We study deep jointsource-channelcoding (JSCC) in a distributed dual-view scenario where the sources are correlated images and the channels are independent. The transmitters encode respective images and then send th...
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We study deep jointsource-channelcoding (JSCC) in a distributed dual-view scenario where the sources are correlated images and the channels are independent. The transmitters encode respective images and then send them to the same central receiver. In this situation, how to effectively use the correlation between sources to enhance the reconstruction quality is worth investigating. In existing deep JSCC studies, the information fusion strategy failed to effectively utilize the correlation between images from different perspectives at the receiver. In this paper, we propose an information fusion module based on multi-layer cross-attention mechanism to fuse image features at different pixel levels to make full use of the source correlation. In addition, while most previous studies allocated the same bandwidth to all images, which ignored the differences between images, we design a content adaptive variable-rate module based on the proposed entropy mask. We conduct experiments on KITTI and InStereo2K datasets and evaluate them using peak signal-to-noise ratio (PSNR) and multi-scale structural similarity index (MS-SSIM) metrics. The experimental results show that our proposed multi-layer information fusion module and entropy mask module can effectively improve the quality of reconstruction by about 1.0 dB PSNR compared to the state-of-the-art distributed JSCC.
distributedsourcecoding (DSC) schemes rely on separate encoding but joint decoding of statistically dependent sources, which exhibit correlation. More specifically, distributed joint source-channel coding (DJSC) is ...
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distributedsourcecoding (DSC) schemes rely on separate encoding but joint decoding of statistically dependent sources, which exhibit correlation. More specifically, distributed joint source-channel coding (DJSC) is associated with the scenario, where the correlated source signals are transmitted through a noisy channel. On one hand, employing DSC or DJSC schemes exploits the existing correlation between sensors resulting in minimising the transmission energy required by the sources, while maintaining reliable communication. On the other hand, Network coding (NC) is an efficient data transport technique leveraging network efficiency, by allowing Relay Nodes (RNs) in a communication network to combine multiple data packets received via the incoming links before transmitting them to the Destination Node (DN). In this paper, the bandwidth -efficient distributedjoint Turbo Trellis -Coded Modulation (DJTTCM) aided by both Dynamic Network coding (DNC) and Adaptive DNC (ADNC)-based cooperative transmission schemes are proposed. Both systems are proposed for supporting correlated source transmissions over hostile channels experiencing both small-scale and large-scale fading in which the RNs dynamically transmit its non -binary linear combinations to the DN. A substantial gain of 19.5 dB was attained at a correlation coefficient of rho = 0.8 over its counterpart dispensing with NC.
In this paper, we propose a new class of distributed joint source-channel coding (DJSCC) methods, namely triple polar codes (T-PC), for transmitting a pair of correlated binary sources over noisy channels. In the T-PC...
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ISBN:
(纸本)9781665421607;9781665421591
In this paper, we propose a new class of distributed joint source-channel coding (DJSCC) methods, namely triple polar codes (T-PC), for transmitting a pair of correlated binary sources over noisy channels. In the T-PC structure, one source is protected by a systematic polar code (SPC), and the other source is encoded into a double polar code (D-PC) word. Following this, we prove the T-PC approaches the corner point of the achievable rate-region of DJSCC. We further propose a distributedjointsource-channel decoding algorithm, which involves two components: a cyclic redundancy check (CRC) aided successive cancellation list (CA-SCL) decoding of the SPC and a joint successive cancellation list (J-SCL) decoding of the D-PC. The CA-SCL and J-SCL decoding procedures alternately generate hard-decisions of sources which are iteratively exchanged as the side information and result in superior performance compared with the state-of-the-art polar code based DJSCC scheme.
We study the problem of deep jointsource-channelcoding (D-JSCC) for correlated image sources, where each source is transmitted through a noisy independent channel to the common receiver. In particular, we consider a...
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ISBN:
(纸本)9781665405409
We study the problem of deep jointsource-channelcoding (D-JSCC) for correlated image sources, where each source is transmitted through a noisy independent channel to the common receiver. In particular, we consider a pair of images captured by two cameras with probably overlapping fields of view transmitted over wireless channels and reconstructed in the center node. The challenging problem involves designing a practical code to utilize both source and channel correlations to improve transmission efficiency without additional transmission overhead. To tackle this, we need to consider the common information across two stereo images as well as the differences between two transmission channels. In this case, we propose a deep neural networks solution that includes lightweight edge encoders and a powerful center decoder. Besides, in the decoder, we propose a novel channel state information aware cross attention module to highlight the overlapping fields and leverage the relevance between two noisy feature *** results show the impressive improvement of reconstruction quality in both links by exploiting the noisy representations of the other link. Moreover, the proposed scheme shows competitive results compared to the separated schemes with capacity-achieving channel codes.
distributedsourcecoding (DSC) is a new technology for source compression. In contrast with the traditional source compression methods, DSC achieves low-complexity encoding by transferring complex decorrelation proce...
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distributedsourcecoding (DSC) is a new technology for source compression. In contrast with the traditional source compression methods, DSC achieves low-complexity encoding by transferring complex decorrelation process from the encoder to the decoder, so that it is suitable for friendly up-link serves of low-power devices such as mobile phones. This paper addresses the transmission of DSC bit stream and proposes two kinds of transmission systems, including distributed joint source-channel coding (DJSCC) and distributed separate source-channelcoding (DSSCC). And LDPC (Low Density Parity Check) is used in these two systems for the simulation experiments which finally show that DJSCC outperforms DSSCC in practice.
distributed joint source-channel coding (DJSCC) refers to the correlated sources which are encoded separately and reconstructed in the centre node by joint decoding. Therefore, the challenging problem involves designi...
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distributed joint source-channel coding (DJSCC) refers to the correlated sources which are encoded separately and reconstructed in the centre node by joint decoding. Therefore, the challenging problem involves designing a simple and efficient decoding algorithm to utilize the correlations among distributedsources. In this letter, a multi-task deep learning decoder is proposed for the low-density parity-check (LDPC) code-based DJSCC system to improve the performance. This proposal is based on the shared neural normalized min-sum (SNNMS) decoding network and the log likelihood ratio (LLR) shared unit is added to further exploit the source correlation. Moreover, an adaptive multi-loss function is proposed to prevent the network tilting the balance in favor of one certain task. Simulation results indicate that the proposed decoder can achieve a significant performance improvement with almost the same complexity compared with the separated SNNMS decoders.
作者：
Cen, FengTongji Univ
Dept Control Sci & Engn Shanghai 200092 Peoples R China
Low-density parity-check (LDPC) codes with the parity-based approach for distributedjointsourcechannelcoding (DJSCC) with decoder side information is described in this letter. The parity-based approach is theoreti...
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Low-density parity-check (LDPC) codes with the parity-based approach for distributedjointsourcechannelcoding (DJSCC) with decoder side information is described in this letter. The parity-based approach is theoretical limit achievable. Different edge degree distributions are used for source variable nodes and parity variable nodes. Particularly, the codeword-averaged density evolution (CADE) is presented for asymmetrically correlated nonuniform sources over the asymmetric memoryless transmission channel. Extensive simulations show that the splitting of variable nodes can improve the coding efficiency of suboptimal codes and lower the error floor.
coding complexity and error-resilience are the two key factors for video streaming in Wireless Multimedia Sensor Networks (WMSNs). Towards this objective, this paper proposes a Robust distributed Video coding (RDVC) f...
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coding complexity and error-resilience are the two key factors for video streaming in Wireless Multimedia Sensor Networks (WMSNs). Towards this objective, this paper proposes a Robust distributed Video coding (RDVC) framework to optimize the quality of video transmission for WMSNs. A new error-resilient Key frame coding scheme is proposed based on the protection of Wyner-Ziv coding (WZC), in which additional Wyner-Ziv bits play a significant role for better error resilience and better Rate/Distortion (RD) performance. In addition, following the theoretical background of distributedjointsourcechannelcoding (DJSCC), a novel distributedsourcechannel codec based on Group Puncture Rate Adaptive IRA code (GPRA-IRA) is proposed, upon which the error-resilient Wyner-Ziv codec is designed. These experimental results show that the proposed RDVC scheme outperforms the relevant coding in terms of RD performance, notably by up to 1-3 dB, while achieving a lower encoding complexity.
In the context of distributed joint source-channel coding, we conceive reduced-complexity turbo trellis coded modulation (TTCM)-aided syndrome-based block decoding for estimating the cross-over probability p(e) of the...
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In the context of distributed joint source-channel coding, we conceive reduced-complexity turbo trellis coded modulation (TTCM)-aided syndrome-based block decoding for estimating the cross-over probability p(e) of the binary symmetric channel, which models the correlation between a pair of sources. Our joint decoder achieves an accurate correlation estimation for varying correlation coefficients at 3 dB lower SNR, than conventional TTCM decoder, despite its considerable complexity reduction.
Considering the fact that sensors are energy-limited and the wireless channel conditions in wireless sensor networks, there is an urgent need for a low-complexity coding method with high compression ratio and noise-re...
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Considering the fact that sensors are energy-limited and the wireless channel conditions in wireless sensor networks, there is an urgent need for a low-complexity coding method with high compression ratio and noise-resisted features. This paper reviews the progress made in distributed joint source-channel coding which can address this issue. The main existing deployments, from the theory to practice, of distributed joint source-channel coding over the independent channels, the multiple access channels and the broadcast channels are introduced, respectively. To this end, we also present a practical scheme for compressing multiple correlated sources over the independent channels. The simulation results demonstrate the desired efficiency.
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