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 learning, natural language processing (NLP) has achieved great success in analyzing and understanding a large amount of language texts. Inspired by research results in both areas, we aim to provide a new view on communication systems from the semantic level. Particularly, we propose a deep learning based semantic communication system, named DeepSC, for text transmission. Based on the Transformer, the DeepSC aims at maximizing the system capacity and minimizing the semantic errors by recovering the meaning of sentences, rather than bit-or symbol-errors in traditional communications. Moreover, transfer learning is used to ensure the DeepSC applicable to different communication environments and to accelerate the model training process. To justify the performance of semantic communications accurately, we also initialize a new metric, named sentence similarity. Compared with the traditional communication system without considering semantic information exchange, the proposed DeepSC is more robust to channel variation and is able to achieve better performance, especially in the low signal-to-noise (SNR) regime, as demonstrated by the extensive simulation results.

The rapid development of deep learning (DL) and widespread applications of Internet-of-Things (IoT) have made the devices smarter than before, and enabled them to perform more intelligent tasks. However, it is challenging for any IoT device to train and run DL models independently due to its limited computing capability. In this paper, we consider an IoT network where the cloud/edge platform performs the DL based semantic communication (DeepSC) model training and updating while IoT devices perform data collection and transmission based on the trained model. To make it affordable for IoT devices, we propose a lite distributed semantic communication system based on DL, named L-DeepSC, for text transmission with low complexity, where the data transmission from the IoT devices to the cloud/edge works at the semantic level to improve transmission efficiency. Particularly, by pruning the model redundancy and lowering the weight resolution, the L-DeepSC becomes affordable for IoT devices and the bandwidth required for model weight transmission between IoT devices and the cloud/edge is reduced significantly. Through analyzing the effects of fading channels in forward-propagation and backpropagation during the training of L-DeepSC, we develop a channel state information (CSI) aided training processing to decrease the effects of fading channels on transmission. Meanwhile, we tailor the semantic constellation to make it implementable on capacity-limited IoT devices. Simulation demonstrates that the proposed L-DeepSC achieves competitive performance compared with traditional methods, especially in the low signal-to-noise (SNR) region. In particular, while it can reach as large as 20x compression ratio without performance degradation.

Semantic communications focus on the transmission of semantic features. In this letter, we consider a task-oriented multi-user semantic communication system for multimodal data transmission. Particularly, partial users transmit images while the others transmit texts to inquiry the information about the images. To exploit the correlation among the multimodal data from multiple users, we propose a deep neural network enabled semantic communication system, named MU-DeepSC, to execute the visual question answering (VQA) task as an example. Specifically, the transceiver for MU-DeepSC is designed and optimized jointly to capture the features from the correlated multimodal data for task-oriented transmission. Simulation results demonstrate that the proposed MU-DeepSC is more robust to channel variations than the traditional communication systems, especially in the low signal-to-noise (SNR) regime.

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In this paper, a joint multi-user constellation is proposed for energy detection-based non-coherent massive multiple-input multiple-output system. This is motivated by the simple design and high energy efficiency it entails for both the transmitter and receiver.First, the orthogonal codes is employed to suppress the multi-user interference. However, this comes at the price of consuming more communications resources. In this study, the key to reduce code redundancy is the design of a joint constellation since it makes energy detection applicable when multiple users employ the same orthogonal codes. Although it is unsolvable initially, our analysis indicates that through minimizing the symbol-error rate (SER), the joint constellation design becomes feasible. Concretely, two analytical expressions of SER based on Gamma and Gaussian distributions are derived. Via minimizing the error probability, an important result that the joint constellation should satisfy is obtained. Accordingly, an isometric constellation design is proposed to find constellations that enable non-coherent reception with multiple users, and achieve the minimum SER simultaneously. In addition, decoding regions of symbol decision are optimized to further improve the error performance.

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While semantic communications have shown the potential in the case of single-modal single-users, its applications to the multi-user scenario remain limited. In this paper, we investigate deep learning (DL) based multi-user semantic communication systems for transmitting single-modal data and multimodal data, respectively. We will adopt three intelligent tasks, including, image retrieval, machine translation, and visual question answering (VQA) as the transmission goal of semantic communication systems. We will then propose a Transformer based unique framework to unify the structure of transmitters for different tasks. For the single-modal multi-user system, we will propose two Transformer based models, named, DeepSC-IR and DeepSC-MT, to perform image retrieval and machine translation, respectively. In this case, DeepSC-IR is trained to optimize the distance in embedding space between images and DeepSC-MT is trained to minimize the semantic errors by recovering the semantic meaning of sentences. For the multimodal multi-user system, we develop a Transformer enabled model, named, DeepSC-VQA, for the VQA task by extracting text-image information at the transmitters and fusing it at the receiver. In particular, a novel layer-wise Transformer is designed to help fuse multimodal data by adding connection between each of the encoder and decoder layers. Numerical results will show that the proposed models are superior to traditional communications in terms of the robustness to channels, computational complexity, transmission delay, and the task-execution performance at various task-specific metrics.

Semantic communications focus on the successful transmission of information relevant to the transmission task. In this paper, we investigate multi-users transmission for multimodal data in a task semantic communication system. We take the vision-answering as the semantic transmission task, in which part of the users transmit images and the other users transmit text to inquiry the information about the images. The receiver will provide answers based on the image and text from multiple users in the considered system. To exploit the correlation between the multimodal data from multiple users, we proposed a deep neural network enabled multi-user semantic communication system, named MU-DeepSC, for the visual question answering (VQA) task, in which the answer is highly dependent on the related image and text from the multiple users. Particularly, based on the memory, attention, and composition (MAC) neural network, we jointly design the transceiver and merge the MAC network to capture the features from the correlated multimodal data for serving the transmission task. The MU-DeepSC extracts the semantic information of image and text from different users and then generates the corresponding answers. Simulation results validate the feasibility of the proposed MU-DeepSC, which is more robust to various channel conditions than the traditional communication systems, especially in the low signal-to-noise (SNR) regime.