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检索条件"主题词=Joint Source-channel coding"

共 624 条记录，以下是131-140 订阅

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Deep joint source-channel coding using Overlap Image Division for Block Noise Reduction 99

Deep Joint Source-Channel Coding using Overlap Image Divisio...

引用

IEEE 99th Vehicular Technology Conference (VTC-Spring)

作者： Yamamoto, Ryunosuke Inoue, Yoshiaki Hisano, Daisuke Osaka Univ Grad Sch Engn Osaka Japan

ISBN: (纸本)9798350387414

Deep learning-based joint source-channel coding (Deep JSCC) has attracted attention. Deep JSCC maps information source features directly to IQ symbols using an autoencoder instead of source and channel coding. In image transmission, severe image degradation due to the cliff effect has been a problem in the lower signal-to-noise ratio (SNR) regions. Meanwhile, Deep JSCC provides a better peak signal-to-noise ratio (PSNR) even in low SNR regions. However, a high-resolution image needs to be divided into small patch images to input into Deep JSCC, consideration of the limitation of transmission capacity and delay. The image division causes block noise and PSNR degradation. This paper proposes Deep JSCC with overlapping image division to suppress the block noise. The proposed overlap division overlaps several pixels between patch images when dividing into patch images. After image recomposition by the decoder of Deep JSCC, the receiver removes the overlapping pixel information and combines the edge of the patch images smoothly. This paper conducts the experiments to reveal the effectiveness of block noise reduction owing to the proposed scheme with DIV2K data set. Consequently, we indicate the PSNR is drastically improved in the specific SNR region.

关键词： Overlap image division image coding joint source-channel coding deep learning

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Lossy joint source-channel coding in the finite blocklength regime 2012

Lossy joint source-channel coding in the finite blocklength ...

引用

IEEE International Symposium on Information Theory

作者： Kostina, Victoria Verdu, Sergio Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

ISBN: (纸本)9781467325790

This paper shows new tight finite-blocklength bounds for the best achievable lossy joint source-channel code rate, and demonstrates that joint source-channel code design brings considerable performance advantage over a separate one in the non-asymptotic regime. A joint source-channel code maps a block of k source symbols onto a length-n channel codeword, and the fidelity of reproduction at the receiver end is measured by the probability epsilon that the distortion exceeds a given threshold d. For memoryless sources and channels, it is demonstrated that the parameters of the best joint source-channel code must satisfy nC - kR(d) approximate to root nV + kV(d)Q(-1) (epsilon), where C and V are the channel capacity and dispersion, respectively;R(d) and V(d) are the source rate-distortion and rate-dispersion functions;and Q is the standard Gaussian complementary cdf.

关键词： Achievability converse finite blocklength regime joint source-channel coding lossy source coding memoryless sources rate-distortion theory Shannon theory

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PRIVACY-AWARE joint source-channel coding FOR IMAGE TRANSMISSION BASED ON DISENTANGLED INFORMATION BOTTLENECK 49

PRIVACY-AWARE JOINT SOURCE-CHANNEL CODING FOR IMAGE TRANSMIS...

引用

49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sun, Lunan Guo, Caili Chen, Mingzhe Yang, Yang Beijing Univ Posts & Telecommun Beijing Peoples R China Univ Miami Coral Gables FL 33124 USA

ISBN: (纸本)9798350344868;9798350344851

Current privacy-aware joint source-channel coding (JSCC) works aim at avoiding private information transmission by adversarially training the JSCC encoder and decoder under specific signal-to-noise ratios (SNRs) of eavesdroppers. However, these approaches incur additional computational and storage requirements as multiple neural networks must be trained for various eavesdroppers' SNRs to determine the transmitted information. To overcome this challenge, we propose a novel privacy-aware JSCC for image transmission based on disentangled information bottleneck (DIB-PAJSCC). In particular, we derive a novel disentangled information bottleneck objective to disentangle private and public information. Given the separate information, the transmitter can transmit only public information to the receiver while minimizing reconstruction distortion. Since DIB-PAJSCC transmits only public information regardless of the eavesdroppers' SNRs, it can eliminate additional training adapted to eavesdroppers' SNRs. Experimental results show that DIB-PAJSCC can reduce the eavesdropping accuracy on private information by up to 20% compared to existing methods.

关键词： joint source-channel coding image transmission privacy wiretap channel

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Deep joint source-channel coding Based on Semantics of Pixels for Wireless Image Transmission 34

Deep Joint Source-Channel Coding Based on Semantics of Pixel...

引用

IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Sun, Qizheng Guo, Caili Yang, Yang Tang, Rui Liu, Chuanhong Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Beijing Lab Adv Informat Networks Beijing 100876 Peoples R China

ISBN: (纸本)9781665464833

Current image coding methods for semantic communication typically concentrate on intelligent tasks or image reconstruction separately, and seldom consider both aspects simultaneously. To balance these two aspects during wireless image transmission, we propose a joint source-channel coding method based on the semantics of pixels (SP), which can retain both pixel information for reconstruction and semantic information for intelligent tasks. Specifically, we first design a gradient-based mechanism to quantify the semantic importance of downstream intelligent tasks on pixels. Then, we design the SP-based loss function to train the deep joint source-channel coding network. Experiment results demonstrate that the proposed method maintains reconstruction performance and improves the task performance by 1.61% and 4.06%, respectively, compared to the state-of-the-art deep joint source-channel coding method and traditional separate source-channel coding method at the same transmission rate and signal-to-noise ratio.

关键词： joint source-channel coding semantics of pixels semantic preservation intelligent task perception results

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DEEP joint source-channel coding OF IMAGES WITH FEEDBACK

DEEP JOINT SOURCE-CHANNEL CODING OF IMAGES WITH FEEDBACK

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kurka, David Burth Gunduz, Deniz Imperial Coll London Dept Elect & Elect Engn London England

ISBN: (纸本)9781509066315

We consider wireless transmission of images in the presence of channel output feedback, by introducing an autoencoder-based deep joint source-channel coding (JSCC) scheme. We achieve impressive results in terms of the end-to-end reconstruction quality for fixed length transmission, and in terms of the average delay for variable length transmission. To the best of our knowledge, this is the first practical JSCC scheme that can fully exploit channel output feedback, demonstrating yet another setting in which modern machine learning techniques can enable the design of new and efficient communication methods that surpass the performance of traditional structured coding schemes.

关键词： Deep neural networks Feedback joint source-channel coding Wireless image transmission

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DEEP LEARNING FOR joint source-channel coding OF TEXT

DEEP LEARNING FOR JOINT SOURCE-CHANNEL CODING OF TEXT

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Farsad, Nariman Rao, Milind Goldsmith, Andrea Stanford Univ Elect Engn Stanford CA 94305 USA

ISBN: (纸本)9781538646588

We consider the problem of joint source and channel coding of structured data such as natural language over a noisy channel. The typical approach to this problem in both theory and practice involves performing source coding to first compress the text and then channel coding to add robustness for the transmission across the channel. This approach is optimal in terms of minimizing end-to-end distortion with arbitrarily large block lengths of both the source and channel codes when transmission is over discrete memoryless channels. However, the optimality of this approach is no longer ensured for documents of finite length and limitations on the length of the encoding. We will show in this scenario that we can achieve lower word error rates by developing a deep learning based encoder and decoder. While the approach of separate source and channel coding would minimize bit error rates, our approach preserves semantic information of sentences by first embedding sentences in a semantic space where sentences closer in meaning are located closer together, and then performing joint source and channel coding on these embeddings.

关键词： deep learning natural language processing joint source-channel coding

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Semantics-Guided Contrastive joint source-channel coding for Image Transmission 14

Semantics-Guided Contrastive Joint Source-Channel Coding for...

引用

14th IEEE International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Hua, Wenhui Chen, Dezhao Fang, Junli Chen, Lingyu Mota, Joao F. C. Hong, Xuemin Xiamen Univ Sch Informat Xiamen Peoples R China Heriot Watt Univ Sch Engn & Phys Sci Edinburgh Scotland

ISBN: (纸本)9781665450850

Deep joint source-channel coding (D-JSCC) provides several advantages over conventional coding schemes, in which source and channel coding are designed separately. For example, D-JSCC schemes suffer from smaller delays and are more robust to rapid channel variation. However, D-JSCCs are often designed without explicit structure or insight, making them less adaptive, hard to control, and theoretically unfounded. In this paper, we propose a contrastive joint-source-channel coding (C-JSCC) design, which uses supervised contrastive learning (SCL) to make the latent space of a conventional D-JSCC more structured and meaningful. By testing on the CIFAR-10 dataset, we show that C-JSCC consistently outperforms its D-JSCC counterpart in both tasks of image reconstruction and classification. Moreover, C-JSCC is shown to output images with perceptual quality better than the classic BPG image codec in the low bits-per-pixel (bpp) region. The roles of various hyper-parameters in C-JSCC are investigated by analytical approximations, experiments, and visualization techniques.

关键词： Contrastive learning joint source-channel coding semantic information image communications

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ANALOG joint source-channel coding IN RAYLEIGH FADING channelS

ANALOG JOINT SOURCE-CHANNEL CODING IN RAYLEIGH FADING CHANNE...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： de Oliveira Brante, Glauber Gomes Souza, Richard Demo Garcia-Frias, Javier Fed Univ Technol Parana UTFPR CPGEI Curitiba Parana Brazil

ISBN: (纸本)9781457705397

We consider discrete-time all-analog-processing joint source-channel coding, using non-linear spiral-like curves. We assume a Rayleigh channel, where the receiver may employ or not multiple antennas. Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) detection are considered. Our results show that MMSE performs much better than ML in high CSNR in single-antenna wireless systems, while diversity combining is able to significantly reduce such performance gap, therefore making the low complexity ML decoding very attractive in the case of multiple receive antennas.

关键词： joint source-channel coding wireless channel receive diversity analog coding

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Federated Learning via Lattice joint source-channel coding

Federated Learning via Lattice Joint Source-Channel Coding

引用

IEEE International Symposium on Information Theory (ISIT)

作者： Azimi-Abarghouyi, Seyed Mohammad Varshney, Lay R. KTH Royal Inst Technol Stockholm Sweden Univ Illinois Urbana IL USA

ISBN: (纸本)9798350382853;9798350382846

This paper introduces a universal federated learning framework that enables over-the-air computation via digital communications, using a new joint source-channel coding scheme. Without relying on channel state information at devices, this scheme employs lattice codes to both quantize model parameters and exploit interference from the devices. A novel two-layer receiver structure at the server is designed to reliably decode an integer combination of the quantized model parameters as a lattice point for the purpose of aggregation. Numerical experiments validate the effectiveness of the proposed scheme. Even with the challenges posed by channel conditions and device heterogeneity, the proposed scheme markedly surpasses other over-the-air FL strategies.

关键词： Federated learning over-the-air computation joint source-channel coding lattice codes digital communications

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DeepJSCC-Q: channel Input Constrained Deep joint source-channel coding

DeepJSCC-Q: Channel Input Constrained Deep Joint Source-Chan...

引用

IEEE International Conference on Communications (ICC)

作者： Tung, Tze-Yang Kurka, David Burth Jankowski, Mikolaj Gunduz, Deniz Imperial Coll London Dept Elect & Elect Engn London England

ISBN: (纸本)9781538683477

Recent works have shown that the task of wireless transmission of images can be learned with the use of machine learning techniques. Very promising results in end-to-end image quality, superior to popular digital schemes that utilize source and channel coding separation, have been demonstrated through the training of an autoencoder, with a non-trainable channel layer in the middle. However, these methods assume that any complex value can be transmitted over the channel, which can prevent the application of the algorithm in scenarios where the hardware or protocol can only admit certain sets of channel inputs, such as the use of a digital constellation. Herein, we propose DeepJSCC-Q, an end-to-end optimized joint source-channel coding scheme for wireless image transmission, which is able to operate with a fixed channel input alphabet. We show that DeepJSCC-Q can achieve similar performance to models that use continuous-valued channel input. Importantly, it preserves the graceful degradation of image quality observed in prior work when channel conditions worsen, making DeepJSCC-Q much more attractive for deployment in practical systems.

关键词： joint source-channel coding wireless image transmission deep neural networks

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