检索结果-内蒙古大学图书馆

channel-Blind joint source-channel coding for Wireless Image Transmission

SENSORS 2024年第12期24卷 4005页

作者： Yuan, Hongjie Xu, Weizhang Wang, Yuhuan Wang, Xingxing Commun Univ China State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China

joint source-channel coding (JSCC) based on deep learning has shown significant advancements in image transmission tasks. However, previous channel-adaptive JSCC methods often rely on the signal-to-noise ratio (SNR) of the current channel for encoding, which overlooks the neural network's self-adaptive capability across varying SNRs. This paper investigates the self-adaptive capability of deep learning-based JSCC models to dynamically changing channels and introduces a novel method named channel-Blind JSCC (CBJSCC). CBJSCC leverages the intrinsic learning capability of neural networks to self-adapt to dynamic channels and diverse SNRs without relying on external SNR information. This approach is advantageous, as it is not affected by channel estimation errors and can be applied to one-to-many wireless communication scenarios. To enhance the performance of JSCC tasks, the CBJSCC model employs a specially designed encoder-decoder. Experimental results show that CBJSCC outperforms existing channel-adaptive JSCC methods that depend on SNR estimation and feedback, both in additive white Gaussian noise environments and under slow Rayleigh fading channel conditions. Through a comprehensive analysis of the model's performance, we further validate the robustness and adaptability of this strategy across different application scenarios, with the experimental results providing strong evidence to support this claim.

关键词： wireless image transmission joint source-channel coding attention mechanism broadcasting

来源：评论

学校读者我要写书评

暂无评论

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

来源：评论

学校读者我要写书评

暂无评论

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

来源：评论

学校读者我要写书评

暂无评论

Bandwidth-Agile Image Transmission With Deep joint source-channel coding

引用

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2021年第12期20卷 8081-8095页

作者： Kurka, David Burth Gunduz, Deniz Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

We propose deep learning based communication methods for adaptive-bandwidth transmission of images over wireless channels. We consider the scenario in which images are transmitted progressively in layers over time or frequency, and such layers can be aggregated by receivers in order to increase the quality of their reconstructions. We investigate two scenarios, one in which the layers are sent sequentially, and incrementally contribute to the refinement of a reconstruction, and another in which the layers are independent and can be retrieved in any order. Those scenarios correspond to the well known problems of successive refinement and multiple descriptions, respectively, in the context of joint source-channel coding (JSCC). We propose DeepJSCC-l, an innovative solution that uses convolutional autoencoders, and present three architectures with different complexity trade-offs. To the best of our knowledge, this is the first practical multiple-description JSCC scheme developed and tested for practical information sources and channels. Numerical results show that DeepJSCC-l can learn to transmit the source progressively with negligible losses in the end-to-end performance compared with a single transmission. Moreover, DeepJSCC-l has comparable performance with state of the art digital progressive transmission schemes in the challenging low signal-to-noise ratio (SNR) and small bandwidth regimes, with the additional advantage of graceful degradation with channel SNR.

关键词： Image transmission joint source-channel coding multiple description coding successive refinement wireless communication

来源：评论

学校读者我要写书评

暂无评论

Communicating Correlated sources Over MAC and Interference channels II: joint source-channel coding

引用

IEEE TRANSACTIONS ON INFORMATION THEORY 2021年第6期67卷 3847-3872页

作者： Padakandla, Arun Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

We present the second part of our work on communicating correlated sources over multiple access (MAC) and interference channels (IC). Specifically, we undertake a Shannon-theoretic study of the above scenarios and focus on characterizing sufficient conditions for lossless recoverability of the sources at the decoder(s). We enhance the fixed block-length (B-L) coding technique by incorporating the technique of inducing source correlation onto channel inputs, originally discovered by Cover, El Gamal and Salehi. In contrast to the first part, performance analysis of a joint source-channel decoder poses new challenges. We enhance the earlier developed suite of coding and analytical tools to overcome these challenges and derive (simplified) single-letter characterizations for a new set of sufficient conditions for both scenarios. For both the MAC and IC problems, the derived sufficient conditions are (i) subsumed in the current known tightest, and (ii) strictly weaker for identified examples. Lastly, we propose simple 'plug-in' approaches that can further weaken the derived sufficient conditions. Our findings enable us to subsume Dueck's findings (1981) and go even further for the example considered therein.

关键词： Encoding channel coding Integrated circuits Task analysis Correlation Interference channels Tools Shannon theory joint source-channel coding inner bound achievability sufficient conditions correlated sources constant composition codes

来源：评论

学校读者我要写书评

暂无评论

A Type-Aware coding Approach to joint source-channel coding

引用

IEEE COMMUNICATIONS LETTERS 2021年第11期25卷 3454-3457页

作者： Yao, Xinyuanmeng Ma, Xiao Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Sun Yat Sen Univ Guangdong Key Lab Informat Secur Technol Guangzhou 510006 Peoples R China

In this letter, we propose a type-aware coding approach to joint source-channel coding (JSCC) and present a concrete construction with polarization-adjusted convolutional (PAC) codes. A data sequence is first encoded by enumerative coding and then encoded by a data-dependent PAC code, resulting in a transmitted codeword. At the receiver, a type-search decoding algorithm is employed, and the implementation complexity can be reduced by searching only those typical types. Simulation results show that the presented JSCC scheme can outperform the conventional tandem coding scheme with a fixed-to-fixed-length source code and achieve a performance within the gap between the upper and lower bound on the minimum FER of finite-length JSCCs.

关键词： Codes Encoding Decoding Picture archiving and communication systems Indexes Convolutional codes Upper bound Enumerative coding fixed-to-variable-length joint source-channel coding PAC codes type-aware coding

来源：评论

学校读者我要写书评

暂无评论

joint source-channel coding AND BAYESIAN MESSAGE PASSING DETECTION FOR GRANT-FREE RADIO ACCESS IN IOT

JOINT SOURCE-CHANNEL CODING AND BAYESIAN MESSAGE PASSING DET...

引用

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

作者： Dommel, Johannes Utkovski, Zoran Stanczak, Slawomir Simeone, Osvaldo Fraunhofer Heinrich Hertz Inst Dept Wireless Commun & Networks Berlin Germany Tech Univ Berlin Dept Telecommun Syst Network Informat Theory Grp Berlin Germany Kings Coll London Dept Informat Kings Ctr Learning & Informat Proc KCLIP London England

ISBN: (纸本)9781509066315

Consider an Internet-of-Things (IoT) system that monitors a number of multi-valued events through multiple sensors sharing the same bandwidth. Each sensor measures data correlated to one or more events, and communicates to the fusion center at a base station using grant-free random access whenever the corresponding event is active. The base station aims at detecting the active events, and, for each active event, to determine a scalar value describing each active event's state. A conventional solution based on Separate source-channel (SSC) coding would use a separate codebook for each sensor and decode the sensors' transmitted packets at the base station in order to subsequently carry out events' detection. In contrast, this paper considers a potentially more efficient solution based on joint source-channel (JSC) coding via a non-orthogonal generalization of Type-Based Multiple Access (TBMA). Accordingly, all sensors measuring the same event share the same codebook (with non-orthogonal codewords), and the base station directly detects the events' values without first performing individual decoding for each sensor. A novel Bayesian message-passing detection scheme is developed for the proposed TBMA-based protocol, and its performance is compared to conventional solutions.

关键词： Type-Based Multiple Access approximate message passing IoT random access joint source-channel coding

来源：评论

学校读者我要写书评

暂无评论

Impact of training models on deep joint source-channel coding applicable to 5G systems

引用

IEICE COMMUNICATIONS EXPRESS 2024年第12期13卷 466-469页

作者： Yamamoto, Ryunosuke Matsumoto, Keigo Inoue, Yoshiaki Hara, Yuko Maruta, Kazuki Nakayama, Yu Hisano, Daisuke Osaka Univ Grad Sch Engn 2-1 Yamadaoka Suita Osaka 5650871 Japan Tokyo Inst Technol Sch Engn 2-12-1 OhokayamaMeguro Ku Tokyo 1528550 Japan Tokyo Univ Sci Fac Engn 6-3-1 NiijukuKatsushika Ku Tokyo 1258585 Japan Tokyo Univ Agr & Technol Inst Engn 2-24-16 Naka Cho Koganei Tokyo 1848588 Japan

With the development of 5G technology and the proliferation of IoT devices, Deep joint source-channel coding (DeepJSCC) has attracted attention for efficiently transmitting video and image data. DeepJSCC can maintain a good peak signal-to-noise ratio (PSNR) of images even at a meager signal-to-noise ratio (SNR). In cellular communication systems, the compression ratio must adapt to channel fluctuations, requiring multiple training models at the base station. However, the optimal SNR and compression ratio combination during training has yet to be reported. This paper investigates the necessary number of training models by stepwise varying SNR and compression ratio during training.

关键词： 5G joint source-channel coding deep learning semantic com- munication image transmission

来源：评论

学校读者我要写书评

暂无评论

Polarized Dropout: A Novel Deep joint source channel coding Scheme for Erasure channels

引用

IEEE COMMUNICATIONS LETTERS 2024年第9期28卷 1986-1990页

作者： Ren, Zichang Wang, Yiru Zhao, Yuping Peking Univ Sch Elect Beijing 100871 Peoples R China

This letter investigates the challenges encountered by deep joint source-channel coding in erasure channels. We explore the effectiveness of the widely adopted dropout technique in endowing deep neural networks with resilience against erasures. However, directly applying dropout at the channel layer introduces uncertainty into the neural network's training process, leading to performance degradation. To address this issue, we introduce the Polarized Dropout scheme and a novel network architecture that encodes analog symbols using the Walsh-Hadamard transform based on real-number field computation. Leveraging the polarization of symbol recovery probabilities, for a given erasure rate, a determined set of neurons will be assigned a dropout rate of 1, while the remainder are assigned a dropout rate of 0. Simulation results indicate a maximum enhancement of nearly 6dB in communication performance.

关键词： joint source-channel coding deep learning polar code erasure channel joint source-channel coding deep learning polar code erasure channel

来源：评论

学校读者我要写书评

暂无评论

Low-Delay Analog Distributed joint source-channel coding using SIRENs 29

Low-Delay Analog Distributed Joint Source-Channel Coding usi...

引用

29th European Signal Processing Conference (EUSIPCO)

作者： Xuan, Ziwei Narayanan, Krishna Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

ISBN: (纸本)9789082797060

We consider the design of joint source-channel coding (JSCC) schemes for two multiterminal source-channel coding problems - namely, the transmission of a Gaussian source with side information at the receiver and the transmission of bivariate Gaussian sources over two independent Gaussian channels. Our focus is on low-delay transmission. We formulate the design problem as optimization of an autoencoder (AE) model, and show that sinusoidal representation networks (SIRENs) are a good choice due to their inherent periodicity and the ability of stretched sinusiods to cover the source space. We show that SIRENs outperform parametric ReLU based networks. The complexity of the proposed method scales better with source dimension than the best traditional schemes known in the literature while their performance is comparable to or better than that of the best traditional schemes. We demonstrate that the spontaneously learned encoder mappings share resemblance to the classical Wyner-Ziv mappings for JSCC with side information, and exhibits structured patterns in the case of distributed coding that are interpretable.

关键词： Distributed source-channel coding joint source-channel coding SIRENs deep learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：