performance.Additionally,to further enhance the robustness of the system under varying signal-to-noise ratio(SNR)conditions,we incorporate SNR adaptive net(SAnet)into the JSCCmodule,which dynamically adjusts the encoding strategy by integrating SNRinformationwith latent features,thereby improving SNR adaptability.Experimental results across diverse resolution datasets demonstrate that the proposed method achieves superior image transmission performance compared to existing CNN-and Transformer-based semantic communication models,while maintaining competitive computational efficiency.In particular,under an Additive White Gaussian Noise(AWGN)channel with SNR=10 dB and a channel bandwidth ratio(CBR)of 1/16,MNTSCC consistently outperforms NTSCC,achieving a 1.72 dB Peak Signal-to-Noise Ratio(PSNR)gain on the Kodak24 dataset,0.79 dB on CLIC2022,and 2.54 dB on CIFAR-10,while reducing computational cost by 32.23%.The code is available at https://github.com/WanChen10/MNTSCC(accessed on 09 July 2025).
基金supported in part by the National Key R&D Program of China under Grant 2022YFB3103500in part by the National Natural Science Foundation of China under Grant 62302195.
文摘Semantic secure communication is an emerging field that combines the principles of source-channel coding with the need for secure data transmission.It is of great significance in modern communications to protect the confidentiality and privacy of sensitive information and prevent information leaks and malicious attacks.This paper presents a novel approach to semantic secure communication through the utilization of joint source-channel coding,which is based on the design of an automated joint source-channel coding algorithm and an encryption and decryption algorithm based on semantic security.The traditional and state-of-the-art joint source-channel coding algorithms are selected as two baselines for different comparison purposes.Experimental results demonstrate that our proposed algorithm outperforms the first baseline algorithm,the traditional source-channel coding,by 61.21%in efficiency under identical channel conditions(SNR=15 dB).In security,our proposed method can resist 2 more types of attacks compared to the two baselines,exhibiting nearly no increases in time consumption and error rate compared to the state-of-the-art joint source-channel coding algorithm while the secure semantic communication is supported.
文摘Deep learning-based Joint Source-Channel Coding(JSCC)is a crucial component in semantic communication,and recent research has made significant progress in adapting to different channels.In this paper,we propose a multi-stage progressive technique called Deep learning based Progressive Joint Source-Channel Coding(DP-JSCC).This approach partitions the source into multiple stages and transmits the signals continuously.The receiver gradually enhances the quality of image reconstruction by progressively receiving the signals,offering greater flexibility compared to existing dynamic rate transmission methods.The model adopts a lightweight architectural design,where we introduce an efficient module called the Inverted Shuffle Attention Bottleneck(ISAB)and incorporate self-attention mechanisms in the encoding and decoding process to capture signal correlations and establish long-range dependencies.Additionally,we introduce the Progressive Focus Weight Allocation(PFWA)method to improve the image reconstruction capability in progressive transmission tasks.These design enhance the expressive capacity of the model.Simulation results demonstrate that DP-JSCC can flexibly adjust the transmission rate according to requirements without the need for retraining or deployment,enabling continuous optimization of signals at different rates.Furthermore,compared to stateof-the-art JSCC methods,DP-JSCC exhibits advantages in terms of computational complexity,parameter count,and reconstruction performance.
文摘This paper proposes an adaptive joint source and channel coding scheme for H.264 video multicast over wireless LAN which takes into account the user topology changes and varying channel conditions of multiple users, and dynamically allocates available bandwidth between source coding and channel coding, with the goal to optimize the overall system performance. In particular, source resilience and error correction are considered jointly in the scheme to achieve the optimal performance. And a channel estimation algorithm based on the average packet loss rate and the variance of packet loss rate is proposed also. Two overall performance criteria for video multicast are investigated and experimental results are presented to show the improvement obtained by the scheme.
基金Supported by the Foundation of Ministry of Education of China (211CERS10)
文摘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.
文摘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).
文摘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.
基金Supported by the Foundation of Ministry of Education of China (211CERS10)
文摘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.
基金National High-Tech Research and Development Plan of China(No.2003AA1Z2130)Science and Technology Project of Zhejiang Province,China(No.2006C11200)
文摘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.
基金This work was supported in part by the National Natural Science Foundation of China(62293481)in part by the Young Elite Scientists Sponsorship Program by CAST(2023QNRC001)+1 种基金in part by the National Natural Science Foundation for Young Scientists of China(62001050)in part by the Fundamental Research Funds for the Central Universities(2023RC95).
文摘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.
基金Sponsored by the Fundamental Research Funds for the Central Universities (Grant No. HIT. NSRIF. 201149)the National Natural Science Foundation of China (Grant No. 61071104)
文摘To improve the performance of MIMO-OFDM video transmission systems on the limitation of wireless bandwidth and transmitting power,we propose an adaptive joint resource allocation algorithm with unequal error protection(UEP) based on joint source-channel coding(JSCC) according to H.264 video compression standard and RCPT channel coding.According to different thresholds of the average SNR of subchannels,the algorithm dynamically allocates the source coding parameters of original video data and the channel coding parameters of RCPT,which realizes UEP for the compressed video data of different importance.Through the bit and power allocation based on MQAM modulation and the subspace allocation based on beamforming technology for different subcarriers,an adaptive joint resource allocation making full use of space-frequency domain resources have been realized.The simulation results indicate that the algorithm improves the adaptability of video transmission systems in different wireless environments and the quality of video retrieval.
文摘For two-way video communications over wireless channels using the automatic repeat request (ARQ) retransmission scheme, TMN8 rate control scheme is not effective in minimizing the number of frames skipped and cannot guarantee video quality during the retransmissions of error packets. This paper presents a joint source channel bit allocation scheme that allocates target bits according to encoder buffer fullness and estimation of channel condition by retransmission information. The results obtained from implementing our scheme in H.263+coder over wireless channel model show that our proposed scheme encodes the video sequences with lower and steadier buffer delay, fewer frames skipped and higher average PSNR compared to TMN8.
基金supported in part by the National Key Research and Development Program of China under Grant 2024YFE0200600in part by the National Natural Science Foundation of China under Grant 62071425+3 种基金in part by the Zhejiang Key Research and Development Plan under Grant 2022C01093in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LR23F010005in part by the National Key Laboratory of Wireless Communications Foundation under Grant 2023KP01601in part by the Big Data and Intelligent Computing Key Lab of CQUPT under Grant BDIC-2023-B-001.
文摘Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes indispensable.In that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information semantics.However,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of IK-HARQ.Therefore,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ.In this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework SC-TDA-HARQ.In particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for re-transmission.Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.
