The extremely large-scale array is considered to be one of the key technologies for 6G, which can significantly improve spectral efficiency. However, the extremely-large number of antennas results in a larger range of the near field (e.g., hundreds of meters), leading to the electromagnetic wave propagation modeling changing from plane wave to spherical wave. This makes conventional channel estimation methods suffer from inevitable performance degradation due to the additional distance information in the spherical wavefront. To address this problem, this paper proposes a deep reinforcement learning based near-field channel estimation, in which the multi-agent deep deterministic policy gradient (MADDPG) is employed. Specifically, the near-field channel estimation task is first formulated as a compressed sensing problem by using a sparse spatial grid-based dictionary. Then, the Actor Critic (AC) network based MADDPG algorithm is employed to jointly optimize the angular and distance information by an approximate global search. In addition, an advantage Actor Critic network-based channel estimation algorithm is proposed, which improves both the stability and efficiency of the AC-based algorithm. Finally, the numerical results show that the proposed algorithms outperform the benchmarks in terms of normalized mean square error.

Digital and analog semantic communications (SemCom) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom. In order to overcome these challenges, we propose a novel SemCom framework and a corresponding system called HDA-DeepSC, which leverages a hybrid digital-analog approach for multimedia transmission. This is achieved through the introduction of analog-digital allocation and fusion modules. To strike a balance between data rate and distortion, we design new loss functions that take into account long-distance dependencies in the semantic distortion constraint, essential information recovery in the channel distortion constraint, and optimal bit stream generation in the rate constraint. Additionally, we propose denoising diffusion-based signal detection techniques, which involve carefully designed variance schedules and sampling algorithms to refine transmitted signals. Through extensive numerical experiments, we will demonstrate that HDA-DeepSC exhibits robustness to channel variations and is capable of supporting various communication scenarios. Our proposed framework outperforms existing benchmarks in terms of peak signal-to-noise ratio and multi-scale structural similarity, showcasing its superiority in semantic communication quality.

Semantic communications have shown great potential in reducing the transmitted data amount through the powerful capability to extract and transmit essential semantic information. Although existing works have achieved certain transmission efficiency, challenges such as efficiently interpretable semantic extraction, and dynamically adaptive channel coding have not been fully explored. In this paper, we tackle these challenges by proposing a task-oriented scene graph-based semantic communication system with adaptive channel coding, named GRACE, to perform image retrieval task. To enhance the interpretability of semantic communications and reduce semantic redundancy, we introduce a scene graph semantic encoder. This encoder fully exploits informative scene graph semantics, effectively extracting scene graphs and performing further semantic coding. Additionally, to handle variable channel conditions in real-world scenarios, we develop a semantic-aware adaptive channel coding to adapt to channel conditions and reduce the communication resources. At the receiver, the image retrieval task is accomplished based on the recovered scene graph semantics. The experimental results demonstrate the superiority of the proposed system compared to other communication systems in terms of task-execution performance, robustness against channel variations, transmission efficiency, and computational complexity.

Semantic communications have been utilized to execute numerous intelligent tasks by transmitting task-related semantic information instead of bits. In this article, we propose a semantic-aware speech-to-text transmission system for the single-user multiple-input multiple-output (MIMO) and multi-user MIMO communication scenarios, named SAC-ST. Particularly, a semantic communication system to serve the speech-to-text task at the receiver is first designed, which compresses the semantic information and generates the low-dimensional semantic features by leveraging the transformer module. In addition, a novel semantic-aware network is proposed to facilitate transmission with high semantic fidelity by identifying the critical semantic information and guaranteeing its accurate recovery. Furthermore, we extend the SAC-ST with a neural network-enabled channel estimation network to mitigate the dependence on accurate channel state information and validate the feasibility of SAC-ST in practical communication environments. Simulation results will show that the proposed SAC-ST outperforms the communication framework without the semantic-aware network for speech-to-text transmission over the MIMO channels in terms of the speech-to-text metrics, especially in the low signal-to-noise regime. Moreover, the SAC-ST with the developed channel estimation network is comparable to the SAC-ST with perfect channel state information.

Semantic communications have been regarded as a promising solution for the next generation communication systems to alleviate the spectral resource shortage and the network congestion. Existing image semantic communication systems extract and transmit global semantics, which can cope with the downstream data reconstruction or intelligent tasks. However, the semantics involved in these methods are still severely redundant and uninterpretable. In this work, we propose a novel task-oriented semantic communication framework based on scene graph, named DeepSC-SG. Specifically, we first devise a scene graph based semantic encoder, which extracts the explainable scene graph semantics from the input images and encodes the semantics into informative graph embeddings. Then we design a joint source-channel (JSC) codec to combat physical channel impairment. After receiving the semantics, a semantic decoder is devised to achieve the downstream image retrieval task by computing scene graph similarities. Simulation results demonstrate that the proposed DeepSC-SG is fairly robust to the channel variations compared to the traditional communication systems, which has great potential in realizing downstream intelligent tasks like image retrieval with significantly reduced size of transmitted data.

While semantic communications succeed in effectively transmitting due to the strong capability to extract the essential semantic information, it is still far from intelligent communications. In this paper, we introduce an essential component, memory, into semantic communications to mimic human communications. Particularly, we propose a deep learning (DL) based semantic communication system with memory, named Mem-DeepSC, by considering the scenario question answer as the task at the receiver. We exploit universal Transformer based transceiver to extract the semantic information and introduce the memory module to enhance the semantic decoding capability at the receiver. Moreover, we derive the semantic channel capacity and propose a consecutive dynamic transmission method to minimize the transmission latency. Numerical results show that Mem-DeepSC is superior to benchmarks in terms of answer accuracy and the number of transmitted symbols.

While semantic communication succeeds in efficiently transmitting due to the strong capability to extract the essential semantic information, it is still far from the intelligent or human-like communications. In this paper, we introduce an essential component, memory, into semantic communications to mimic human communications. Particularly, we investigate a deep learning (DL) based semantic communication system with memory, named Mem-DeepSC, by considering the scenario question answer task. We exploit the universal Transformer based transceiver to extract the semantic information and introduce the memory module to process the context information. Moreover, we derive the relationship between the length of semantic signal and the channel noise to validate the possibility of dynamic transmission. Specially, we propose two dynamic transmission methods to enhance the transmission reliability as well as to reduce the communication overheads by masking some unessential elements, which are recognized through training the model with mutual information. Numerical results show that the proposed Mem-DeepSC is superior to benchmarks in terms of answer accuracy and transmission efficiency, i.e., number of transmitted symbols.

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 adopt three intelligent tasks, including, image retrieval, machine translation, and visual question answering (VQA) as the transmission goal of semantic communication systems. We propose a Transformer based framework to unify the structure of transmitters for different tasks. For the single-modal multi-user system, we 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 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.

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 back-propagation 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 $40\times $ compression ratio without performance degradation.

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 makes joint transceiver optimization possible. Powered by deep learning, natural language processing (NLP) has achieved great success in analyzing and understanding large amounts 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. Compared with the traditional communication system without considering semantic information exchange, the proposed DeepSC is more robust to channel variation and can achieve better performance, especially in the low signal-to-noise ratio (SNR) regime, as demonstrated by the extensive simulation results.

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 reduce SER simultaneously. In addition, decoding regions of symbol decision are optimized to further improve the error performance. In the end, numerical simulations are carried out to highlight the effectiveness of our proposed scheme.

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. Although it is unsolvable initially, our analysis indicates 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 must 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. Finally, numerical simulations are carried out to highlight the effectiveness of our proposed scheme.