Related papers: D$^2$-JSCC: Digital Deep Joint Source-channel Codi…
We consider image transmission via deep joint source-channel coding (DeepJSCC) over multi-hop additive white Gaussian noise (AWGN) channels by training a DeepJSCC encoder-decoder pair with a pre-trained deep hash distillation (DHD) module…
Semantic communication (SemComm) has emerged as a new communication paradigm. To enhance efficiency, multiple-input-multiple-output (MIMO) technology has been further integrated into SemComm systems. However, existing MIMO SemComm systems…
Semantic communication (SemCom) improves transmission efficiency by focusing on task-relevant information. However, transmitting semantic-rich data over insecure channels introduces privacy risks. This paper proposes a novel SemCom…
Recent developments in Deep learning based Joint Source-Channel Coding (DeepJSCC) have demonstrated impressive capabilities within wireless semantic communications system. However, existing DeepJSCC methodologies exhibit limited…
Recently, deep learned enabled end-to-end (E2E) communication systems have been developed to merge all physical layer blocks in the traditional communication systems, which make joint transceiver optimization possible. Powered by deep…
Deep joint source-channel coding (DeepJSCC) has shown promise in wireless transmission of text, speech, and images within the realm of semantic communication. However, wireless video transmission presents greater challenges due to the…
Semantic Communication (SemCom) systems, empowered by deep learning (DL), represent a paradigm shift in data transmission. These systems prioritize the significance of content over sheer data volume. However, existing SemCom designs face…
We propose joint transmission-recognition schemes for efficient inference at the wireless edge. Motivated by the surveillance applications with wireless cameras, we consider the person classification task over a wireless channel carried out…
Semantic communication (SemCom) has emerged as a promising paradigm for achieving unprecedented communication efficiency in sixth-generation (6G) networks by leveraging artificial intelligence (AI) to extract and transmit the underlying…
This paper introduces a vision transformer (ViT)-based deep joint source and channel coding (DeepJSCC) scheme for wireless image transmission over multiple-input multiple-output (MIMO) channels, denoted as DeepJSCC-MIMO. We consider…
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…
The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned data transmission schemes have exhibited superior…
Deep learning (DL)-based joint source-channel coding (JSCC) methods have achieved remarkable success in wireless image transmission. However, these methods either focus on conventional distortion metrics that do not necessarily yield high…
We consider the problem of joint source-channel coding for semantic communication from a rateless perspective, the purpose of which is to settle the balance between reliability (distortion/perception) and effectiveness (rate) of…
Deep learning (DL)-based Semantic Communications (SemCom) is becoming critical to maximize overall efficiency of communication networks. Nevertheless, SemCom is sensitive to wireless channel uncertainties, source outliers, and suffer from…
We consider wireless transmission of images in the presence of channel output feedback. From a Shannon theoretic perspective feedback does not improve the asymptotic end-to-end performance, and separate source coding followed by…
Many wireless vision applications, such as autonomous driving, require preservation of global structural information rather than only per-pixel fidelity. However, existing Deep joint source-channel coding (DeepJSCC) schemes mainly optimize…
An adaptive joint source-channel coding (JSCC) scheme is presented for transmitting correlated sources over discrete-memoryless two-way channels subject to distortion constraints. The proposed JSCC scheme makes use of the previously…
At the confluence of 6G, deep learning (DL), and natural language processing (NLP), DL-enabled text semantic communication (SemCom) has emerged as a 6G enabler since it minimizes bandwidth consumption, transmission delay, and power usage.…
As communication systems transition from symbol transmission to conveying meaningful information, sixth-generation (6G) networks emphasize semantic communication. This approach prioritizes high-level semantic information, improving…