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 paper proposes using the mean squared error (MSE) of the semantic information between the transmitter and receiver as a performance metric for semantic communication. This paper 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 semantic digital modulation constellation mapping 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 3dB while maintaining the same multi-scale 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.

In this article, we propose a multi-label convolution neural network (MLCNN)-aided transmit antenna selection (AS) scheme for end-to-end multiple-input multiple-output (MIMO) Internet of Things (IoT) communication systems in correlated channel conditions. In contrast to the conventional single-label multi-class classification ML schemes, we opt for using the concept of multi-label in the proposed MLCNN-aided transmit AS MIMO IoT system, which may greatly reduce the length of training labels in the case of multi-antenna selection. Additionally, applying multi-label concept may significantly improve the prediction accuracy of the trained MLCNN model under correlated large-scale MIMO channel conditions with less training data. The corresponding simulation results verified that the proposed MLCNN-aided AS scheme may be capable of achieving near-optimal capacity performance in real time, and the performance is relatively insensitive to the effects of imperfect CSI.

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, the 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, compared with the element-distance-based scheme, the proposed shared feature extraction method based on Pearson correlation not only exhibits an approximate 0.2 advantage in multiscale structural similarity (MS-SSIM) but also efficiently eliminates the burden caused by the transmission of shared feature indexes.

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.

In this research, we present a cooperative communication network based on two energy-harvesting (EH) decode-and-forward (DF) relays that contain the harvest-store-use (HSU) architecture and may harvest energy from the surrounding environment by utilizing energy buffers. To improve the performance of this network, an opportunistic routing (OR) algorithm is presented that takes into account channel status information, relay position, and energy buffer status, as well as using the maximal ratio combining (MRC) at the destination to combine the signals received from the source and relays. The theoretical expressions for limiting distribution of energy stored in infinite-size buffers are derived from the discrete-time continuous-state space Markov chain model (DCSMC). In addition, the theoretical expressions for outage probability, throughput, and per-packet time slot cost in the network are obtained utilizing both the limiting distributions of energy buffers and the probabilities of transmitter candidates set. Through numerous simulation results, it is demonstrated that simulation results match with corresponding theoretical results.

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 multiantenna systems. To address this gap, we propose semantic importance-aware communication [SIAC-multiple-input multiple-output (MIMO)], a system designed for 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 multiuser 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 $2{\times }2$ to $64{\times }64$ 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 multiuser MIMO setups compared to traditional source-channel separation schemes, highlighting the system’s potential for efficient and robust communication.