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

作者： Bokaeil, Mohammad Jensen, Jesper Doclo, Simon Ostergaard, Jan Aalborg Univ Aalborg Denmark Oticon AS Copenhagen Denmark Carl von Ossietzky Univ Oldenburg Dept Med Phys & Acoust Oldenburg Germany

ISBN: (纸本)9798350374520;9798350374513

Ensuring intelligible speech communication for hearing assistive devices in low-latency scenarios presents significant challenges in terms of speech enhancement, coding and transmission. In this paper, we propose novel solutions for low-latency joint speech transmission and enhancement, leveraging deep neural networks (DNNs). Our approach integrates two state-of-the-art DNN architectures for low-latency speech enhancement and low-latency analog joint source-channel-based transmission, creating a combined low-latency system and jointly training both systems in an end-to-end approach. Due to the computational demands of the enhancement system, this order is suitable when high computational power is unavailable in the decoder, like hearing assistive devices. The proposed system enables the configuration of total latency, achieving high performance even at latencies as low as 3 ms, which is typically challenging to attain. The simulation results provide compelling evidence that a joint enhancement and transmission system is superior to a simple concatenation system in diverse settings, encompassing various wireless channel conditions, latencies, and background noise scenarios.

关键词： low-latency joint source-channel coding analogue speech transmission speech enhancement

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Adaptive Early Exiting for Collaborative Inference over Noisy Wireless channels 1

Adaptive Early Exiting for Collaborative Inference over Nois...

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1st IEEE International Conference on Machine Learning for Communication and Networking (IEEE ICMLCN)

作者： Jankowski, Mikolaj Gunduz, Deniz Mikolajczyk, Krystian Imperial Coll London London England Boeing Poland Gdansk Poland

ISBN: (纸本)9798350343205;9798350343199

Collaborative inference systems are one of the emerging solutions for deploying deep neural networks (DNNs) at the wireless network edge. Their main idea is to divide a DNN into two parts, where the first is shallow enough to be reliably executed at edge devices of limited computational power, while the second part is executed at an edge server with higher computational capabilities. The main advantage of such systems is that the input of the DNN gets compressed as the subsequent layers of the shallow part extract only the information necessary for the task. As a result, significant communication savings can be achieved compared to transmitting raw input samples. In this work, we study early exiting in the context of collaborative inference, which allows obtaining inference results at the edge device for certain samples, without the need to transmit the partially processed data to the edge server at all, leading to further communication savings. The central part of our system is the transmission-decision (TD) mechanism, which, given the information from the early exit, and the wireless channel conditions, decides whether to keep the early exit prediction or transmit the data to the edge server for further processing. In this paper, we evaluate various TD mechanisms and show experimentally, that for an image classification task over the wireless edge, proper utilization of early exits can provide both performance gains and significant communication savings.

关键词： Collaborative learning edge machine learning joint source-channel coding early exit

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DCS-JSCC: Leveraging Deep Compressed Sensing into JSCC for Wireless Image Transmission 25

DCS-JSCC: Leveraging Deep Compressed Sensing into JSCC for W...

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25th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Jarrahi, Mohammad Amin Bourtsoulatze, Eirina Abolghasemi, Vahid Univ Essex Sch Comp Sci & Elect Engn CSEE Colchester Essex England

ISBN: (纸本)9798350393194;9798350393187

This paper presents a novel approach integrating deep compressed sensing (DCS) into joint source-channel coding (JSCC) for efficient image transmission. Leveraging the capabilities of DCS, the proposed method offers enhanced compression and resilience to channel noise in wireless image transmission systems. A key component of the proposed method is utilising a convolutional neural network (CNN) structure to implement a block-based DCS technique for image compression. The proposed encoder consists of a well-designed CNN-based structure to capture structural information of the input image which is subsequently mapped to a complex-value domain. Finally, the proposed decoder deals with channel noise and reconstructs the original image. Applying the proposed CNN-based sampling matrices and reconstruction capabilities helps the proposed algorithm enhance image compression and reconstruction in wireless transmission systems. The CIFAR-10 and Kodak datasets are used to evaluate the performance of DCS-JSCC, showing a significant improvement in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), across different channel Signal-to-Noise Ratios (SNRs) and channel bandwidth values in comparison with state-of-art JSCC frameworks. Experimental evaluations demonstrate the effectiveness of the proposed method in achieving superior compression levels and maintaining image quality under varying channel conditions.

关键词： Wireless image transmission joint source-channel coding compressed sensing deep learning

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Two-Way source-channel coding

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IEEE TRANSACTIONS ON INFORMATION THEORY 2021年第10期67卷 6507-6524页

作者： Weng, Jian-Jia Alajaji, Fady Linder, Tamas Natl Taiwan Ocean Univ Dept Elect Engn Keelung 202301 Taiwan Queens Univ Dept Math & Stat Kingston ON K7L 3N6 Canada

We propose an adaptive lossy joint source-channel coding (JSCC) scheme for sending correlated sources over two-terminal discrete-memoryless two-way channels (DM-TWCs). The main idea is to couple the independent operations of the terminals via an adaptive coding mechanism, which can mitigate cross-interference resulting from simultaneous channel transmissions and concurrently exploit the sources' correlation to reduce the end-to-end reconstruction distortions. Our adaptive JSCC scheme not only subsumes existing lossy coding methods for two-way simultaneous communication but also improves their performance. Furthermore, we derive outer bounds for our two-way lossy transmission problem and establish complete JSCC theorems in some special settings. In these special cases, a non-adaptive separate source-channel coding (SSCC) scheme achieves the optimal performance, thus simplifying the design of the source-channel communication system.

关键词： channel coding source coding Protocols Propagation losses Adaptive coding Distortion Noise measurement Network information theory two-way channels lossy transmission joint source-channel coding correlated sources hybrid analog and digital coding superposition coding adaptive coding

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Deep FAVIB: Deep Learning-Based Forward-Aware Quantization via Information Bottleneck Method 59

Deep FAVIB: Deep Learning-Based Forward-Aware Quantization v...

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59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Hummert, Matthias Hassanpour, Shayan Wuebben, Dirk Dekorsy, Armin Univ Bremen Dept Commun Engn D-28359 Bremen Germany

ISBN: (纸本)9781728190549

We focus on a (generic) joint source-channel coding problem, appearing in a broad variety of real-world application. Explicitly, a noisy observation from a user/source signal should be compressed, ahead of getting forwarded over an error-prone and rate-limited channel to a remote processing unit. The design problem shall be formulated in a fashion that the impacts of the forward link are taken into account. Aligned with the Information Bottleneck (IB) method, we consider the Mutual Information (MI) as the fidelity criterion, and work out a data-driven approach to tackle the underlying design problem based upon a finite sample set. For that, we derive a tractable variational lower-bound of the objective functional, and present a general learning architecture which can be used to optimize the given lower-bound by standard training of the encoder and decoder Deep Neural Networks. This approach that is, principally, based upon the (generative) latent variable models, extends the concepts of Variational AutoEncoder (VAE) and Deep Variational Information Bottleneck (Deep VIB) for (remote) source coding to the context of joint source-channel coding. We validate the effectiveness of our approach by several numerical simulations over typical transmission scenarios.

关键词： Information bottleneck variational autoencoder deep learning quantization joint source-channel coding 6G

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Contrastive Learning based Semantic Communication for Wireless Image Transmission 98

Contrastive Learning based Semantic Communication for Wirele...

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98th IEEE Vehicular Technology Conference (VTC-Fall)

作者： Tang, Shunpu Yang, Qianqian Fan, Lisheng Lei, Xianfu Deng, Yansha Nallanathan, Arumugam Zhejiang Univ Key Lab Collaborat Sensing & Autonomous Unmanned Hangzhou Peoples R China Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou Peoples R China Southwest Jiaotong Univ Inst Mobile Commun Chengdu Peoples R China Kings Coll London Dept Engn London England Queen Mary Univ London Sch Elect Engn & Comp Sci London England

ISBN: (纸本)9798350329285

Recently, semantic communication has been widely applied in wireless image transmission systems as it can prioritize the preservation of meaningful semantic information in images over the accuracy of transmitted symbols, leading to improved communication efficiency. However, existing semantic communication approaches still face limitations in achieving considerable inference performance in downstream AI tasks like image recognition, or balancing the inference performance with the quality of the reconstructed image at the receiver. Therefore, this paper proposes a contrastive learning (CL)-based semantic communication approach to overcome these limitations. Specifically, we regard the image corruption during transmission as a form of data augmentation in CL and leverage CL to reduce the semantic distance between the original and the corrupted reconstruction while maintaining the semantic distance among irrelevant images for better discrimination in downstream tasks. Moreover, we design a two-stage training procedure and the corresponding loss functions for jointly optimizing the semantic encoder and decoder to achieve a good trade-off between the performance of image recognition in the downstream task and reconstructed quality. Simulations are finally conducted to demonstrate the superiority of the proposed method over the competitive approaches. In particular, the proposed method can achieve up to 56% accuracy gain on the CIFAR10 dataset when the bandwidth compression ratio is 1/48.

关键词： Semantic communication image transmission contrastive learning joint source-channel coding

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TAGIC: Task-Guided Image Communication Framework for Seamless Teleoperation

TAGIC: Task-Guided Image Communication Framework for Seamles...

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IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Diao, Yufeng Zhang, Yichi Zhao, Philip G. De Martini, Daniele Univ Glasgow Sch Engn Glasgow Lanark Scotland Univ Manchester Dept Comp Sci Manchester Lancs England Univ Oxford Oxford Robot Inst ORI Oxford England

ISBN: (纸本)9798350384482;9798350384475

Image-based teleoperation offers significant flexibility and efficiency in several applications, such as teleoperated driving;still, it highly depends on reliable communication bandwidth and high Signal-to-Noise Ratio (SNR), which is hard to guarantee in uncontrolled environments. This poster tackles the challenge of reliable communication under limited bandwidth. We propose to leverage the context and task knowledge to guide the compression to favor task performance rather than image fidelity. In particular, we jointly designed source-channel coding with a task performer to present an end-to-end TAsk-Guided Image Communication (TAGIC) framework, which uses Soft Introspective Variational Autoencoder (S-IntroVAE) and prioritizes the task-critical image information with limited communication bandwidth in the low SNR region. We demonstrate the effectiveness of TAGIC in a teleoperated driving scenario through the CARLA simulation platform - a widely used simulator in the autonomous driving community. Given the equivalent value of bandwidth compression ratio, TAGIC achieves a 202.6% improvement in the driving score over existing methods at low SNR.

关键词： joint source-channel coding teleoperated driving

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Optimizing Low-Complexity Analog Mappings for Low-Power Sensors With Energy Scheduling Capabilities

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IEEE INTERNET OF THINGS JOURNAL 2022年第18期9卷 17387-17402页

作者： Mouris, Boules A. Stavrou, Photios A. Thobaben, Ragnar KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Informat Sci & Engn S-10044 Stockholm Sweden EURECOM F-06904 Sophia Antipolis France

Power consumption is a major challenge for a massive deployment of wireless sensors in Internet of Things (IoT) networks. This article studies the use of analog joint source-channel coding (AJSCC) mappings in low-power sensing schemes. In particular, we propose a novel triangular mapping geometry as a low-complexity dimension reduction mapping. The proposed triangular mapping is employed for analog compression of multiple sensor readings into one signal and, thus, limits the need for power-hungry analog-to-digital conversion and processing at the sensing nodes. A comprehensive performance analysis of the proposed triangular mapping in terms of the mean squared error (MSE) performance is provided analytically and verified numerically. The problem of mapping adaptation to different source distributions is also studied. Moreover, the proposed triangular mapping is adopted in an energy scheduling problem in which the sensing nodes schedule their use of the received powers at different time instants and adjust the mapping parameters accordingly with the goal of minimizing the sum distortion at the receiver. We present a fast low-complexity algorithm for optimal energy scheduling and verify its performance in comparison with commercial convex optimization solvers. It is shown that the proposed mapping provides a very good MSE performance compared to the AJSCC benchmarks despite having a much lower complexity circuit implementation.

关键词： Analog mappings energy harvesting joint source-channel coding low-power sensors

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channel-CONFIGURABLE DEEP WIRELESS SPEECH TRANSMISSION 25

CHANNEL-CONFIGURABLE DEEP WIRELESS SPEECH TRANSMISSION

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Bokaei, Mohammad Jensen, Jesper Doclo, Simon Ostergaard, Jan Aalborg Univ Aalborg Denmark Oticon AS Copenhagen Denmark Carl von Ossietzky Univ Oldenburg Oldenburg Germany

ISBN: (纸本)9798350303582;9798350303599

The proliferation of edge-based wireless speech applications necessitates the development of resource-efficient, low-latency speech communication systems capable of functioning across diverse communication channel conditions. Ensuring intelligible speech communication under conditions of constrained resources and low-latency presents a challenging problem within the domain of speech transmission. In this paper, we introduce a very low-latency configurable speech transmission system leveraging joint source-channel coding and deep neural networks (DNNs). Our proposed system is a unified deep neural network system engineered to operate effectively across a wide range of wireless communication channel scenarios. The system encompasses both a joint source-channel encoder and a joint source-channel decoder, each with access to channel state information (CSI). In this context, CSI signifies the type of fading in the wireless channel. Notably, our system has a total latency of 2 ms. Through extensive simulations, we empirically demonstrate that the proposed configurable system closely approximates the performance of ideal systems specifically tailored to individual wireless channel scenarios. Our evaluation is rooted in the assessment of instrumental measures of speech quality and intelligibility, affirming the efficacy of our system in diverse and resource-constrained communication contexts.

关键词： low-latency joint source-channel coding speech transmission edge communication

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Diversity Image coding Using Irregular Interpolation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2021年 30卷 5665-5677页

作者： Goren, Mor Zamir, Ram Tel Aviv Univ EE Syst Dept IL-69978 Tel Aviv Israel Nvidia Corp Mellanox Technol IL-69710 Tel Aviv Israel

Diversity "multiple description" (MD) source coding promises graceful degradation in the presence of a priori unknown number of erased packets in the channel. A simple coding scheme for the case of two packets consists of oversampling the source by a factor of two and delta-sigma quantization. This approach was applied successfully to JPEG-based image coding over a lossy packet network, where the interpolation and splitting into two descriptions are done in the discrete cosine transform (DCT) domain. Moreover, unlike the classical source-channel separation approach - which is designed for a predetermined number of erasures (say, K out of N), hence its distortion does not improve when the channel behaves better than expected - an MD coding scheme aims to achieve a better reconstruction quality when more or all the N descriptions are received at the decoder side. The extension to a larger number of descriptions, however, suffers from noise amplification whenever the received descriptions form a non-uniform sampling pattern. In this work, we examine inter- and intra-block interpolation methods, and show how noise amplification can be reduced by redesigning the interpolation filter at the encoder. Specifically, for a given total coding rate, we demonstrate that an "irregular" interpolation filter is robust to the pattern of received packets over all ( K out of N) patterns, with some degradation relative to low-pass (LP) interpolation in the case where all N packets arrived. We provide experimental results comparing LP and irregular interpolation filters, and examine the effect of noise shaping on the trade-off between the central distortion (receiving all packets) and side distortion (receiving K packets).

关键词： Multiple descriptions source coding JPEG interpolation DCT irregular spectrum joint source-channel coding delta-sigma quantization

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