Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost-effectiveness, and ease of deployment. The rapid advancement of 5G tec...
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Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost-effectiveness, and ease of deployment. The rapid advancement of 5G technology and mobile edge computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices. However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. In response to the aforementioned challenges, this paper proposes a semantic communication solution. Specifically, considering the limited computational and storage resources of WSN devices, we propose a flexible Attention-based Adaptive coding (AAC) module. This module integrates window and channel attention mechanisms, dynamically adjusts semantic information in response to the current channel state, and facilitates adaptation of a single model across various Signal-to-Noise Ratio (SNR) environments. Furthermore, to validate the effectiveness of this approach, the paper introduces an end-to-end joint source channel coding (JSCC) scheme for image semantic communication, employing the AAC module. Experimental results demonstrate that the proposed scheme surpasses existing deep JSCC schemes across datasets of varying resolutions;furthermore, they validate the efficacy of the proposed AAC module, which is capable of dynamically adjusting critical information according to the current channel state. This enables the model to be trained over a range of SNRs and obtain better results.
Low-latency configurable speech transmission presents significant challenges in modern communication systems. Traditional methods rely on separate source and channelcoding, which often degrades performance under low-...
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Low-latency configurable speech transmission presents significant challenges in modern communication systems. Traditional methods rely on separate source and channelcoding, which often degrades performance under low-latency constraints. Moreover, non-configurable systems require separate training for each condition, limiting their adaptability in resource-constrained scenarios. This paper proposes a configurable low-latency deep jointsource-channelcoding (JSCC) system for speech transmission. The system can be configured for varying signal-to-noise ratios (SNR), wireless channel conditions, or bandwidths. A jointsource-channel encoder based on deep neural networks (DNN) is used to compress and transmit analog-coded information, while a configurable decoder reconstructs speech from noisy compressed signals. The system latency is adaptable based on the input speech length, achieving a minimum latency of 2 ms, with a lightweight architecture of 25 k parameters, significantly fewer than state-of-the-art systems. The simulation results demonstrate that the proposed system outperforms conventional separate source-channelcoding systems in terms of speech quality and intelligibility, particularly in low-latency and noisy channel conditions. It also shows robustness in fixed configured scenarios, though higher latency conditions and better channel environments favor traditional coding systems.
We investigate joint source channel coding (JSCC) for wireless image transmission over multipath fading channels. Inspired by recent works on deep learning based JSCC and model-based learning methods, we combine an au...
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We investigate joint source channel coding (JSCC) for wireless image transmission over multipath fading channels. Inspired by recent works on deep learning based JSCC and model-based learning methods, we combine an autoencoder with orthogonal frequency division multiplexing (OFDM) to cope with multipath fading. The proposed encoder and decoder use convolutional neural networks (CNNs) and directly map the source images to complex-valued baseband samples for OFDM transmission. The multipath channel and OFDM are represented by non-trainable (deterministic) but differentiable layers so that the system can be trained end-to-end. Furthermore, our JSCC decoder further incorporates explicit channel estimation, equalization, and additional subnets to enhance the performance. The proposed method exhibits 2.5 - 4 dB SNR gain for the equivalent image quality compared to conventional schemes that employ state-of-the-art but separate source and channelcoding such as Better Portable Graphics (BPG) and Low-Density Parity-Check (LDPC) schemes. The performance further improves when the system incorporates the channel state information (CSI) feedback. The proposed scheme is robust against OFDM signal clipping and parameter mismatch for the channel model used in training and evaluation.
A CM2-S3 transmission link in wireless body area networks (WBAN) is implemented with a joint source channel coding (JSCC) system based on double protograph low-density parity-check (DP-LDPC) code pairs with high relia...
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A CM2-S3 transmission link in wireless body area networks (WBAN) is implemented with a joint source channel coding (JSCC) system based on double protograph low-density parity-check (DP-LDPC) code pairs with high reliability and low power consumption. For the practicality and suitability considerations, the M-ary differential chaos shift keying scheme is introduced to modulate the CM2-S3 channel with better performance than the standard modulations in WBAN. Due to the non-Gaussian-like distribution of the CM2-S3 channel model, the initial joint protograph extrinsic information transfer (JPEXIT) algorithm does not work as a coding analysis tool. To solve this problem, a numerical Gaussian approximation is employed to modify the iterative mutual information function of the JPEXIT for achieving more precise output. Then, a joint code rate compatible (JCRC) method, designed with compatibility in resisting variable channel variances and adapting different source statistics, is proposed for optimizing the DP-LDPC code pairs. Based on the JCRC method, the code searching algorithm is simplified with lower complexity because of the reduced cycle-index. The simulation results show that optimized code pairs have better bit-error ratio performances compared to existing codes, which provides a strong technical support to promote high-quality data transmission in eHealthcare on the physical layer.
Recently, deep joint source channel coding (DJSCC) techniques have been extensively studied and have shown significant performance with limited bandwidth and low signal to noise ratio. Most DJSCC work considers discre...
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We present a deep learning based joint source channel coding (JSCC) scheme for wireless image transmission over multipath fading channels with non-linear signal clipping. The proposed encoder and decoder use convoluti...
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ISBN:
(纸本)9781728171227
We present a deep learning based joint source channel coding (JSCC) scheme for wireless image transmission over multipath fading channels with non-linear signal clipping. The proposed encoder and decoder use convolutional neural networks (CNN) and directly map the source images to complex-valued baseband samples for orthogonal frequency division multiplexing (OFDM) transmission. The proposed model-driven machine learning approach eliminates the need for separate source and channelcoding while integrating an OFDM datapath to cope with multipath fading channels. The end-to-end JSCC communication system combines trainable CNN layers with non-trainable but differentiable layers representing the multipath channel model and OFDM signal processing blocks. Our results show that injecting domain expert knowledge by incorporating OFDM baseband processing blocks into the machine learning framework significantly enhances the overall performance compared to an unstructured CNN. Our method outperforms conventional schemes that employ state-of-the-art but separate source and channelcoding such as BPG and LDPC with OFDM. Moreover, our method is shown to be robust against non-linear signal clipping in OFDM for various channel conditions that do not match the model parameter used during the training.
Reconfigurable intelligent surface (RIS) is a programmable metasurface composed of sub-wavelength meta-atoms and a controller. Recent research works have shown that multi-layer RISs can form over-the-air neural networ...
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ISBN:
(纸本)9798350310900
Reconfigurable intelligent surface (RIS) is a programmable metasurface composed of sub-wavelength meta-atoms and a controller. Recent research works have shown that multi-layer RISs can form over-the-air neural networks. This enables support for a variety of tasks, including image recognition, mobile communication coding-decoding, and real-time multi-beam focusing. In this work, we present a RISs-based over-the-air joint source channel coding (JSCC) scheme, named as AirJSCC, where multi-layer RISs are used to realize wave-based complex-valued neural networks (CVNNs). AirJSCC transforms the computation process inherited in JSCC into the transmission of wireless signals through RISs, and thus many benefits, such as light-speed computation and high parallel processing capability, can be achieved. To the best of our knowledge, this is the first deep JSCC scheme that incorporates RISs and CVNNs. Simulation results demonstrate that AirJSCC achieves better image reconstruction performance compared with the baseline scheme, even under low signal-to-noise ratio (SNR) and limited bandwidth, and exhibits robustness against varying channel conditions.
Recent research on joint source channel coding (JSCC) for wireless image communications has achieved great success owing to the employment of deep learning (DL). However, most of the current DL-based JSCC methods impr...
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Benefiting from the advantage of jointsource-channelcoding in the finite block length regime and the advancement of AI technologies, deep joint source channel coding (DJSCC) has been extensively investigated for var...
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ISBN:
(纸本)9781665454681
Benefiting from the advantage of jointsource-channelcoding in the finite block length regime and the advancement of AI technologies, deep joint source channel coding (DJSCC) has been extensively investigated for various sources (e.g., text source, image source, and video source) and achieved remarkable performance in limited bandwidth and low signal-to-noise ratios (SNRs). However, the performance gain brought by DJSCC methods is all observed via simulations in literature, where synchronization, channel estimation and the power amplifier are assumed to be perfect. In this paper, we design a software-defined radio (SDR) based platform to validate the DJSCC method for image transmission in real wireless scenarios.
In this paper, we address the stability problem of a noiseless linear time invariant control system over parallel Gaussian channels with feedback. It is shown that the eigenvalues-rate condition which has been proved ...
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ISBN:
(纸本)9781728105840
In this paper, we address the stability problem of a noiseless linear time invariant control system over parallel Gaussian channels with feedback. It is shown that the eigenvalues-rate condition which has been proved as a necessary condition, is also sufficient for stability over parallel Gaussian channels. In fact, it is proved that for stabilizing a control system over the parallel Gaussian channels, it suffices that the Shannon channel capacity obtained by the water filling technique is greater than the sum of the logarithm of the unstable eigenvalues magnitude. In order to prove this sufficient condition, we propose a new nonlinear joint source channel coding for parallel Gaussian channels by which the initial state is transmitted through communication steps. This coding scheme with a linear control policy results in the stability of the system under the eigenvalues-rate condition. Hence, the proposed encoder, decoder and controller are efficient for this problem.
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