Semantic communication has emerged as a promising paradigm exhibiting improved robustness compared to traditional approaches under low SNR conditions. Precise synchronization is imperative for accurate semantic communication. However, existing synchronization techniques face challenges reliably achieving synchronization at low SNRs, limiting semantic communication development. To improve synchronization performance, especially under low SNR scenarios, this work proposes an image-based semantic synchronization method (SeSy) leveraging inherent image correlations. SeSy is applicable to both semantic and traditional communication systems. Theoretical analysis establishes bounds on the miss detected ratio (MDR) for SeSy. Experimental results demonstrate that SeSy achieves lower MDR and root mean square error (RMSE) compared to traditional methods across various SNR levels, especially at low SNRs.

Since the current error detection and correction of semantic information mainly rely on the detection of the final semantic recovery performance to identify the wrong semantic features, this method reduces the efficiency of semantic communication. To address this issue, this paper proposes a semantic communication system based on hybrid automatic repeat request for semantic packets (SPHARQ). Where a novel semantic check code (SCC) is designed as an effective proxy to enable immediate detection of semantic distortion, and the semantic features to be transmitted are selected based on key factors such as feature importance. Based on the SCC, a dynamic retransmission control criterion jointly driven by semantic and physical metrics is established, enabling semantic-aware retransmission. Building upon this criterion, a cooperative retransmission scheme for semantic packets is designed, further enhancing their transmission quality and efficiency. Then, theoretical analysis is conducted on the average number of transmissions and the throughput of semantic packets in this system, and corresponding closed-form expressions are provided. Simulations validate the theoretical analysis, showing that at low signal-to-noise ratio (SNR) the proposed system achieves gains of up to 0.25 in multi-scale structural similarity (MS-SSIM), 4 dB in peak signal-to-noise ratio (PSNR), and enhanced intersection over union (IoU) across multiple segmentation categories over the latest semantic HARQ scheme, with lower overhead.

Semantic communication has emerged as a promising paradigm, attracting significant research interest due to its potential to enhance communication efficiency. This paper extends semantic communication to doubly selective channel scenarios, addressing the challenges of time-varying and frequency-selective fading. Inspired by the noise resilience of semantic transmission and the principle of semantic inequality, a superimposed pilot design method is proposed for semantic transmission over doubly selective channels. An optimization problem is theoretically analyzed to determine the optimal power allocation ratio between pilot and semantic data symbols under channel estimation errors in doubly selective channels, without considering semantic importance. This analysis provides insights into pilot power and coherence time, serving as a benchmark for semantic performance optimization. Additionally, considering semantic symbol inequality, a suite of deep learning-based models including semantic interleaving, power adaptation, and channel compensation are designed to enable unequal power allocation that prioritizes important semantic symbols, maximizing semantic performance. Numerical results validate the effectiveness of this method, demonstrating substantial improvements in transmission quality over traditional orthogonal pilot schemes in doubly selective channels, particularly under low SNR conditions. Execution time analysis further highlights the computational efficiency of this approach, achieving a favorable balance between performance and complexity.

Video semantic communication, as a cutting-edge field in the convergence of communication and artificial intelligence, is dedicated to improving the quality of video communication through the efficient transmission of semantic features. However, existing semantic systems face problems such as the complexity of shared feature extraction and cross-scene feature conflicts during multi-scene switching. To address this challenge, this paper proposes a video semantic transmission technique based on dynamic Group of Pictures (GOP) segmentation. Specifically, the performance advantages of dynamic GOP under multiple wireless channels are verified by designing a multiscale fusion transition detection algorithm and a dynamic GOP division strategy. The experimental results show that the proposed method can adapt to different content scenarios, significantly optimize the semantic feature extraction and video reconstruction process, and provide reliable technical support for video semantic transmission over complex communication links.

The rapid growth of wireless communication technologies has made semantic communication an increasingly important approach for efficient data transmission. In this paper, a novel deep joint source-channel coding (DeepJSCC) framework based on Feedback-Driven Codebook Optimization (FDCO) for wireless image transmission is proposed. The framework dynamically optimizes the codebook based on channel feedback to improve image reconstruction performance under varying channel conditions. Specifically, a FDCO network is introduced to adjust the balance between common and individual information in the codebook based on the input signal-to-noise ratio (SNR). In addition, the residual between the original and quantized images is encoded to obtain semantic details, which are transmitted to reduce semantic quantization loss. Experimental results demonstrate that the proposed framework improves image quality and compression efficiency, especially under low SNR, and validates FDCO's dynamic adjustment of the codebook's information balance, leading to enhanced performance.

This article introduces a novel method for transmitting video data over noisy wireless channels with high efficiency and controllability. The method derivates from model division multiple access (MDMA) to extract common semantic features from video frames. It also uses deep joint source-channel coding (JSCC) as the main framework to establish communication links and deal with channel noise. An entropy-based semantic importance coding scheme is developed to adjust the data amount accurately and explicitly. We name our method as model division video semantic communication (MDVSC). The main steps of our approach are as follows: first, video frames are transformed into a latent space to reduce computational complexity and redistribute data. Then, common features and individual features are extracted, and semantic importance coding is applied to further eliminate redundant semantic information under the communication bandwidth constraint. We evaluate our method on standard video test sequences and compare it with traditional wireless video coding methods. The results show that MDVSC generally surpasses the conventional methods in terms of quality metrics and has the capability to control code length precisely. Moreover, additional experiments and ablation studies are conducted to demonstrate its potential for various tasks.

With the rapid development of Internet of Things (IoT) technology, more sensors are required to operate in complex channel scenarios and under limited communication resources. Semantic communication, as an emerging paradigm, extracts, transmits, and reconstructs information at the semantic level, offering advantages, such as high compression rates and strong noise resistance. These features are expected to find widespread application across various IoT scenarios. However, widely used image similarity evaluation metrics like peak signal-to-noise ratio and multiscale structural similarity index primarily focus on pixel or structural features, making it challenging to accurately measure the loss of semantic-level information during transmission. This limitation poses challenges for the performance evaluation of visual semantic communication systems and restricts the emergence of more novel and efficient systems. To address this issue, we propose a new semantic evaluation metric-semantic similarity score (SeSS). This metric is based on Scene Graph Generation and graph matching techniques, transforming image similarity scores into graph matching scores. By manually annotating thousands of image pairs, we fine-tuned the hyperparameters within SeSS to align it more closely with human semantic perception. The performance of SeSS has been tested across various image datasets and specific IoT visual tasks. Experimental results demonstrate the effectiveness of SeSS in measuring differences in semantic-level information between images, making it a valuable tool for evaluating visual semantic communication systems. This development is expected to encourage the emergence of more robust systems suited for diverse IoT scenarios. The code of SeSS is openly available onhttps://github.com/FSR3340/Semantic_Similarty_ScoreGitHub.

Compatibility between semantic communication and traditional mobile communication systems remains a significant challenge. Therefore, we propose a cross-layer encrypted semantic communication (CLESC) framework for panoramic video transmission, incorporating feature extraction, encoding, encryption, cyclic redundancy check (CRC), and retransmission processes to achieve compatibility between semantic communication and traditional communication systems. Additionally, we propose an adaptive cross-layer transmission mechanism that dynamically adjusts CRC, channel coding, and retransmission schemes based on the importance of semantic information. This mechanism ensures that important information is prioritized under poor transmission conditions. To verify the aforementioned framework, we design an end-to-end adaptive panoramic video semantic transmission (APVST) network that leverages a deep joint source-channel coding (JSCC) structure and attention mechanism, integrated with a latitude adaptive module that facilitates adaptive semantic feature extraction and variable-length encoding of panoramic videos. Simulation results demonstrate that the proposed CLESC framework effectively achieves compatibility and adaptability between semantic and traditional communication systems, significantly enhancing channel robustness. Compared to traditional and artificial intelligence (AI)-based video source coding transmission schemes, our proposed CLESC achieves superior transmission performance under low signal-to-noise ratio (SNR) conditions.

Semantic communication, a promising paradigm for next-generation wireless systems, optimizes the representation of semantic information and its resilience to channel effects, outperforming traditional systems in low signal-to-noise ratio (SNR) environments. However, most existing frameworks focus on Single-Input Single-Output (SISO) channels which limits their use in multi-antenna systems. To address this gap, we propose Semantic Importance-Aware Communication (SIAC-MIMO), a system designed for Multiple-Input Multiple-Output (MIMO) fading channels. SIAC-MIMO integrates semantic symbol inequality with advanced channel-aware techniques. SIAC-MIMO prioritizes critical semantic symbols, adapts transmission to MIMO channel states, and employs Orthogonal Model Division Multiple Access (O-MDMA) for multi-user broadcasting to mitigate interference while enhancing scalability. A bilateral progressive training algorithm is introduced to align semantic allocation with channel eigenmodes. To evaluate the effectiveness of this system, a theoretical framework is developed to analyze semantic performance metrics, such as semantic information distortion and semantic outage probability. The experiments across 2W2 to 64W64 MIMO setups demonstrate SIAC-MIMO’s superiority, achieving 5–18% improvements in Mean Structural Similarity Index Measure (MS-SSIM) at low SNR in single-user scenarios and 16–23% improvements in multi-user MIMO setups compared to traditional source-channel separation schemes, highlighting the system’s potential for efficient and robust communication.

As a novel communication paradigm, semantic communication (SemCom) can greatly improve communication efficiency, which has aroused extensive research by scholars worldwide. As one of the important aspects of digital communication nowadays, how to combine channel estimation with SemCom is an important research direction. In this article, based on orthogonal frequency-division multiplexing (OFDM) communication architecture, the semantic importance-aware reordering-enhanced SemCom system (SIARE-SC) is proposed, which utilizes the inequality of semantic symbols combined with channel estimation in OFDM systems to reduce the distortion caused by channel estimation interpolation error (CEIE) and further improve the signal recovery quality. To enhance the generalizability of the system, we extend the verification of the effectiveness of SIARE-SC in various scenarios with different sources, channels, and pilot patterns. Furthermore, the importance reordering method proposed in the SIARE-SC has good applicability and effectiveness, which can be used to be compatible with other SemCom systems and has a significant suppression effect on the peak-to-average power ratio (PAPR). Meanwhile, CEIE has been considered for the first time to be included in the analysis of SemCom distortion, and mathematically derive the performance expressions of SIARE-SC under different channel and pilot pattern scenarios from three perspectives, namely, channel bandwidth ratio (CBR), signal-to-noise ratio (SNR), and CEIE, to obtain the corresponding bound of performance. The proposed SIARE-SC is shown to significantly improve semantic performance in various scenarios by conducting a large number of experimental tests.

Driven by the severe self-interference (SI) in in-band full-duplex (IBFD) technologies, which creates extremely harsh communication environments, addressing this challenge has become a critical research focus. Semantic communication technologies, meanwhile, exhibit significant advantages in constrained environments by enabling efficient information transmission with reduced data volume and optimized bandwidth utilization. This article proposes an in-band full-duplex semantic communication (IBFD-SC), which combines IBFD with semantic communication to save transmission volume and improve spectral efficiency. The system incorporates a semantic importance mechanism, which is merged with radio frequency (RF) communication links. A semantic importance mapping module is introduced to map semantic symbols to baseband signals, considering both channel conditions and the significance of semantic symbols. Additionally, a nonlinear interference cancellation method is designed to eliminate SI, ensuring the integrity and reliability of key semantic information during communication. Experimental results demonstrate that the integration of semantic importance effectively mitigates interference and improves communication performance, particularly under low signal to interference plus noise ratio (SINR) conditions.

With the rapid development of Internet of Things (IoT) technology, an increasing number of resource-constrained devices operate in dynamic and heterogeneous network environments, posing challenges for efficient image transmission. Multi-user semantic communication (SC) enables reduced bandwidth consumption and enhanced noise resilience by understanding the intrinsic meaning of information and sharing common semantic features across devices, offering great potential for widespread applications in various IoT scenarios. However, current multi-users SC approaches for image transmission lack adaptability and fail to consider both content and style features, leading to degraded image reconstruction quality. Moreover, semantic redundancy among devices remains underutilized, limiting bandwidth efficiency in IoT networks. To address these limitations, in this paper, a novel multi-user content-style adaptive semantic communication system for image transmission in IoT scenarios is proposed. Specifically, a dual-branch semantic information extraction and adaptive recovery scheme is first established, which simultaneously captures and adaptively fuses semantic content and style features to improve reconstruction quality. Secondly, an adaptive common information extraction and enhanced coding module is introduced for resource-limited IoT devices, which dynamically adjusts the transmission rate based on varying channel conditions and the computational capabilities of different users, further optimizing communication performance. Finally, experimental results show that the proposed method improves peak signal-to-noise (PSNR) by at least 10% under poor SNR conditions for multi-users semantic communication, compared to baseline methods.

The current state of semantic communication research is primarily based on analog modulation, whereas digital communication is more prevalent in practical applications. However, traditional digital modulation constellation mapping designs are not suitable for semantic communication, as they are primarily focused on minimizing the bit error rate of the transmitted data rather than the change in the numerical value of the semantic information. To address this issue, a semantic digital modulation constellation mapping (sDMCM) scheme based on pulse amplitude modulation (PAM)/quadrature amplitude modulation (QAM) is proposed, that considers the internal correlation of the semantic information. In addition, this article proposes using the mean-squared error (MSE) of the semantic information between the transmitter and receiver as a performance metric for semantic communication. This article also provides the theoretical MSE performance of the proposed sDMCM through formula derivation. Finally, the simulation results match the theoretical results, demonstrating the rationality of the theoretical derivation. Comparing the proposed sDMCM scheme with traditional mapping schemes, such as Gray, Pseudo-Gray (Pe-Gray), and structural quadrant (SQ) constellations in an image semantic communication system under the additive white Gaussian noise (AWGN) channel, the restored image can tolerate an SNR drop of about 3 dB while maintaining the same multiscale structure similarity (MS-SSIM) performance. In addition, sDMCM is applied in an industrial Internet of Things semantic communication system to validate its role in IoT applications.

Semantic communication (SemCom) is regarded as a promising and revolutionary technology in 6G, aiming to transcend the constraints of “Shannon’s trap” by filtering out redundant information and extracting the core of effective data. Compared to traditional communication paradigms, SemCom offers several notable advantages, such as reducing the burden on data transmission, enhancing network management efficiency, and optimizing resource allocation. Numerous researchers have extensively explored SemCom from various perspectives, including network architecture, theoretical analysis, potential technologies, and future applications. However, as SemCom continues to evolve, a multitude of security and privacy concerns have arisen, posing threats to the confidentiality, integrity, and availability of SemCom systems. This paper presents a comprehensive survey of the technologies that can be utilized to secure SemCom. Firstly, we elaborate on the entire life cycle of SemCom, which includes the model training, model transfer, and semantic information transmission phases. Then, we identify the security and privacy issues that emerge during these three stages. Furthermore, we summarize the techniques available to mitigate these security and privacy threats, including data cleaning, robust learning, defensive strategies against backdoor attacks, adversarial training, differential privacy, cryptography, blockchain technology, model compression, and physical-layer security. Lastly, this paper outlines future research directions to guide researchers in related fields.

In this paper, we propose a novel semantic digital analog converter (sDAC) for the compatibility between semantic and digital communications. Most of the current semantic communication systems rely primarily on analog modulation, limiting their integration with digital communication systems, which are more common in practice. In fact, traditional quantization methods are unsuitable for semantic communication because they do not account for semantic information within symbols. These factors block the wide application of the semantic communication. To address these challenges, sDAC is proposed. It is a simple yet efficient and generative module used to realize digital and analog bi-directional conversion. The entire process is independent of any specific semantic model, modulation methods, or channel conditions. In the experiment section, the performance of sDAC is tested across different semantic models, semantic tasks, modulation methods, channel conditions and quantization orders. Test results show that the proposed sDAC has great generative properties and channel robustness.

Significant progress has been made in researching video semantic communication technology and adaptive bitrate (ABR) algorithms. However, wireless network fluctuations challenge video semantic communication systems without ABR algorithms to achieve a satisfactory balance between high semantic recovery accuracy and efficient bandwidth utilization. This paper proposes an adaptive bitrate video semantic increment transmission system based on buffer and semantic importance to address this issue. Firstly, a buffer-based video semantic increment transmission system is designed to dynamically adjust the amount of video semantic data transmitted by the transmitter based on network fluctuations. Then, a novel Deep Learning and Reinforcement Learning based ABR algorithm (DR-ABR) is developed to determine the optimal video incremental ratio under the current network conditions. Furthermore, a semantic feature compression technology based on semantic importance is proposed to compress the video data according to the abovementioned ratio. Experimental results demonstrate that the proposed method outperforms traditional approaches in terms of video semantic transmission performance.

In this paper, we introduce an end-to-end non-orthogonal multiple access (NOMA) framework for the downlink transmission of correlated information sources in the multi-user scenario, in which the data required or transmitted by multiple users share similar content. To enhance the end-to-end transmission performance, we resort to the semantic communication paradigm and build our system based on the deep joint source-channel coding (D-JSCC) scheme. Inspired by Wyner’s common information, an information theoretical concept, the common information (CI) extraction is proposed to capture the correlation between multiple users effectively. By relaxing the constraint of the object function, equivalency can be established between common information extraction and mutual information maximization. Thereby, the Jenson-Shannon divergence (JSD) is adopted in the loss function for learning the common information representation (CIR). In order to categorize the theoretical performance limit of the proposed system, semantic synonymous mapping (SSM) based information theory is applied for analyzing the effect of correlation level and different decoding schemes on the achievable channel capacity. Specifically, the analytical expression of channel capacity under additive white Gaussian noise (AWGN) and Rayleigh channel is derived and verified by Monte-Carlo experiments. By conducting simulations on three different image datasets, it is verified that our proposed scheme can outperform a series of other state-of-the-art (SoTA) multiple access or distributed source coding (DSC) schemes under up to seven user scenarios. Besides, the visualization and ablation study results validate the effectiveness of the common information extraction.

Semantic Communication (SemCom) has opened a new paradigm in the 6G system. However, the performance of SemCom can be severely affected by time-varying path loss, channel noises, and other interference in wireless channels. Therefore, we propose a novel Semantic Prior aided Channel-adaptive Equalizing and De-noising SemCom (SP-EDNSC) framework, where adaptive elimination channel impact is regarded as an inverse problem. This inverse problem is addressed through semantic priors learned from score-based generative models cached in knowledge base. To reduce distortion while enhancing perceptual quality, we further combine autoencoders, adversarial learning and diffusion models to develop a latent diffusion-based (SP-Latent-Diff EDNSC) system within the SP-EDNSC framework. In the semantic space, the joint semantic equalizer and de-noiser module utilizes the proposed latent diffusion posterior sampling method. This method iteratively executes a modified reverse stochastic differential equation to sample clean semantic features, using the time-dependent score function of likelihood and semantic priors. The semantic priors are derived from pre-trained latent diffusion models, while the likelihood is approximated by a multivariate normal distribution. Simulations demonstrate that our scheme achieves superior performance in both distortion metrics like PSNR and SSIM, as well as in perceptual performance (LPIPS).

In order to meet the requirements of 6G communication for environmental adaptability and interference resistance, obtaining accurate Channel State Information (CSI) is of paramount importance. However, traditional communication methods struggle to fulfill these demands, leading to a growing interest in deep learning-based channel estimation solutions among researchers. This paper introduces a solution to the channel estimation problem in OFDM systems, employing a deep learning approach based on the MLP-Mixer block, referred to as CENet. CENet's channel-mixing and token-mixing structures enable better capturing of both temporal and spectral channel characteristics. The proposed channel estimation method consists of two parts: firstly, preliminary channel estimation results are generated using the LS algorithm, and then CENet is employed to further refine these preliminary results. Simulation results demonstrate the superiority of the proposed approach over other deep learning methods. Additionally, this paper extends the method to MIMO scenarios and introduces pruning techniques to reduce redundant parameters in the MLP layers, thereby reducing computational complexity.

Semantic communication, as a new research paradigm, has garnered widespread attention from academia and industry. One of the important aspects is the study of channel estimation, which can further improve the recovery of signals in communication systems. However, most existing studies on semantic communication have only considered the case of perfect channel estimation. In this paper, pilots-assisted channel estimation is considered, and a symbol reordering method named Entropy-Based Importance Reordering (EBIR) is proposed to mitigate the distortions induced by slow fading channels. The method distinguishes important and unimportant semantic sym-bols based on the entropy value obtained from the entropy model. Based on the characteristics of channel estimation in slow fading time-varying channels, important semantic symbols are reassigned to improve signal recovery further. The results show that the effectiveness and universality of EBIR are validated for different sources, channel bandwidth ratios (CBRs) and channel states.

As a new communication paradigm, semantic communication has received widespread attention in communication fields. However, since the decoding of semantic signals relies on contextual knowledge, misalignment between the starting position of the semantic signal and the AI-based semantic decoder would prevent source signal recovery and reconstruction. To achieve more precise semantic communication, this study proposes an image-based semantic synchronization method leveraging intrinsic semantic features of image content. Specifically, a shared synchronized image (SyncImg) is encoded into a synchronization vector header at the transmitter and sent to the receiver. The receiver adopts a sliding window semantic decoder combined with classification and template matching methods to locate the synchronization point. Experimental results demonstrate that compared with traditional methods, the proposed method achieves a lower miss detected ratio (MDR) and root-mean-square error (RMSE) under low signal-to-noise ratios, realizing accurate synchronization of semantic signals across different devices.

With the further study of 6G, the development of Internet of Things (IoT) network with 6G draws more attention. Making the system sustainable and enhancing the performance of the system are the current important research directions. This paper introduces a cooperative communication network based on energy-harvesting (EH) decode-and-forward (DF) relays. To solve the current problem of node self-sustaining capacity and achieve sustainable communication, the relay nodes within this system adopt a harvest-storage-use (HSU) structure, allowing them to extract energy from the surrounding environment through energy buffering. To enhance the communication system's performance, the paper incorporates the opportunistic routing algorithm and the generalized selection combining (GSC) algorithm. Further-more, utilizing a discrete-time continuous-state space Markov chain model (DCSMC), the paper derives a theoretical expression for the energy limiting distribution stored in infinite buffers. Through the utilization of probability distribution and the state transition matrix, the paper provides theoretical expressions for system outage probability and throughput. Simulation verification confirms the theoretical results' robust agreement with the simulated outcomes. At last, there is a maximum improvement of about 10% failure probability lower than the existing model.

Nowadays, the need for high-quality image reconstruction and restoration is more and more urgent. However, most image transmission systems may suffer from image quality degradation or transmission interruption in the face of interference such as channel noise and link fading. To solve this problem, a relay communication network for semantic image transmission based on shared feature extraction and hyperprior entropy compression (HEC) is proposed, where the shared feature extraction technology based on Pearson correlation is proposed to eliminate partial shared feature of extracted semantic latent feature. In addition, the HEC technology is used to resist the effect of channel noise and link fading and carried out respectively at the source node and the relay node. Experimental results demonstrate that compared with other recent research methods, the proposed system has lower transmission overhead and higher semantic image transmission performance. Particularly, under the same conditions, the multi-scale structural similarity (MS-SSIM) of this system is superior to the comparison method by approximately 0.2.

With the development of communication in the post-5G era, the combination of communication and cloud computing becomes closer. In order to promote the further development of cloud-network, this paper will study the performance of Multi-server cloud Database under the Communication quality of quasi-static Rayleigh fading channel with Multiple antennas(MC-MD). The CLIENTS with unlimited customers, a COMMUNICATION SYSTEM subject to quasi-static Rayleigh fading, and CLOUD DATABASE with two-phase locking protocol are the three components of the MC-MD model. Transactions are 1)initiated by the CLIENTS, 2)transmitted to the CLOUD DATABASE through the COMMUNICATION SYSTEM for processing, 3)then returned to the CLIENTS. The indicators of the model is mathematically derived by using queuing theory. These include client’s indicators(average concurrent quantity of the system in steady state(CQ), average transactions stay time of the system in steady state(ST), average queue length of the Waiting Area in steady state(QL), and average transactions wait time of the Waiting Area in steady state(WT)) and server’s indicator(average number of service desks in the busy period at steady state(DN)). Under the appropriate conditions, the results indicate that the theoretical value of service performance is basically consistent with the simulation value. Clearly, the high speed improves the service performance of the system and decreases the service pressure. On the basis of this, the optimization strategy is proposed and the simulation indicators Jitter of transactions sojourn time of the system in steady state(STJ) is added. The results show that the transaction scheduling optimization strategy effectively reduce the delay and its jitter.

We propose a novel task-oriented and semantic-aware heterogeneous networks (TOSA-HetNets) framework for multitype Artificial Intelligence of Things (AIoT) devices with various requirements, where the dense edge servers with different transmission capabilities, computing resources, and power consumption are divided into different layers to provide on-demand collaboration for AIoT devices located in accessible areas. Moreover, we propose a device–edge collaboration intelligent tasks inference scheme between edge servers and AIoT devices in TOSA-HetNets, it includes AIoT devices performing semantic features extraction and uploading the corresponding semantic features to the associated edge servers, multiple layers of edge servers collaborating with AIoT devices to execute the intelligent tasks and transmit the intelligent task results back to AIoT devices. To investigate the performance of TOSA-HetNets in supporting device–edge collaboration intelligent tasks inference, we adopt stochastic geometry to obtain the closed-form expressions of average task success probability, power consumption, and network throughput in the downlink transmission. Furthermore, we define a metric of average achievable task back-transmission energy efficiency (TBT-EE) to measure the information bit of successfully transmitted correct intelligent task results with unit power consumption, which is a function of average task success probability, average network throughput on the unit area, and the total power consumption. Meanwhile, we maximize the average achievable TBT-EE by optimizing the density of edge servers and the average semantic compression ratio. Simulation results verify the correctness of the obtained closed-form expressions and show that the edge servers’ density and average semantic compression ratio have different influences on the performance of TOSA-HetNets.

Multiple access technologies are critical technologies in every communication era. As a promising paradigm for next-generation mobile communication, semantic communication has explored new semantic information space resources. Based on the characteristic that different semantic models cannot understand semantic information generated by other models, we propose the concept of semantic orthogonal signals. Combining the advantages of Deep joint source and channel coding (DeepJSCC), an Orthogonal-Model Division Multiple Access (O-MDMA) technology that can be applied to any semantic model is proposed. The essence of O-MDMA is to migrate the anti-interference capability of DeepJSCC to the multi-user capacity. Compared with Non-Orthgonal Multiple Access (NOMA) and Model Division Multiple Access (MDMA) technologies, O-MDMA has better performance. The O-MDMA can be integrated with NOMA, and experimental results show that the combined technique can save more bandwidth.

Compared with the current Shannon's Classical Information Theory (CIT) paradigm, semantic communication (SemCom) has recently attracted more attention, since it aims to transmit the meaning of information rather than bit-by-bit transmission, thus enhancing data transmission efficiency and supporting future human-centric, data-, and resource-intensive intelligent services in 6G systems. Nevertheless, channel noises are common and even serious in 6G-empowered scenarios, limiting the communication performance of SemCom, especially when Signal-to-Noise (SNR) levels during training and deployment stages are different, but training multi-networks to cover the scenario with a broad range of SNRs is computationally inefficient. Hence, we develop a novel De-Noising SemCom (DNSC) framework, where the designed de-noiser module can eliminate noise interference from semantic vectors. Upon the designed DNSC architecture, we further combine adversarial learning, variational autoencoder, and diffusion model to propose the Latent Diffusion DNSC (Latent-Diff DNSC) scheme to realize intelligent online de-noising. During the offline training phase, noises are added to latent semantic vectors in a forward Markov diffusion manner and then are eliminated in a reverse diffusion manner through the posterior distribution approximated by the U-shaped Network (U-Net), where the semantic de-noiser is optimized by maximizing evidence lower bound (ELBO). Such design can model real noisy channel environments with various SNRs and enable to adaptively remove noises from noisy semantic vectors during the online transmission phase. The simulations on open-source image datasets demonstrate the superiority of the proposed Latent-Diff DNSC scheme in PSNR and SSIM over different SNRs than the state-of-the-art schemes, including JPEG, Deep JSCC, and ADJSCC.

With the continuous development of artificial intelligence (AI) technology, AI applications will play an increasingly important role in the sixth generation (6G) networks. At the same time, the emergence of technologies such as cloud computing has led to a growing number of AI models being applied in the Internet-of-Things (IoT). However, increasing sizes of AI models cause heavy burden on networks. In this paper, a multipath transmission scheme for the model slices based on the network function virtualization (NFV) is proposed. First, an optimization problem is formulated to decide the storage nodes for the model slices and the routing. With the physical network resource constraints, the problem is formulated as a mixed integer linear programming (MILP) to minimize the transmission cost. Second, a heuristic algorithm based on the steiner tree problem is designed to solve the optimization problem. Finally, based on the transfer learning method we get one generic slice and two specific slices from VGG16 for simulation. The results show when the destination nodes number and the network size are large, the transmission scheme for model slices has better performance in bandwidth utilization.

In this article, a cooperative communication network based on energy-harvesting (EH) decode-and-forward (DF) relays that harvest energy from the ambience using buffers with the harvest-store-use (HSU) architecture is considered. An opportunistic routing (OR) protocol, which selects the transmission path of packet based on the node transmission priority, is proposed to improve data delivery in this network. Additionally, an algorithm based on the state transition matrix (STM) is proposed to obtain the probability distribution of the candidate broadcast node set. Based on the probability distribution, the existence conditions and the theoretical expressions for the limiting distribution of energy in energy buffers using a discrete-time continuous-state space Markov chain (DCSMC) model are derived. Furthermore, the closed-form expressions for network outage probability and throughput are obtained with the help of the limiting distributions of energy stored in buffers. Numerous experiments have been performed to validate the derived theoretical expressions of the performance of this cooperative communication network.

The recently developed reconfigurable intelligent surfaces (RISs) are capable of improving the coverage of space–air–ground integrated networks (SAGINs), where the signals can be reflected in the desired direction without relying on power-thirsty radio-frequency (RF) chains. However, in the face of the substantially increased Doppler frequency, the classic orthogonal frequency-division multiplexing (OFDM) becomes inadequate in supporting RIS for the following reasons. First, the detrimental doubly selective fading leads to intersymbol interference (ISI) and intercarrier interference (ICI), which result in error floors for OFDM operating in the time–frequency (TF) domain. Second, it is far from trivial to configure RIS based on the time-varying fading channels. Third, the interpolation-based TF-domain channel estimation methods become impractical for the high-Doppler and high-dimensional RIS systems. Against this background, in this article, we propose the powerful 2-D orthogonal time–frequency space (OTFS) modulation for RIS-aided SAGINs, which transforms the time-varying fading encountered in the TF-domain to the time-invariant fading in the delay-Doppler (DD) domain. More explicitly, first, for the first time in the literature, we devise the DD-domain channel model of RIS-assisted SAGINs in the face of doubly selective fading. Second, in order to facilitate the RIS configuration in the DD-domain, we propose to create “virtual” Doppler frequencies that guide the phase changes at the RIS, even though the RIS phase rotations do not suffer from Doppler effects. Third, we conceive an attractive DD-domain RIS channel estimation method that can support both OFDM and OTFS, where the TF-domain interpolation is eliminated. Our simulation results demonstrate that the proposed DD-domain RIS configuration and channel estimation methods for both OFDM and OTFS are capable of mitigating the error floors encountered in the TF-domain. Furthermore, our simulation results confirm that OTFS-based RIS-assisted SAGIN systems are capable of outperforming their OFDM counterparts and exhibit excellent performance across a wide range of SAGIN channel parameters including the Ricean K factor, Doppler frequency, delay spread, coverage distance, and carrier frequency.

Traditional communication systems treat messages’ semantic aspects and meaning as irrelevant to communication, revealing its limitations in the era of artificial intelligence (AI), such as communication efficiency and intent-sharing among different entities. Through broadening the scope of the traditional communication system and the AI-based encoding techniques, in this manuscript, we present a novel semantic communication system, which involves the essential semantic information exploration, transmission and recovery for more efficient communications. Compared to other state-of-the-art semantic communication-related works, our proposed semantic communication system is characterized by the “flow of the intelligence” via the propagation of the model. Besides, the concept of semantic slice-models (SeSM) is proposed to enable flexible model-resembling under the different requirements of the model performance, channel situation and transmission goals. Specifically, a layer-based semantic communication system for images (LSCI) is built on the simulation platform to demonstrate the feasibility of the proposed system and a novel semantic metric called semantic service quality (SS) is proposed to evaluate the semantic communication systems. We evaluate the proposed system on Cityscapes and Open Images datasets, resulting in averaged 10% and 2% bit rate reduction over JPEG and JPEG2000, respectively. In comparison to LDPC, the proposed channel coding scheme can averagely save 2dB and 5dB in AWGN channel and Rayleigh fading channel, respectively.

In a multi-user system, system resources should be allocated to different users. In traditional communication systems, system resources generally include time, frequency, space, and power, so multiple access technologies such as time division multiple access (TDMA), frequency division multiple access (FDMA), space division multiple access (SDMA), code division multiple access (CDMA), and non-orthogonal multiple access (NOMA) are widely used. In semantic communication, which is considered a new paradigm of the next-generation communication system, we extract high-dimensional features from signal sources in a model-based artificial intelligence approach from a semantic perspective and construct a model information space for signal sources and channel features. From the high-dimensional semantic space, we excavate the shared and personalized information of semantic information and propose a novel multiple access technology, named model division multiple access (MDMA), which is based on the resource of the semantic domain. From the perspective of information theory, we prove that MDMA can attain more performance gains than traditional multiple access technologies. Simulation results show that MDMA saves more bandwidth resources than traditional multiple access technologies, and that MDMA has at least a 5-dB advantage over NOMA in the additive white Gaussian noise (AWGN) channel under the low signal-to-noise (SNR) condition.

The electrocardiogram (ECG) signal is used to monitor the electrical activity of the heart throughout each cardiac cycle. Cardiovascular disease (CVD) and arrhythmias can be diagnosed using an ECG. Meanwhile, as Artificial Intelligence progressed, deep learning is increasingly being utilized to detect and classify ECGs. Even though various algorithms for detecting and classifying ECG signals have been developed, the majority of them focus on signals with a high signal-to-noise ratio (SNR) obtained from hospital patients. Detecting R peaks in noisy signals collected by wearable dynamic ECG devices remains research value and practical significance. In this paper, an approach combined 8-layer U-net with depthwise separable convolution named 8-DS-Unet is proposed to locate R peaks, particularly during the low-quality signal episode. Additionally, a six-layer network (6-DS-Unet) is proposed to further improve inference speed. The algorithm's high accuracy and low computational complexity enable model porting to wearables with limited hardware resources. The network was trained using the CPSC 2019 dataset and then validated on four additional datasets to verify robustness. In the CPSC 2019 dataset, the proposed 8-DS-Unet network achieved a Precision of 0.9966 and a Recall of 0.9915.

Visible light communication on the basis of IM/DD system has attracted enormous interest in recent years. One of the major topics to be investigated in this field is orthogonal frequency division multiplexing (OFDM). This paper proposed a novel iterative receiver for PAM-DMT based hybrid OFDM in order to enhance its performance. The concept of OFDM models and structure of conventional receiver are introduced firstly. Then the proposed iterative receiver and its computational complexity are presented. Simulation showed that under the same bit error rate (BER) of 10−4, the required signal to noise ratio (SNR) for transmitting has been reduced for about 2.5 dB. In conclusion, the proposed iterative receiver could achieve a considerable performance gain under a variety of simulation conditions, which demonstrated its potential for being applied in the visual light communication system.

In this study, the authors consider a two‐hop network, where multiple source nodes (SNs) transmit to a destination node (DN) with the aid of a relay node (RN). The RN is equipped with a buffer, which is capable of storing multiple frames received from the SNs. During each time slot, the proposed protocol activates either the SN–RN hop or the RN–DN hop, depending on the channel quality of each hop and the buffer state at the RN. To optimise the hop activation for the network, they design a hop quality metric and propose a multi‐user buffer‐aided‐relaying uplink (MU‐BR‐UL) protocol, with the aid of the minimum signal‐to‐noise power ratio approximation. The benefits of the proposed protocol are analysed in terms of the end‐to‐end (e2e) outage probability and the e2e transmission delay. Then, the optimal power allocation is proposed for minimising the e2e outage probability under the total power constraint. The results indicate that the outage performance is significantly improved when the proposed power allocation is utilised in the MU‐BR‐UL protocol.

This work considers the physical-layer security of a wiretap channel, where a transmitter communicates with a receiver using the precoding-aided spatial modulation (PSM) in the presence of an unauthorized passive eavesdropper. We first analyze the security capability of the PSM and show that the PSM has the property of low-probability-of-interception (LPI). Based on the observations, we then design a secret PSM (SPSM), which represents a generalization of the PSM. We derive the upperbounds for the bit error rate (BER) of both the desired receiver and the eavesdropper when assuming communications over Rayleigh fading channels. Both the numerical results evaluated from the BER upper-bounds and the simulation results are provided to demonstrate the secrecy performance of the SPSM. Our studies show that the SPSM can significantly enhance the security of the PSM, in addition to inheriting all its merits.

In multihop diversity (MHD) aided multihop links, the nodes are assumed to have buffers for temporarily storing their received packets for further transmission at instances of good channel quality. Since adaptive modulation is employed, the number of packets in each time slot (TS) is affected both by the channel quality and the buffer fullness. During each time-slot (TS), the criterion used for activating a specific hop is that of transmitting the highest number of packets. When more than one hop is capable of transmitting the same number of packets, the particular hop having the highest channel quality (reliability) is activated. Hence, we refer to this regime as the maximum throughput adaptive rate transmission (MTART) scheme. The bit error ratio (BER), the outage probability, the throughput as well as the bandwidth-efficiency of the MTART scheme is analyzed. Our results demonstrate that our MTART regime has the potential of significantly outperforming conventional adaptive modulation. Naturally, the BER is improved by the buffering scheme advocated at the cost of an increased delay. Hence, the distribution of the end-to-end packet delay will also be characterized.

In this contribution, we propose and study a bufferaided opportunistic routing (BOR) scheme, which combines the benefits of both opportunistic routing and multihop diversity (MHD) aided transmissions. It was conceived for a buffer-aided three-node network (B3NN) composed of a source node (SN), a buffer-aided relay node (RN) and a destination node (DN). In this network, there are three channels namely the SN-RN, RN-DN and SN-DN channels. The key motivation is that we are aiming for activating the specific channels requiring a recuded energy dissipation. In order to study this problem, a three-dimensional (3D) transmission activation probability space (TAPS) is proposed, which is divided into four regions representing each of the three channels plus an outage region. In a specific time slot (TS), the instantaneous channel fading values may be directly mapped to a specific point in this 3D channel space. The BOR scheme then relies on the position of this point to select the most appropriate channel for its transmission. Both the energy dissipation and the outage probability (OP) are investigated for transmission in this network. The results show that when the system is operated at a normalized throughput of 0.4 packet/TS, the energy dissipation was reduced by 24.8% to 77.6% compared to three different benchmark schemes. Alternatively, our technique is capable of reducing the OP by 89.6% when compared to conventional opportunistic routing. As in all buffer-aided system, the performance improved with the cost of higher packet delay, which is also studied.

Hierarchical modulation (HM) is widely employed across the telecommunication industry. The potential application of the coded HM scheme in cooperative communications has drawn much interest. In this paper, a twin-relay-aided triple-layer cooperative communication system is proposed. The system amalgamates rate-1/2 TTCM, triple-layer HM-64QAM, and twin-layer SPM-16QAM schemes in the context of cooperative communications. We have optimized the entire system based on the HM ratio pair (R1, R2), the superposition modulation (SPM) weighting pair (α, β), and the positions of the two relays. The simulation results show that our optimized system is capable of reliably transmitting a triple-layer HM-64QAM signal with the aid of two time slots at an average signal-to-noise ratio of 6.94 dB per time slot.

In multihop diversity aided multihop links, the number of bits transmitted in each Time Slot (TS) is affected by both the Channel Quality (CQ) and the Buffer Fullness (BF), when adaptive modulation is employed. We assume that every node has buffers for temporarily storing its received packets for further transmission at instances of good CQ. For the sake of improving the throughput, a Maximum Throughput Adaptive Rate Transmission (MTART) scheme was proposed, where the specific hop having the capability of transmitting the highest number of bits (packets) will be activated. If more than one hops are capable of transmitting the same number of bits, the particular hop having the highest CQ (reliability) is activated. We demonstrate that the MTART scheme has 8 dB gain at the Outage Probability (OP) of 10-3and has 3 dB gain in terms of the throughput attained in comparison to the conventional adaptive modulation aided scheme.

We propose a simple yet effective norm-based joint transmit and receive antenna selection (NBJTRAS) assisted and two-tier channel estimation (TTCE) aided space-time shift keying (STSK) system, which is capable of significantly outperforming the conventional STSK system, while efficiently utilising available radio frequency (RF) chains. Specifically, the NBJTRAS carries out antenna selection based on the channel estimation (CE) generated using a low-complexity training based least square channel estimator by reusing RF chains. The selected sub-channel matrix is further refined by an efficient semi-blind CE and data detection scheme. Our simulation results show that only a few iterations are sufficient for the TTCE scheme to approach the optimal maximum-likelihood detection performance associated with perfectly channel state information.

In this paper buffer-aided Opportunistic Routing (OR) was designed with the aid of the novel concept of linear Transmission Activation Probability Space (TAPS) partitioning invoked for relay-assisted networks, which combines the benefits of both OR [1] and of buffer-aided transmissions [2]. More specifically, a packet may be transmitted from the Source Node (SN) to the Destination Node (DN) either directly or indirectly via one of theMRelay Nodes (RNs), depending on the instantaneous channel qualities. The above-mentioned linear multi-dimensional TAPS partitioning concept is proposed for partitioning the transmission space into (2M+1) transmission regions plus an outage region, while ensuring that the number of input packets is equal to the number of output packets at each RN's buffer. The benefit of having a buffer and tolerating the associated delay is that the best channel is activated for transmission based on our linear TAPS partitioning method.

A buffer-aided two hop link is studied, where the RN is capable of temporarily storing the received packets. We commerce by defining the concept of a two-dimensional Channel Probability Space (CPS) based on the source-relay and relay-destination channel. Specifically, a non-linear CPS division method is proposed, which partitions the CPS into several regions representing the quality of the specific channels plus an outage region. Then the best channel is activated for the sake of minimizing the system's energy dissipation. Finally, the proposed buffer-aided transmission scheme relying on our non-linear CPS division regime is investigated and the results show that at given average end-to-end energy dissipation, the outage probability was reduced by 33.5% compared to the benchmark scheme.

Most of the nodes in ad hoc networks rely on batteries, which requires energy saving. Hence, numerous energy-efficient routing algorithms have been proposed for solving this problem. In this paper, we exploit the benefits of cross-layer information exchange, such as the knowledge of the Frame Error Rate (FER) in the physical layer, the maximum number of retransmissions in the Medium Access Control (MAC) layer and the number of relays in the network layer. Energy-consumption-based Objective Functions (OF) are invoked for calculating the end-to-end energy consumption of each potentially available route for both Traditional Routing (TR) and for our novel Opportunistic Routing (OR), respectively. We also improve the TR and the OR with the aid of efficient Power Allocation (PA) for further reducing the energy consumption. For the TR, we take into account the dependencies amongst the links of a multi-hop route, which facilitates a more accurate performance evaluation than upon assuming the links that are independent. Moreover, two energy-efficient routing algorithms are designed based on Dijkstra's algorithm. The algorithms based on the energy OF provide the theoretical bounds, which are shown to be close to the bound found from exhaustive search, despite the significantly reduced complexity of the former. Finally, the end-to-end throughput and the end-to-end delay of this system are analyzed theoretically and a new technique of characterizing the delay distribution of OR is proposed. The simulation results show that our energy-efficient OR outperforms the TR and that their theoretical analysis accurately matches the simulation results.

The concept of multihop diversity is proposed, where all the nodes of a multihop link are assumed to have buffers for temporarily storing their received packets. During each time-slot, the best hop having, for example, the highest signal-to-noise ratio (SNR), is selected from the set of those hops, where the corresponding nodes have packets awaiting transmission in their buffer. The packet is then transmitted over the best hop. Explicitly, this hop-selection procedure yields selection diversity. In this paper, we assume having perfect channel knowledge and focus our attention on the principles and performance bounds of the error probability and outage probability, when M-ary quadrature amplitude modulation (MQAM) is employed. The error probability and outage probability of the multihop links operated under our proposed multihop diversity scheme are investigated, when communicating over Rayleigh fading channels. Our studies show that relying on multiple hops has the potential of providing a significant diversity gain, which may be exploited for enhancing the reliability of wireless multihop communications.

The concept of multihop diversity is proposed, where all the nodes of a multihop link are assumed to have buffers for temporarily storing their received packets. During each time-slot, the best hop having, for example, the highest signal-to-noise ratio (SNR), is selected from the set of those hops that have packets awaiting transmission in the buffer. The packet is then transmitted over the best hop. This hop-selection procedure yields selection diversity, but it requires the global channel knowledge of the hops' channel quality. In this paper, we assume having perfect channel knowledge and focus our attention on the principles and performance bounds of the error probability and outage probability. Our studies show that relying on multiple hops has the potential of providing a significant diversity gain, which may be exploited for enhancing the reliability of wireless multihop communications.

In ad hoc networks the nodes actively and voluntarily participate in constructing a network and act as relays for other nodes. As a result of node-mobility, the Channel State Information (CSI) varies and hence a substantial amount of control messages have to be exchanged across the network to maintain reliable communications between certain pairs of nodes, which potentially imposes a high energy-consumption. Therefore, minimizing the energy consumption and maximizing the throughput of ad hoc nodes is extremely important. This paper analyzes both the energy consumption and the achievable throughput of a multihop route by exploiting both the CSI quantified, for example in terms of the Frame Error Ratio (FER), as well as the number of Medium Access Control (MAC) retransmissions and the number of hops. Since using limited number of MAC retransmissions in a hop-by-hop retransmission mode imposes dependencies amongst the links of a multi-hop route, a more accurate objective function may be formulated than that which assumes the availability of an infinite number of retransmissions and which considers the links to be independent. Our simulations confirm the improved accuracy of the proposed Objective Function (OF), especially when the FER and the number of hops are high and the number of MAC retransmissions is low. Additionally, a low-complexity routing algorithm is designed, which carries out routing decisions based on the energy consumption predicted by the OF, and strikes a compromise between having 'few long-distance hops' and 'many short-distance hops' for the sake of energy minimization and throughput maximization.