In this paper, we are interested in the lossy transmission of a single source over parallel additive white Gaussian noise channels with independent quasi-static fading and receiver channel state information. We call t...
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ISBN:
(纸本)9781538647271
In this paper, we are interested in the lossy transmission of a single source over parallel additive white Gaussian noise channels with independent quasi-static fading and receiver channel state information. We call this the lossy multi-connectivity problem. Motivated by the ultra-reliable and low latency communication requirements, we consider the finite blocklength performance of lossy multi-connectivity. By generalizing the non-asymptotic bounds of Kostina and Verdi' for the lossy joint source-channel coding problem, we derive non-asymptotic achievability and converse bounds for the lossy multi-connectivity problem. Using these non-asymptotic bounds, under mild conditions on the fading distribution, we derive good approximations for the finite blocklength performance in the spirit of second-order asymptotics for any discrete memoryless source under any bounded distortion measure. Our results demonstrate that the asymptotic notions of outage probability and outage capacity are actually good criteria even in the finite blocklength regime. Finally, we illustrate our results via numerical examples.
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 symbo...
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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.
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 e...
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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.
In this paper, we consider transmission of a Gaussian source over a Gaussian channel under bandwidth compression in the presence of interference known only to the transmitter. We study hybrid digital-analog (HDA) join...
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ISBN:
(纸本)9781457704376
In this paper, we consider transmission of a Gaussian source over a Gaussian channel under bandwidth compression in the presence of interference known only to the transmitter. We study hybrid digital-analog (HDA) joint source-channel coding schemes and propose two novel coding schemes that achieve the optimal mean-squared error (MSE) distortion. This can be viewed as the extension of results by Wilson et al. [1], originally proposed for sending a Gaussian source over a Gaussian channel in two cases: 1) Matched bandwidth with known interference only at the transmitter, 2) bandwidth compression where there is no interference in the channel. The proposed HDA codes can cancel the interference of the channel and obtain the "optimum performance theoretically attainable" (OPTA) of the AWGN channel with no interference in the case of bandwidth compression. We also provide performance analysis in the presence of signal-to-noise ratio (SNR) mismatch where we expect that HDA schemes perform better than strictly digital schemes.
Here, we design the efficient technique of transmitting the source transition probability matrix (STPM) by accompanied with the lowest frequency subband (LFS). The entries of this stochastic matrix are adaptively comp...
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ISBN:
(纸本)9781424451234
Here, we design the efficient technique of transmitting the source transition probability matrix (STPM) by accompanied with the lowest frequency subband (LFS). The entries of this stochastic matrix are adaptively computed by applying the first order Markov model with MPEG-4 zerotree sequences. Because of channel disturbances, we obtain the mismatched STPM at the ML-Viterbi receiver and then employ it for newly computed branch metrics at the MAP source-controlled channel decoder. For analysis, we also evaluate the residual redundancies for both the "Lena" and the "Barbara" images. The system performance is summarized in term of both PSNR (dB) and WER for three types of slow flat Rician fading channels. In the mismatched STPM simulation results, we still obtain the most PSNR improvement of about 0.14 dB.
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 for...
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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) sourcecoding to the context of joint source-channel coding. We validate the effectiveness of our approach by several numerical simulations over typical transmission scenarios.
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 nove...
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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 jointsource-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.
This paper concerns new effective method of joint data coding/ modulation which may improve energy-efficiency and energy savings of modern wireless transmission systems. The method require a priori knowledge of input ...
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ISBN:
(纸本)9781538676325
This paper concerns new effective method of joint data coding/ modulation which may improve energy-efficiency and energy savings of modern wireless transmission systems. The method require a priori knowledge of input data probability distribution to map them to the modulation symbols in the most efficient way.
We investigate fundamental limits of lossy communication in a bi-directional (two-way), half-duplex relay channel, where users wish to exchange correlated Gaussian sources and do so by sending their data according to ...
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ISBN:
(纸本)9781467331227
We investigate fundamental limits of lossy communication in a bi-directional (two-way), half-duplex relay channel, where users wish to exchange correlated Gaussian sources and do so by sending their data according to a two-phase transmission protocol. We first establish a general distortion inner bound using the data processing inequality. Based on uncoded transmission schemes, separate source and channelcoding schemes, and a lattice coding scheme, we then develop several cooperative joint source-channel coding (JSCC) approaches. Furthermore, we compare the corresponding achievable distortion performances in terms of the correlation coefficient between Gaussian sources. This comparison particularly shows that separate source and channelcoding in combination with decode-and-forward relaying achieves the best distortion values of any joint source-channel coding scheme in this paper.
The SoftCast scheme has been proposed as a promising alternative to traditional video broadcasting systems in wireless environments. In its current form, SoftCast performs image decoding at the receiver side by using ...
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ISBN:
(纸本)9781665432870
The SoftCast scheme has been proposed as a promising alternative to traditional video broadcasting systems in wireless environments. In its current form, SoftCast performs image decoding at the receiver side by using a Linear Least Square Error (LLSE) estimator. Such approach maximizes the reconstructed quality in terms of Peak Signal-to-Noise Ratio (PSNR). However, we show that the LLSE induces an annoying blur effect at low channel Signal-to-Noise Ratio (CSNR) quality. To cancel this artifact, we propose to replace the LLSE estimator by the Zero-Forcing (ZF) one. In order to better understand the perceived quality offered by these two estimators, a mathematical characterization as well as an objective and subjective studies are performed. Results show that the gains brought by the LLSE estimator, in terms of PSNR and Structural SIMiliraty (SSIM), are limited and quickly tend to null value as the CSNR increases. However, higher gains are obtained by the ZF estimator when considering the recent Video Multi-method Assessment Fusion (VMAF) metric proposed by Netflix, which evaluates the perceptual video quality. This result is confirmed by the subjective assessment.
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