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, 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.

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.

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.

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.