Physical-Layer Authentication (PLA) offers endogenous security, lightweight implementation, and high reliability, making it a promising complement to upper-layer security methods in Edge Intelligence (EI)-empowered Industrial Internet of Things (IIoT). However, state-of-the-art Channel State Information (CSI)-based PLA schemes face challenges in recognizing mobile multi-users due to the constantly shifting CSI distributions with user movements. To address this issue, we propose a Temporal Dynamic Graph Convolutional Network (TDGCN)-based PLA scheme, which employs Graph Neural Networks (GNNs) to capture the spatio-temporal dynamics induced by user movements. Firstly, we partition CSI fingerprints into multivariate time series and utilize dynamic GNNs to capture their associations. Secondly, Temporal Convolutional Networks (TCNs) handle temporal dependencies within each CSI fingerprint dimension. Additionally, Dynamic Graph Isomorphism Networks (GINs) and cascade node clustering pooling further enable efficient information aggregation and reduced computational complexity. Simulations demonstrate the proposed scheme's superior authentication accuracy compared to seven baseline schemes.

Satellite-ground semantic communication is anticipated to serve a critical role in the forthcoming sixth-generation (6G) mobile networks. Nonetheless, task-oriented data transmission in such systems remains a formidable challenge, primarily due to the dynamic nature of Signal-to-Noise Ratio (SNR) fluctuations and the stringent bandwidth limitations inherent to Low Earth Orbit (LEO) satellite channels. In response to these constraints, we propose an Importance-Aware Robust Semantic Transmission (IRST) framework, specifically designed for scenarios characterized by bandwidth scarcity and channel variability. The IRST scheme begins by applying a segmentation model enhancement algorithm to improve the granularity and accuracy of semantic segmentation. Subsequently, a task-driven semantic selection method is employed to prioritize the transmission of semantically vital content based on real-time Channel State Information (CSI). Furthermore, the framework incorporates a stack-based, SNR-aware channel codec capable of executing adaptive channel coding in alignment with SNR variations. Comparative evaluations across diverse operating conditions demonstrate the superior performance and resilience of the IRST model relative to existing benchmarks. The code is available at https://github.com/lightwindy-ch/IRST.git.

Semantic communication employs deep learning to transmit semantically meaningful information rather than raw data, thereby improving communication efficiency. To facilitate the adaptation of continuous semantic features to digital transmission systems, vector quantization (VQ) serves as an effective approach for discretizing high-dimensional features into compact codebook indices. However, existing VQ-based systems face a critical dilemma: conventional VQ codebooks demand large index ranges to preserve fidelity, contradicting digital modulation’s need for limited discrete states to ensure noise robustness. To bridge this gap, we design a 2K image transmission framework that jointly considers codebook compactness and transmission robustness. The framework operates in two stages: In Stage 1, we devise MOC-RVQ, a multi-head ordered codebook (MOC) with residual vector quantization (RVQ) to reduce the index range while maintaining image fidelity. In Stage 2, a Swin Transformer-based noise reduction block (NRB) is integrated with feature requantization for further robust restoration. Experiments on 2K-resolution datasets demonstrate that the proposed MOC-RVQ surpasses traditional codecs like BPG, JPEG, and learnable baselines, while maintaining low transmission overhead.

Diffusion models (DMs) have recently achieved significant success in wireless communications systems due to their denoising capabilities. The broadcast nature of wireless signals makes them susceptible not only to Gaussian noise, but also to unaware interference. This raises the question of whether DMs can effectively mitigate interference in wireless semantic communication systems. In this paper, we model the interference cancellation problem as a maximum a posteriori (MAP) problem over the joint posterior probability of the signal and interference, and theoretically prove that the solution provides excellent estimates for the signal and interference. To solve this problem, we develop an interference cancellation diffusion model (ICDM), which decomposes the joint posterior into independent prior probabilities of the signal and interference, along with the channel transition probability. The log-gradients of these distributions at each time step are learned separately by DMs and accurately estimated through deriving. ICDM further integrates these gradients with advanced numerical iteration method, achieving accurate and rapid interference cancellation. Extensive experiments demonstrate that ICDM significantly reduces the mean square error (MSE) and enhances perceptual quality compared to schemes without ICDM. For example, on the CelebA dataset under the Rayleigh fading channel with a signal-to-noise ratio (SNR) of 20 dB and signal to interference plus noise ratio (SINR) of 0 dB, ICDM reduces the MSE by 4.54 dB and improves the learned perceptual image patch similarity (LPIPS) by 2.47 dB. The code is available at https://github.com/Wireless3C-SJTU/ICDM.

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.

Next-generation wireless networks are expected to develop a novel paradigm of integrated sensing and communications (ISAC) to enable both the high-accuracy sensing and high-speed communications. However, conventional mono-static ISAC systems, which simultaneously transmit and receive at the same equipment, may suffer from severe self-interference, and thus significantly degrade the system performance. To address this issue, this paper studies a multi-static ISAC system for cooperative target localization and communications, where the transmitter transmits ISAC signal to multiple receivers (REs) deployed at different positions. We derive the closed-form of weighted sum Cramér-Rao bound (CRB) on the joint estimations of both the transmission delay and Doppler shift for cooperative target localization, and the weighted sum CRB minimization problem is formulated by considering the cooperative cost and communication rate requirements for the REs. To solve this problem, we first decouple it into two subproblems for RE selection and transmit beamforming, respectively. Then, a minimax linkage-based method is proposed to solve the RE selection subproblem, and a successive convex approximation algorithm is adopted to deal with the transmit beamforming subproblem with non-convex constraints. Finally, numerical results validate our analysis and reveal that our proposed multi-static ISAC scheme achieves better ISAC performance than the conventional mono-static ones with ideal SI cancellation when the number of cooperative REs is large.

End-to-end (E2E) semantic communication (SemCom) powered by semantic knowledge base (SKB) is an efficient SemCom framework. However, in practical scenarios, SKB discrepancy among multiple SemCom pairs arises due to dynamic environmental changes (e.g., varying source data or tasks) or system-level alterations (e.g., integration of new SemCom pairs with divergent SKBs). Such discrepancy leads to performance disparity in semantic transmission, where underperforming pairs fail to maintain efficient task execution. To address this challenge, this paper introduces a cooperative SKB update policy, which enables collaborative evolution of SKBs to mitigate SKB discrepancy and improve performance of underperforming pairs. For each SemCom pair endowed with SKB-enabled SemCom, partial local SKB is selected out and uploaded to a mobile edge computing (MEC) server for establishing a global SKB. The global SKB aggregates the advantages of each local SKB, and is broadcasted to all SemCom pairs. Then, each SemCom pair updates their local SKB with the assistance of the global SKB. This process makes the local SKBs more sensible and less ambiguous, thereby enhancing the semantic transmission performance. Furthermore, in order to maximize the cooperative gains under limited uplink budgets of SemCom pairs, a knowledge selection optimization problem is formulated for the selection of the uploaded knowledge. Numerical results show that the proposed cooperative SKB update policy obtains significant performance gains, especially for the initially poor-performing pairs, and provide comprehensive performance comparison of the knowledge selection scheme.

With the rapid advancement of 6G, identity authentication has become increasingly critical for ensuring wireless security. The lightweight and keyless Physical Layer Authentication (PLA) is regarded as an instrumental security measure in addition to traditional cryptography-based authentication methods. However, existing PLA schemes often struggle to adapt to dynamic radio environments. To overcome this limitation, we propose the Adaptive PLA with Channel Extrapolation and Generative AI (APEG), designed to enhance authentication robustness in dynamic scenarios. Leveraging Generative AI (GAI), the framework adaptively generates Channel State Information (CSI) fingerprints, thereby improving the precision of identity verification. To refine CSI fingerprint generation, we propose the Collaborator-Cleaned Masked Denoising Diffusion Probabilistic Model (CCMDM), which incorporates collaborator-provided fingerprints as conditional inputs for channel extrapolation. Additionally, we develop the Cross-Attention Denoising Diffusion Probabilistic Model (CADM), employing a cross-attention mechanism to align multi-scale channel fingerprint features, further enhancing generation accuracy. Simulation results demonstrate the superiority of the APEG framework over existing time-sequence-based PLA schemes in authentication performance. Notably, CCMDM exhibits a significant advantage in convergence speed, while CADM, compared with model-free, time-series, and VAE-based methods, achieves superior accuracy in CSI fingerprint generation.

Semantic communication (SemCom), as a novel paradigm for future communication systems, has recently attracted much attention due to its superiority in communication efficiency. However, similar to traditional communication, it also suffers from eavesdropping threats. Intelligent eavesdroppers could launch advanced semantic analysis techniques to infer secret semantic information. Therefore, some researchers have designed Semantic Steganography Communication (SemSteCom) schemes to confuse semantic eavesdroppers. However, the state-of-the-art SemSteCom schemes for image transmission rely on the pre-selected cover image, which limits the generalization. To address this issue, we propose a Generative Diffusion Model-based Coverless Semantic Steganography Communication (SemSteDiff) scheme to hide secret images into generated stego images. The semantic related private and public keys enable legitimate receiver to decode secret images correctly while the eavesdropper without the completely correct key-pairs fail to obtain them. Simulation results demonstrate the effectiveness of the plug-and-play design in different Joint Source-Channel Coding (JSCC) frameworks. Results under different eavesdropping settings show that, when Signal-to-Noise Ratio (SNR) = 0 dB, the peak signal-to-noise ratio (PSNR) of the legitimate receiver is 4.14 dB higher than that of the eavesdropper.

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.

The existing works on holographic MIMO are mainly based on the time encoding (TE) scheme. Since the continuous aperture of holographic MIMO is able to capture both the temporal and the spatial variation of electromagnetic waves, we propose a space-time encoding (STE) scheme, which relies on the orthogonal basis function representation of the spatial-temporal EM waves. From the perspective of electromagnetic information theory, we derive the achievable information rate of the STE scheme in the narrowband communication systems and prove that the STE scheme achieves a higher information rate than the TE scheme. Firstly, We build the transmission model of the STE scheme based on electromagnetic information theory and investigate the characteristics of the model, including the blocklength of codewords and the signal-to- noise ratio. Specifically, the blocklength is determined through proving the eigenvalue distribution of the space-time-wavenumber-frequency limited operator and the signal-to-noise ratio is obtained based on proposed noise model. Then we derive the achievable information rate of both the STE scheme and the TE scheme by employing the finite blocklength information theory. Closed-form approximations of the rates are further derived, based on which we prove the conclusion that the STE scheme achieves a higher information rate than the TE scheme while utilizing the same spatial and temporal resources. Numerical results verify the accuracy of the approximations and indicate that the STE scheme improves the information rate by 7.96% over the TE scheme.

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.

To facilitate low-latency and secure transmission in Internet of Everything, a secure short-packet transmission framework is proposed in an uplink non-orthogonal multiple access (NOMA)-based integrated sensing and communication (ISAC) system. A triple-functional base station is utilized, which simultaneously carries out the tasks of receiving short-packet messages, detecting potential eavesdroppers, and transmitting active jamming signals. The achievable secure short-packet transmission rate is developed to measure the security performance, where the practical cases of imperfect inter-functional and inter-device interference elimination are considered. To optimize the security performance by effectively leveraging sensing capabilities, a problem is formulated aiming at maximizing the secure short-packet transmission rate, while guaranteeing the sensing quality. A key challenge to solve this problem lies in the performance loss terms associated with decoding error and information leakage in short-packet transmission, which makes the optimization problem highly coupled and strictly non-convex. To tackle this challenge, an alternating optimization (AO)-based approach is devised to solve the formulated problem iteratively, where an approximation method is proposed to convert the secure short-packet transmission rate into a manageable form. The convergence and effectiveness of the proposed design are validated by simulation results, which reveal that i) the devised AO-based algorithm converges within a modest number of iteration times; ii) the proposed NOMA-ISAC-based security design outperforms other benchmark schemes, demonstrating the benefit of utilizing sensing capabilities to enhance security.

As a promising 6G enabler beyond conventional bit-level transmission, semantic communication can considerably reduce required bandwidth resources, while its combination with multiple access requires further exploration. This paper proposes a knowledge distillation-driven and diffusion-enhanced (KDD) semantic non-orthogonal multiple access (NOMA), named KDD-SemNOMA, for multi-user uplink wireless image transmission. Specifically, to ensure robust feature transmission across diverse transmission conditions, we firstly develop a ConvNeXt-based deep joint source and channel coding architecture with enhanced adaptive feature module. This module incorporates signal-to-noise ratio and channel state information to dynamically adapt to additive white Gaussian noise and Rayleigh fading channels. Furthermore, to improve image restoration quality without inference overhead, we introduce a two-stage knowledge distillation strategy, i.e., a teacher model, trained on interference-free orthogonal transmission, guides a student model via feature affinity distillation and cross-head prediction distillation. Moreover, a diffusion model-based refinement stage leverages generative priors to transform initial SemNOMA outputs into high-fidelity images with enhanced perceptual quality. Extensive experiments on CIFAR-10 and FFHQ-256 datasets demonstrate superior performance over state-of-the-art methods, delivering satisfactory reconstruction performance even at extremely poor channel conditions. These results highlight the advantages in both pixel-level accuracy and perceptual metrics, effectively mitigating interference and enabling high-quality image recovery.

Lightweight and efficient neural network models for deep joint source-channel coding (JSCC) are crucial for semantic communications. In this paper, we propose a novel JSCC architecture, named MambaJSCC, that achieves great performance with low computational and parameter overhead. MambaJSCC utilizes the visual state space model with channel adaptation (VSSM-CA) blocks as its backbone for transmitting images over wireless channels, where the VSSM-CA primarily consists of the generalized state space models (GSSM) and the zero-parameter, zero-computational channel adaptation method (CSI-ReST). We design the GSSM module, leveraging reversible matrix transformations to express generalized scan expanding operations, and theoretically prove that two GSSM modules can effectively capture global information. We discover that GSSM inherently possesses the ability to adapt to channels, a form of endogenous intelligence. Based on this, we design the CSI-ReST method, which injects channel state information (CSI) into the initial state of GSSM to utilize its native response, and into the residual state to mitigate CSI forgetting, enabling effective channel adaptation without introducing additional computational and parameter overhead. Experimental results on different devices, including IoT device JETSON AGX ORIN, show that MambaJSCC not only outperforms existing JSCC methods (e.g., SwinJSCC) across various scenarios but also significantly reduces parameter size, computational overhead, and inference delay. We have released our code and pre-trained models at https://github.com/Wireless3C-SJTU/MambaJSCC, allowing full reproduction of our results.

As a typical scenario for the 6th Generation mobile communication systems (6G), Hyper Reliable Low Latency Communication (HRLLC) is expected to ensure extremely low delay and high reliability, while supporting wireless transmission of large-scale massive data. However, existing communication networks face the dual challenges of inadequate performance metrics and limited network resources. Therefore, this paper proposes the Flexible Bit and Semantic on-demand Transmission (FBST) framework, including three key technologies: adaptive transmission mode decision, flexible transmission time interval scheduling, adjustable semantic compression ratio. The FBST framework could satisfy the strict QoS requirements of users and provide on-demand services for users. Based on the Stochastic Network Calculus (SNC) modeling method, we conduct precise delay analysis and provided a general expression for the delay violation probability of the α - κ - μ channel, which could be extended to various complex channels. In addition, the Knowledge-base Parameterized Deep Q-Network (KP-DQN) algorithm is proposed to solve the resource allocation issue, which is a mixed action space problem with complex calculations caused by SNC. Finally, the simulation results show that FBST framework could satisfy extremely strict delay and reliability requirements of users, and the KP-DQN algorithm improving operational efficiency by over 76.8%.

The state-of-the-art semantic communication (SC) schemes typically rely on end-to-end deep learning frameworks that lack interpretability and struggle with robust semantic selection and reconstruction under noisy conditions. To address this issue, this paper presents KGRAG-SC, a knowledge graph-assisted SC framework that leverages retrieval-augmented generation principles. KGRAG-SC employs a multi-dimensional knowledge graph, enabling efficient semantic extraction through community-guided entity linking and GraphRAG-assisted processing. The transmitter constructs minimal connected subgraphs that capture essential semantic relationships and transmits only compact entity indices rather than full text or semantic triples. An importance-aware adaptive transmission strategy provides unequal error protection based on structural centrality metrics, prioritizing critical semantic elements under adverse channel conditions. At the receiver, large language models perform knowledge-driven text reconstruction using the shared knowledge graph as structured context, ensuring robust semantic recovery even with partial information loss. Experimental results demonstrate that KGRAG-SC achieves superior semantic fidelity in low Signal-to-Noise Ratio (SNR) conditions while significantly reducing transmission overhead compared to traditional communication methods, highlighting the effectiveness of integrating structured knowledge representation with generative language models for SC systems.

Semantic communication (SemCom), as a typical paradigm of deep integration between artificial intelligence (AI) and communication technology, significantly improves communication efficiency and resource utilization efficiency. However, the security issues of SemCom are becoming increasingly prominent. Semantic features transmitted in plaintext over physical channels are easily intercepted by eavesdroppers. To address this issue, this paper proposes Encrypted Semantic Super-Resolution Enhanced Communication (SREC) to secure SemCom. SREC uses the modulo-256 encryption method to encrypt semantic features, and employs super-resolution reconstruction method to improve the reconstruction quality of images. The simulation results show that in the additive Gaussian white noise (AWGN) channel, when different modulation methods are used, SREC can not only stably guarantee security, but also achieve better transmission performance under low signal-to-noise ratio (SNR) conditions.

Integrated sensing and communications (ISAC) as one of the key technologies is capable of supporting high-speed communication and high-precision sensing for the upcoming 6G. This paper studies a waveform strategy by designing the orthogonal frequency division multiplexing (OFDM)-based reference signal (RS) for sensing and communication in ISAC system. We derive the closed-form expressions of Cramér-Rao bound (CRB) for the distance and velocity estimations, and obtain the communication rate under the mean square error of channel estimation. Then, a weighted sum CRB minimization problem on the distance and velocity estimations is formulated by considering communication rate requirement and RS intervals constraints, which is a mixed-integer problem due to the discrete RS interval values. To solve this problem, some numerical methods are typically adopted to obtain the optimal solutions, whose computational complexity grow exponentially with the number of symbols and subcarriers of OFDM. Therefore, we propose a relaxation and approximation method to transform the original discrete problem into a continuous convex one and obtain the sub-optimal solutions. Finally, our proposed scheme is compared with the exhaustive search method in numerical simulations, which show slight gap between the obtained sub-optimal and optimal solutions, and this gap further decreases with large weight factor.

Score-based diffusion models represent a significant variant within the family of diffusion models and have found extensive application in the increasingly popular domain of generative tasks. Recent investigations have explored the denoising potential of diffusion models in semantic communications. However, in previous paradigms, noise distortion in the diffusion process does not match precisely with digital channel noise characteristics. In this work, we introduce the ScoreBased Channel Denoising Model (SCDM) for Digital Semantic Communications (DSC). SCDM views the distortion of constellation symbol sequences in digital transmission as a score-based forward diffusion process. We design a tailored forward noise corruption to better align digital channel noise properties in the training phase. During the inference stage, the well-trained SCDM can effectively denoise received semantic symbols under various SNR conditions, reducing the difficulty for the semantic decoder in extracting semantic information from the received noisy symbols and thereby enhancing the robustness of the reconstructed semantic information. Experimental results show that SCDM outperforms the baseline model in PSNR, SSIM, and MSE metrics, particularly at low SNR levels. Moreover, SCDM reduces storage requirements by a factor of 7.8. This efficiency in storage, combined with its robust denoising capability, makes SCDM a practical solution for DSC across diverse channel conditions.

In the space-air-ground integrated networks of the sixth generation (6 G) systems, many satellite imagery need to be transmitted from the satellite to the ground with high resolution for further processing. However, it faces the challenges of limited available bandwidth and the poor channel conditions of satellite-to-ground links. In this paper, leveraging the benefits of semantic communication for efficient transmission under limited bandwidth and low signal-to-noise ratio (SNR) conditions, a joint Preprocessing and Multi-task driven Semantic Communication (PMSC) system for satellite imagery transmission is proposed. To efficiently use the limited bandwidth, we propose a region of interest (ROI) based preprocessing method, which focuses on only relevant regions that will be encoded into semantic information, and processes the ROIs that are pivotal for the tasks. Moreover, we formulate a multi-task driven semantic communication system with a universal joint semantic-channel encoder and distinct decoding processes, making the received semantic features of satellite imagery can be accurately and effectively utilized for different applications. The simulation results demonstrate that the proposed PMSC system has better performance in enhancing reconstruction and classification in the target regions of interest at the same compression ratio, especially under low SNR conditions.

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.

With the development of 6G immersive communication, video intelligent analytics has garnered significant attention. Video intelligent analytics has diverse requirements in different immersive service scenarios, especially in accuracy and latency. However, as resource-limited terminal devices struggle to accom-plish high-accuracy video intelligent analytics tasks, video frames have to be offloaded to edge nodes with sufficient computational and cache resources for further processing. Therefore, in this paper, we consider device-edge collaboration video intelligent an-alytics tasks to improve trade-off performance between accuracy and latency. Specifically, we propose a joint optimization scheme for video frame scheduling, adaptive video frame compression and Machine Learning (ML) model caching to maximize the minimum of utility among all users. We divide the joint optimization problem into two sub-problems and use convex optimization to solve the adaptive frame compression optimization problem. Furthermore, to avoid the curse of dimensionality, we design an expert-assisted proximal policy optimization (EPPO)-based joint video frame scheduling and resource allocation algorithm. Simulation results demonstrate the superiority of the proposed scheme in improving video intelligent analytics performance.

Semantic communications (SemCom) is considered one of the key technologies for next-generation communications. However, most SemCom systems utilize Deep Learning (DL) based joint source-channel coding (JSCC), which are incompatible with existing digital communication systems. In this paper, we propose a novel digital SemCom system based on variable product quantization (VPQ-SemCom), which harnesses multiple lightweight codebooks to represent images and dynamically optimize bitrates according to the entropy of semantic features to adapt to transmission scenarios with multiple bandwidths and SNRs. Specifically, product quantization (PQ), which can represent semantic features with several lightweight codebooks, is introduced to provide powerful representation capacities of semantic features. Furthermore, a rate adaption module, which can flexibly adjust feature length based on the entropy of semantic features, is proposed to integrate with PQ to improve rate-distortion performance. The experimental results demonstrate that VPQ-SemCom shows 32.4% improvement at high SNRs and 62.2% improvement at SNR = 2dB in Learned Perceptual Image Patch Similarity (LPIPS) compared to current state-of-the-art vector quantization (VQ) based digital SemCom systems.

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.

Abstract Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites (CCS) system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control, fundamentally enhancing network resilience. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, new waveform design, grant-free massive access, nonorthogonal multicast, distributed phased array antennas, high-speed inter-satellite communication, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.

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.

With the proliferation of edge computing, efficient artificial intelligence inference on edge devices has become essential for intelligent applications, such as autonomous vehicles and virtual/augmented reality. In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers. We propose an optimization framework to tackle the challenges posed by dynamic channel conditions and device mobility in end-to-end communication systems. Our approach builds upon existing methods by leveraging a semantic knowledge base to drive multilevel feature transmission, accounting for temporal factors, state transitions, and dynamic elements throughout the transmission process. Additionally, we enhance the multilevel feature transmission policy by introducing an additional fifth-level edge-assisted semantic communication, which maximizes recognition performance by leveraging a large semantic knowledge base on the edge server. Formulated as an online optimization problem, our framework aims to simultaneously minimize semantic loss and adhere to specified transmission latency thresholds. To achieve this, we design a soft actor-critic-based deep reinforcement learning system with a carefully designed reward structure for real-time decision making. This approach overcomes the optimization difficulty of the NP-hard problem while fulfilling the optimization objectives. Numerical results showcase the superiority of our approach compared to traditional greedy methods across various system setups using open-source datasets.

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.

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.

In this article, we design an end-to-end digital semantic communication system to transmit semantic symbols that simultaneously facilitate image classification tasks and reconstruction tasks. By training a mutual information-assisted joint source-channel coding (MIJSCC) framework, the learned semantic representation can incorporate both pixel-level generative information for reconstruction and structural discriminative information for classification, which are obtained label-free via global and local mutual information estimation and maximization, as well as mean-square error (MSE) minimization. Then, the high-resolution semantic representation is quantized into finite constellation symbols to satisfy the hardware constraint on discrete control in practical radio frequency systems. Considering dynamic channel conditions in practical communication systems, we further design an adaptive MIJSCC (A-MIJSCC) framework with attention-based semantic enhancement (A-MIJSCC), which allows for the sequential activation of varying dimensions of the semantic representation according to channel signal-to-noise ratio. Compared to existing semantic communication frameworks that are dominated by end target and labels, the MIJSCC addresses the semi-supervised learning of intermediate semantics. Simulation results show that the proposed MIJSCC supports both image classification and reconstruction via task-agnostic semantic extraction, whose performance surpasses the benchmark frameworks. It is also demonstrated that the A-MIJSCC method facilitates the adaptive semantic transmission under varying channel conditions, which effectively reduces the transmission overhead while preserving task performance.

In order to satisfy the requirements for service transformation and upgrading toward industrial digitization, networking, and intelligence, sixth generation-enabled industrial Internet of Things (IIoT) imposes new requirements on deterministic delay. However, the existing best-effort communication networks increase the uncertainty of transmission, making it difficult for users to ensure deterministic delay performance. In this article, we propose a deterministic delay guarantee strategy (DDGS) under reconfigurable intelligent surface (RIS)-assisted communication networks to ensure network performance in IIoT scenarios. In particular, we utilize stochastic network calculus (SNCs) to derive the probability that the delay falls within a specific time window, characterizing the probabilistic bounds of deterministic delay. Then, we explore the relationship between delay determinacy and wireless resources by jointly optimizing the transmit power, the channel blocklength allocation, and the phase-shift matrix at the RIS to maximize delay determinacy. Based on the interdependence of action choices among users and past experience, this article proposes a performance guarantee parameterized deep Q-network (PG-PDQN) algorithm to solve the complex problem containing a mixture of discrete and continuous action spaces. Simulation results show that the DDGS strategy significantly improves the delay determinacy compared to other strategies, and the PG-PDQN algorithm has good convergence, thus effectively improving the network performance.

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.

Semantic communication has drawn substantial attention as a promising paradigm to achieve effective and intelligent communications. However, efficient image semantic communication encounters challenges with a lower testing compression ratio (CR) and signal-to-noise ratio (SNR) compared to the training phase. To tackle this issue, we propose an innovative semantic knowledge base (SKB)-enabled generative semantic communication system for image classification task and image generation task. Specifically, a lightweight SKB, comprising class-level information, is exploited to guide the semantic communication process, which enables us to transmit only the relevant indices. This approach promotes the completion of the image classification task at the transmitter and significantly reduces the transmission load. Meanwhile, the class-level knowledge in the SKB facilitates the image generation task by allowing controllable generation, making it possible to generate class-consistent images in resource-constrained and low SNR scenarios. Furthermore, an adaptive CR and mode selection mechanism is designed to automatically adjust the CR and task mode, which allows the proposed system accommodate various CR and SNR conditions. Evaluation results indicate that the proposed method outperforms the benchmarks and achieves superior performance with minimal CR and SNR.

In this paper, we formulate a Secure Hierarchical Semantic Communication (SH-SC) framework that leverages cloud-edge-device collaboration to enable efficient, robust, and privacy-preserving semantic communications. Firstly, we propose a quantization-aware efficient semantic communication (SemCom) model pre-training scheme running in the cloud. In particular, a semantic quantization method is applied to reduce the data required for transmission, and a quantization-aware multi-splitting points training method is proposed to mitigate the accuracy loss caused by quantization. Secondly, we propose a robust SemCom model deployment strategy in local device and honest but curious edge server for privacy-preserving, where a post-training quantization method on the device is proposed to reduce the computational overhead and enhance privacy preservation. Thirdly, we propose a SemCom model based adaptive device-edge collaborative inferencing mechanism for SemCom quality of AI services (QoAIS), where a Joint Quantization Device-Edge Collaboration Semantic Communication (JQDESC) scheme is formulated. Moreover, we provide a theoretical analysis of the privacy preservation of the proposed quantization scheme against model inversion attack through back-propagation and quantization error accumulation. Experimental results demonstrate that our proposed JQDESC scheme effectively protects privacy under various adversarial capabilities, and has better performance in memory usage and end-to-end latency while maintaining similar accuracy.

Deep learning-based semantic communications (DLSC) leverage deep neural networks in transmitters and receivers, pushing the boundaries beyond Shannon limit. However, DLSC is extremely vulnerable to malicious physical-layer adversarial attacks due to the openness of wireless channels. Meanwhile, existing defense approaches still suffer from two challenges for robust DLSC. First, most methods require offline DLSC retraining to defend against various attacks, causing interruptions of online service. Second, they struggle to achieve effective defense in real-world time-varying channels, thus limiting DLSC reliability. We propose PBNet, integrating a pluggable protector and an adaptive protector to respectively address the above two challenges. First, the pluggable protector utilizes a novel denoising module to safeguard the transmitted signals, enabling hot-pluggable deployment without interrupting communication. Second, the adaptive protector leverages a novel alternating adaption strategy to achieve effective defense in time-varying channels, ensuring robust performances under real-world dynamic conditions. Evaluations involving symbols, images, texts, and speeches show the efficacy of our PBNet, which has respectively achieved an impressive 72.22% and 73.71% accuracy improvement in defending against unknown$l_{0}$-norm and$l_{2}$-norm attacks on image-based DLSC. Furthermore, we developed two real-world radio systems of PBNet to perform over-the-air signal generation, integrating hardware and software such as FPGA chips and GNU radio. We also implemented an interactive UI of PBNet based on QT5, aiming to demonstrate the effect of attacks and defense visually. This work achieves robust DLSC performances under various attacks and time-varying channels, taking a significant step towards the practical defense scheme for robust DLSC.

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.

Terahertz (THz) massive multiple-input multiple-output (MIMO) has recently been expected to address the challenges of extremely high data rates, high reliability and low latency for many future use cases such as immersive communications. This paper investigates the upper bound of the roundtrip interaction delay violation probability (UB-RIDVP) for immersive communications in a THz massive MIMO based communication system through stochastic network calculus (SNC). Specifically, the system design adopts the split rendering introduced in 3GPP TR 26.928, based on which not only the uplink and downlink queuing delays, but also the processing delays at the engine side and the user terminal (UT) side are included in the roundtrip interaction delay of the immersive communications. The traffic arrivals and wireless channels in the uplink and downlink are characterized by carrying out the SNC analysis. We derive the expression of the UB-RIDVP, and solve its parameters through the proposed Solving UB-RIDVP Algorithm. The numerical results show that the theoretical UB-RIDVP can reasonably estimate the trend of its violation probability. When a roundtrip interaction delay bound constraint is given as a performance metric, the proposed analytical approach can be utilized to guide the system design.

In immersive communication, delivering real-time, high-resolution 360-degree panoramic videos imposes extremely high demands on network performance. In this paper, we propose a rate splitting multiple access (RSMA)-enabled adaptive panoramic video semantic transmission (APVST) framework. Specifically, APVST is built based on the deep joint source-channel coding (JSCC) structure and achieves adaptive semantic extraction and variable-length coding of panoramic frames. Additionally, APVST employs an entropy model and a latitude adaptive module to jointly achieve rate control, and utilizes a weight attention module to enhance the panoramic video quality. Given the overlapping field of view (FoV) when users watch panoramic videos, RSMA is integrated into the semantic transmission to further improve system efficiency. Therefore, we introduce an RSMA-enabled semantic stream transmission scheme, and formulate a joint optimization problem for latency and video quality by optimizing power, common rate, and channel bandwidth allocation ratios, aiming to maximize the users’ quality of service (QoS). To address this problem, we develop a deep reinforcement learning (DRL) approach based on the proximal policy optimization (PPO) algorithm, which integrates semantic-level FoV information to effectively adapt to dynamically changing environments. Simulation results indicate that our proposed APVST reduces bandwidth consumption by 20% compared to semantic video transmission schemes and 45% compared to traditional ones. Furthermore, our research validates the effectiveness of RSMA in panoramic video semantic transmission, demonstrating QoS improvements of up to 20% compared to other multiple access schemes.

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).

Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, but how wireless networks can support LLMs has not been extensively studied. In this paper, we propose a wireless distributed LLMs paradigm based on Mixture of Experts (MoE), named WDMoE, through server-device collaboration at the wireless network edge. Specifically, we decompose the MoE layer in LLMs by deploying the gating network and the preceding neural network layer at the edge server of the base station (BS), while distributing the expert networks across the mobile devices. This arrangement leverages the parallel capabilities of expert networks on distributed devices. Moreover, to overcome the instability of wireless communications, we design an expert selection policy by taking into account both the performance of the model and the end-to-end latency, which includes both transmission delay and inference delay. Evaluations conducted across various LLMs and multiple datasets demonstrate that WDMoE not only outperforms existing models, such as Llama 2 with 70 billion parameters, but also significantly reduces end-to-end latency.

Semantic communication has emerged as a promising communication paradigm and there have been extensive research focusing on its applications in the increasingly prevalent multi-user scenarios. However, the knowledge discrepancy among multiple users may lead to considerable disparities in their performance. To address this challenge, this paper proposes a novel multi-pair cooperative semantic knowledge base (SKB) update policy. Specifically, for each pair endowed with SKB-enabled semantic communication, its well-understood knowledge in the local SKB is selected out and uploaded to the server to establish a global SKB, via a score-based knowledge selection scheme. The knowledge selection scheme achieves a balance between the uplink transmission overhead and the completeness of the global SKB. Then, with the assistance of the global SKB, each pair’s local SKB is refined and their performance is improved. Numerical results show that the proposed cooperative SKB update policy obtains significant performance gains with minimal transmission overhead, especially for the initially poor-performing pairs.

Lightweight and efficient neural network models for deep joint source-channel coding (JSCC) are crucial for semantic communications. In this paper, we design a novel JSCC scheme named MambaJSCC, which utilizes a visual state space model with channel adaptation (VSSM-CA) block as its backbone for transmitting images over wireless channels. The VSSM-CA block utilizes VSSM to integrate images with the state space, enabling feature extraction and encoding processes to operate with linear complexity. It also incorporates channel state information (CSI) via a newly proposed CSI embedding method. This method deploys a shared CSI encoding module within both the encoder and decoder to encode and inject the CSI into each VSSM-CA block, improving the adaptability of a single model to varying channel conditions. Experimental results show that MambaJSCC not only outperforms Swin Transformer based JSCC (SwinJSCC) but also significantly reduces parameter size, computational overhead, and inference delay (ID). In particular, with employing an equal number of the VSSM-CA blocks and the Swin Transformer blocks, MambaJSCC achieves a 0.48 dB gain in peak-signal-to-noise ratio (PSNR) while requiring only 53.3% multiply-accumulate operations, 53.8% of the parameters, and 44.9% of ID.

Vector quantization-based image semantic communication systems have successfully boosted transmission efficiency, but face challenges with conflicting requirements between code-book design and digital constellation modulation. Traditional codebooks need wide index ranges, while modulation favors few discrete states. To address this, we propose a multilevel generative semantic communication system with a two-stage training framework. In the first stage, we train a high-quality codebook, using a multi-head octonary codebook (MOC) to compress the index range. In addition, a residual vector quantization (RVQ) mechanism is also integrated for effective multilevel communication. In the second stage, a noise reduction block (NRB) based on Swin Transformer is introduced, coupled with the multilevel codebook from the first stage, serving as a high-quality semantic knowledge base (SKB) for generative feature restoration. Finally, to simulate modern image transmission scenarios, we employ a diverse collection of high-resolution 2K images as the test set. The experimental results consistently demonstrate the superior performance of MOC-RVQ over conventional methods such as BPG or JPEG. Additionally, MOC-RVQ achieves comparable performance to an analog JSCC scheme, while needing only one-sixth of the channel bandwidth ratio (CBR) and being directly compatible with digital transmission systems.

In this paper, we investigate the computation offloading problem in a distributed reconfigurable intelligent surface (RIS)-aided cell-free network, where users offload computing-intensive tasks to their associated base stations with the aid of multiple RISs. To minimize the long-term energy consumption of all users under the constraints of latency tolerance, we formulate a long-term non-convex problem by jointly optimizing the offloading strategy, transmit power at users, reflection matrix at the RIS, receive beamforming at the BS, and CPU resources at both users and the server. To solve this intractable time-varying problem with multiple coupling variables, we propose a two-layer distributed proximal policy optimization (DPPO) algorithm for solving the problem with a high-dimensional decision-making space. Simulation results show that the proposed algorithm effectively reduces the long-term energy consumption of all users while completing the computing task within a given time limit.

With the proliferation of edge computing, efficient AI inference on edge devices has become essential for intelligent applications such as autonomous vehicles and VR/AR. In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers. We propose an online optimization framework to address the challenge of dynamic channel conditions and device mobility in an end-to-end communication system. Our approach builds upon existing methods by leveraging a semantic knowledge base to drive multi-level feature transmission, accounting for temporal factors and dynamic elements throughout the transmission process. To solve the online optimization problem, we design a novel soft actor-critic-based deep reinforcement learning system with a carefully designed reward function for real-time decision-making, overcoming the optimization difficulty of the NP-hard problem and achieving the minimization of semantic loss while respecting latency constraints. Numerical results showcase the superiority of our approach compared to traditional greedy methods under various system setups.

Cross-technology communication (CTC) is a promising technique that enables direct communications among incompatible wireless technologies without needing hardware modification. However, it has not been widely adopted in real-world applications due to its inefficiency and unreliability. To address this issue, this paper proposes a deep joint source-channel coding (DJSCC) scheme to enable efficient and reliable CTC. The proposed scheme builds a neural-network-based encoder and decoder at the sender side and the receiver side, respectively, to achieve two critical tasks simultaneously: 1) compressing the messages to the point where only their essential semantic meanings are preserved; 2) ensuring the robustness of the semantic meanings when they are transmitted across incompatible technologies. The scheme incorporates existing CTC coding algorithms as domain knowledge to guide the encoder-decoder pair to learn the characteristics of CTC links better. Moreover, the scheme constructs shared semantic knowledge for the encoder and decoder, allowing semantic meanings to be converted into very few bits for cross-technology transmissions, thus further improving the efficiency of CTC. Extensive simulations verify that the proposed scheme can reduce the transmission overhead by up to 97.63% and increase the structural similarity index measure by up to 734.78%, compared with the state-of-the-art CTC scheme.

This paper studies a multi-static integrated sensing and communications (ISAC) system for multi-user downlink communications and cooperative target sensing, where the base station transmits ISAC signal and the users deployed at different positions receive it. We analyze the joint estimation of transmission delay and Doppler shift of cooperative users for target sensing, and derive the corresponding Cramér-Rao bound (CRB) in closed form. Then, a CRB minimization problem is formulated by considering the cooperative cost and communication rate requirements among these users. To solve this problem, we propose a minimax linkage-based cooperative method for user selection, and then design an approximation non-convex transforming algorithm for transmit beamforming. Finally, simulation results validate our analysis and reveal significant performance improvement of our proposed methods over the state-of-the-art benchmarks.

In this paper, we investigate a distributed semantic communication system where each device utilizes the semantic model for intelligent tasks. Due to the challenges associated with heterogeneity of federated learning framework, we propose a semantic feature-based learning framework (SFLF) for task-oriented semantic communication. Additionally, we define the global task score to evaluate the task execution performance of whole devices covered by the edge server and formulate a global task score maximization problem under energy constraints. For such framework, the edge server aggregates the semantic features extracted from devices and broadcasts global semantic feature to devices. The devices utilize their local data and the received global semantic feature to train their semantic models by the task loss function and semantic loss function. Besides, since the practical distributed system is usually energy-limited, we propose a distributed proximal policy optimization (DPPO)-based scheduling algorithm to adjust energy consumption in real-time during the training process. Simulation results demonstrate that the proposed SFLF with the DPPO-based device scheduling algorithm outperforms the existing schemes.

In this paper, we propose an adaptive panoramic video semantic transmission (APVST) network built on the deep joint source-channel coding (Deep JSCC) structure for the efficient end-to-end transmission of panoramic videos. The proposed APVST network can adaptively extract semantic features of panoramic frames and achieve semantic feature encoding. To achieve high spectral efficiency and save bandwidth, we propose a transmission rate control mechanism for the APVST via the entropy model and the latitude adaptive model. Besides, we take weighted-to-spherically-uniform peak signal-to-noise ratio (WS-PSNR) and weighted-to-spherically-uniform structural similarity (WS-SSIM) as distortion evaluation metrics, and propose the weight attention module to fuse the weights with the semantic features to achieve better quality of immersive experiences. Finally, we evaluate our proposed scheme on a panoramic video dataset containing 208 panoramic videos. The simulation results show that the APVST can save up to 20% and 50% on channel bandwidth cost compared with other semantic communication-based and traditional video transmission schemes.

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.

Recently, there has been a growing interest in Semantic Communication (SemCom) frameworks that aim to enhance intelligent communications by exploiting the intended meaning of transmitted information. In this context, some researchers have introduced federated learning (FL) to train semantic models effectively and efficiently, while keeping private data on the respective devices. However, publicly sharing model updates during co-training in SemCom can potentially lead to the privacy leakage. To address this issue, this paper conducts membership inference attacks (MIA) against FL-based SemCom co-training processes. Through experiments, we discover instances of privacy leakage, with the rate of leakage varying as the models converge during training. Based on these findings, we propose the Adaptive Privacy Budget-based Differential Privacy (APB-DP) method for secure and effective semantic model training. APB-DP utilizes differential privacy (DP) to safeguard against MIA by introducing artificial noise during the training process, while also dynamically adapting the privacy budget (i.e., the level of noise) as the models converge. This ensures that the privacy protection remains effective throughout the training process. On the other hand, APB-DP takes into account the impact of wireless channels to prevent unnecessary interference. Simulation results show that APB-DP significantly reduces privacy leakage rate by 13% compared to FL-based SemCom. Additionally, it reduces performance loss rate by 71% compared to the state-of-the-art DP-based model training scheme known as NbAFL.

We investigate the joint communication and computation problem in non-orthogonal multiple access based mobile edge computation networks for massive intelligent machine type communication. We model the whole task offloading process as a double tandem queues model and formulate an optimization problem to maximize effective energy efficiency while guaranteeing the End-to-End delay violation probability. To solve this problem, we propose a Joint Transmission Power allocation and Computation Resources allocation (JTPCR) algorithm. Specifically, we first minimize the task processing delay to obtain the optimal computation resource constrained by total computation resources and maximum tolerable delay. In addition, we exploit the Dinkelbach method to solve the fractional programming problem when optimizing the transmission power. We introduce an auxiliary variable and obtain the lower bounding concave approximation of channel capacity through a path-following method. Finally, we propose the alternating direction method of the multipliers to obtain the optimal transmission power. Simulation results show that the proposed JTPCR algorithm outperforms the comparison schemes under both scenarios: finite transmission blocklength and infinite transmission blocklength.

Deep neural network (DNN) is a promising technology for Internet of Visual Things (IoVT) devices to extrct their visual information from unstructured data. However, it is hard to deploy a complete DNN model at resource-constrained IoVT devices to fulfill their latency, energy, and inference accuracy demands. Exploiting the reachable and available computing resources of IoVT devices and mobile-edge computing (MEC) servers, we propose an edge-device collaborative DNN inference framework to empower resource-constrained IoVT devices to perform DNN-based inference. Especially, the DNN model partition separates the DNN model into two parts, which are deployed on both the IoVT devices and multiaccess MEC server for performing inference collaboratively. The DNN early exit and computation resource allocation are employed to accelerate the DNN inference while guaranteeing the inference accuracy. Moreover, a metric to measure the inference performance of average latency and accuracy (IPLA) is designed. Joint multiuser DNN partitioning, early exit point selection, and computation resource allocation are optimized to maximize the tradeoff performance of inference latency and accuracy. We model the optimized problem as an Markov decision process and propose a deep deterministic policy gradient-based edge-device collaborative DNN inference algorithm to solve the problem of huge state space and high-dimensional continuous actions. Experiments are conducted with the Alexnet model on the data set of CIFAR-10 and Resnet-50 model on the data set of ImageNet. Simulation results verify that the proposed algorithm speeds up the overall inference execution of IoVT devices while guaranteeing inference accuracy.

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.

Reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) has recently emerged as a promising candidate to improve the energy and spectral efficiency of Internet of Things (IoT) systems. This paper aims to develop an efficient channel estimation scheme for RIS-assisted MIMO IoT systems within structured Bayesian learning framework. However, the high-dimensional channel matrix with considering its underlying structured sparsity makes efficient channel estimation scheme design a challenging task. To deal with it, we firstly formulate the cascaded RIS-assisted MIMO channel estimation as a generic sparse signal recovery problem with considering the constructed two-level block-structured sparsity of channels. Secondly, we design a flexible prior model to characterize such structured sparsity of channels, in which hierarchical hyperparameters are introduced, and the iterative Bayesian learning-based method is developed to autonomously estimate channels and the hyperparameters associated with the prior model. Thirdly, to relieve the high-computational complexity involving matrix inversion when calculating the posterior of channels, we develop efficient methods from two perspectives. On the one hand, an inverse-free method is developed by relaxed evidence lower bound (ELBO) maximization with an adjustable factor of reducing the gap between the standard ELBO and relaxed ELBO. On the other hand, a method of reducing the dimension of sparse representation matrix aided by external block-structured sparsity is developed. Finally, the computational complexity and convergence properties of the proposed methods are analyzed in detail. Simulation results are provided to verify the superiority of the devised channel estimation methods.

With the explosive growth of communication requirements for real-time intelligent tasks, mobile communication is shifting from the traditional communication to task-oriented communication, where the transmitted data is shifting from undifferentiated transmission to value-oriented transmission. To maximize the value of transmitted data, it is urgent to match the source decisions with the task demands and wireless channel state. In this article, we focus on the joint source-channel optimization problem in task-oriented communication, and we design the timeliness-accuracy degradation (TAD) metric to measure the value of transmitted source. Moreover, we design a source value-based resource allocation scheme to minimize the TAD through joint optimization of task data generation and compression strategies, bandwidth allocation, and transmit power selection. Furthermore, to avoid the curse of dimensionality, we propose dimension-refined reinforcement learning (DRRL) algorithm to obtain the optimal solution of the problem in a stable and low-complexity manner. Numerical results demonstrate that the designed scheme can effectively improve the task performance and verify the low complexity and stability of the algorithm.

In a hybrid scenario of 5G and Industrial Internet of Things (IIoT), there is a lack of a theoretical tool to analyze probabilistic end-to-end (E2E) delay. In this article, we provide a comprehensive procedure, which is based on stochastic network calculus (SNC) with moment-generating functions (MGFs), for calculating the E2E delay violation probability for the target traffic in IIoT. The particularity of the scenario is that the E2E network is composed of two segments: 1) the industrial wireless link challenged by the complex fading channel and 2) the multinode wired network that supports common schedulers. An improved concatenation theorem is proposed to calculate the service capability of the E2E network, and a method based on Meijer G-functions is proposed to calculate the MGF for the service processes of various wireless fading channels. We investigate the impacts of various resource allocation strategies (on both wireless and wired networks) and parameters (e.g., bandwidth, weight, and cycle time) on probabilistic E2E delay and provide numerical performance bounds. We show that the capability joint adaptation of the wireless and wired networks is the key to E2E service guarantee. Moreover, related parameters such as the weights and message sizes should be carefully considered to improve E2E delay.

Mobile-edge computing (MEC) networks bring computing and storage capabilities closer to edge devices, which reduces latency and improves network performance. However, to further reduce transmission and computation costs while satisfying user-perceived quality of experience, a joint optimization in computing, pushing, and caching is needed. In this article, we formulate the joint-design problem in MEC networks as an infinite-horizon discounted-cost Markov decision process and solve it using a deep reinforcement learning (DRL)-based framework that enables the dynamic orchestration of computing, pushing, and caching. Through the deep networks embedded in the DRL structure, our framework can implicitly predict user future requests and push or cache the appropriate content to effectively enhance system performance. One issue we encountered when considering three functions collectively is the curse of dimensionality for the action space. To address it, we relaxed the discrete action space into a continuous space and then adopted soft actor–critic learning to solve the optimization problem, followed by utilizing a vector quantization method to obtain the desired discrete action. Additionally, an action correction method was proposed to compress the action space further and accelerate the convergence. Our simulations under the setting of a general single-user, single-server MEC network with dynamic transmission link quality demonstrate that the proposed framework effectively decreases transmission bandwidth and computing cost by proactively pushing data on future demand to users and jointly optimizing the three functions. We also conduct extensive parameter tuning analysis, which shows that our approach outperforms the baselines under various parameter settings.

The wireless control system (WCS) operating with massive ultra-reliable and low-latency communications is viewed as a promising technology for the Industrial Internet of Things (IIoT). However, the co-design of sensing, control, and communications is full of challenges, and the trade-off between reliability and control performance in a closed-loop WCS under blind areas of the wireless network’s coverage is still to be solved. In this paper, we exploit reconfigurable intelligence surface (RIS) to assist the device located in the blind areas of the wireless network’s coverage in transmitting sensing and control information over the wireless channels. Furthermore, we formulate a joint optimization of reliability and control cost for a RIS-assisted closed-loop WCS. Specifically, we apply a linear quadratic regulator (LQR) cost to measure the control performance, and a trade-off performance metric called reliability-to-control efficiency (RCE) is proposed for the WCS. In addition, we maximize the minimum RCE of IIoT devices while meeting the requirements of reliability, control cost, and communication resources. An alternating optimization-based maximum the minimum RCE algorithm (AO-MmRCEA) is formulated to jointly optimize the transmission power, transmission time, beamforming, and reflecting coefficients of RIS. The convergence and complexity of the proposed AO-MmRCEA algorithm are analyzed. Simulation results demonstrate the convergence and effectiveness of the proposed AO-MmRCEA algorithm for closed-loop WCS.

This article proposes a secrecy and energy-efficient device-edge co-inference scheme for resource-constrained Artificial Intelligence of Things (AIoT) devices with physical layer security assistance. Our approach leverages split learning, where the AIoT device executes the initial part of the AI model, and the mobile edge computing server (MECs) computes the remainder, reducing energy consumption (EC) and inference delay. We measure secrecy capacity under the finite blocklength regime to address the vulnerability of intermediate feature data (IFD) to eavesdropping over wireless channels and its short block length characteristics. The objective is to minimize the average EC of the device-edge co-inference by jointly optimizing deep neural network (DNN) model partitioning and resource allocation. We formulate a distributed reinforcement learning-based joint DNN model partitioning and resource allocation (DRPA) algorithm, which uses knowledge-based reinforcement learning for optimal DNN partitioning and a convex optimization approach for resource allocation. Simulation results demonstrate that the DRPA algorithm achieves near-optimal performance, closely matching the results of exhaustive search methods.

As the proliferation of sophisticated task models in 5G empowered digital twin, it yields significant demands on fast and accurate model training over resource-limited wireless networks. It is vital to investigate how to accelerate the training process based on the salient features of practical systems, including heterogeneous data distributions and system resources both across devices and over time. To study the non-trivial coupling between participating device selection and their appropriate training parameters, we first characterize the dependency of convergence performance bound on system parameters, i.e., statistical structure of local data, mini-batch size and gradient quantization level. Based on the theoretical analysis, a training efficiency optimization problem is formulated subject to heterogeneous communication and computation capabilities among devices. To realize online control of training parameters, we propose an adaptive batch-size assisted device scheduling strategy, which prioritizes the selection of devices that offer good data utility and dynamically adjust their mini-batch sizes and gradient quantization levels adapting to network conditions. Simulation results demonstrate our proposed strategy can effectively speed up the training process as compared with benchmark algorithms.

The randomness and complexity of the wireless channel is challenging to meet the various quality of service (QoS) for different wireless communication application scenarios. Reconfigurable intelligent surface (RIS) technology has been proposed to achieve dynamic control of signal propagation over the wireless medium, and thus enables intelligent reconstruction of the channel environment. Moreover, age of information (AoI) has been proposed to quantify the timeliness of status update information accurately, which is new QoS metric. However, the AoI-driven statistical QoS guarantee problem in the RIS-enabled wireless network is not trivial and needs to be solved. In this paper, we employ the AoI violation probability to measure the reliability requirement for maintaining the freshness of status updates and derive its upper bound. Then, we formulate the AoI-driven effective capacity maximization problem. Finally, we transform the formulated problem into a signal-to-noise ratio (SNR) maximization problem, and further propose a twin delayed deep deterministic policy gradient (TD-DDPG) based joint optimization algorithm for obtaining the effective decisions on transmission power of device and the phase shift of RIS. Simulation results show that the TD-DDPG-based scheme has better performance than other traditional schemes.

The future 6G systems are expected to achieve intelligent connection and interaction between various heterogeneous terminals, increasing the fragility for spoofing attacks. Due to the high security and energy efficiency, Physical Layer Authentication (PLA) has been regarded as a powerful method to verify the identity of devices. Nevertheless, due to the inaccurate identifying fingerprints caused by the imperfect estimation and variations of the limited fingerprints, most of the state-of-the-art PLA schemes have low reliability and robustness in low Signal-Noise Ratio (SNR) environments. Besides, most PLA schemes rely on the prior knowledge of attackers to establish authentication models, thus reducing the feasibility of actual communications. To address the first challenge, we propose a multi-attacker detection architecture based on multi-dimensional fingerprints, which can provide more robust identifiable spatial attributes for devices by using fingerprints observed by receivers in multi-locations. Upon the designed detection architecture, to tackle the second issue, we propose four clustering-based PLA schemes without requiring their training fingerprint sets. Considering that the aforementioned schemes can divide fingerprints from different transmitters into several disjoint clusters but can not precisely identify forged fingerprints, we further propose the graph learning-based PLA approaches with only a few labeled fingerprints. The simulation results on real industrial outdoor and indoor datasets demonstrate the superiority of the designed detection system in Adjusted Mutual Information (AMI) and authentication accurate rate (AucRate) over the single observation-based PLA schemes.

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.

Symbiotic radio (SR) is a promising technology to support ultralow-power or even zero-power Internet of Things (IoT) devices in the sixth-generation mobile networks. In this article, we propose an energy-aware symbiotic transmission in a reconfigurable intelligent surface (RIS) enhanced SR system, in which an IoT network embeds its own data passively over cellular downlink signals by backscattering. The base station (BS) serves multiple cellular users (CUs) through time division multiple access (TDMA) and each IoT device is associated with one CU. We formulate the BS’s energy minimization problem subject to the constraints of the minimum amounts of transmission bits required by IoT devices and CUs. The user association, the active transmit beamforming at the BS, the passive reflecting beamforming at the RIS, and the frame division policy are jointly optimized. The formulated problem is a mixed integer nonlinear programming (MINLP) problem, which is NP-hard and nonconvex. We decouple the problem and solve the subproblems alternatively. First, we design a many-to-one swap-matching-based algorithm to solve the user association subproblem. Then, we develop a joint cooperative beamforming and time allocation optimization algorithm based on the alternative optimization (AO) and semidefinite relaxation (SDR) techniques. Simulation results show that the proposed joint user association and cooperative beamforming algorithm brings significant performance gain in reducing the energy consumption of the BS with fast convergence speed compared with other schemes.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted uplink non-orthogonal multiple access (NOMA) framework for finite blocklength (FBL) transmission is proposed. Considering the different communication requirements of Internet of Things devices (IoTDs), a novel design to achieve high-rate and low-error is proposed. Two operating protocols for STAR-RIS are considered, namely energy splitting (ES) and mode switching (MS). 1) For STAR-RIS with ES, an alternating optimization (AO) algorithm is proposed to handle the highly-coupled mixed integer programming problem. More particularly, a low-complexity received-signal-strength-based device pairing scheme is proposed. Based on the given device pair, the closed-form solutions for the power allocation problem are obtained. The transmitting and reflecting coefficient optimization problem is solved by exploiting the successive convex approximation and semidefinite relaxation methods. 2) For STAR-RIS with MS, a double-layer penalty-based (DLPB) algorithm is proposed to tackle the newly introduced binary amplitude constraints. Numerical results reveal that: i) the proposed AO and DLPB algorithms can converge within a few iteration times; ii) the FBL transmission performance can be improved by employing the proposed STAR-RIS framework compared with conventional transmitting/reflecting-only RISs; iii) NOMA is capable of enhancing FBL rate while guaranteeing the reliability constraints compared with orthogonal multiple access.

In order to truly promote the further development of the Industrial Internet of Things (IIoT), terminal authentication of the IIoT is essential. Physical-layer authentication (PLA) has recently attracted much attention for its high security and lightweight. Nevertheless, most existing PLA schemes in conjunction only the observation of a single receiver will lead to low-reliability and low-robustness of authentication, especially in hostile time-varying wireless channels. To tackle this issue, we developed a multiobservation-multichannel-attribute (MOMCA) based multiuser authentication architecture, which considers both the observations of multireceivers and multiple channel attributes of each observation to enhance wireless security. Specifically, the proposed architecture can provide additional spatial recognition characteristics for multiusers. To better fit the channel features of multiobservations, we proposed two gradient boosting optimization-based schemes. One uses Taylor expansion to approximate objective functions and adds the regularization term to avoid overfitting issues. The other can obtain higher authentication performance by sampling the signal data with small gradient characteristics. The simulations on real industrial indoor and outdoor datasets verify the superiority of the proposed schemes in authentication accuracy over six baseline authentication schemes.

Deep learning (DL) models have achieved remarkable success in various domains. But training an accurate DL model requires large amounts of data, which can be challenging to obtain in medical settings due to privacy concerns. Recently, federated learning (FL) has emerged as a promising solution that shares local models instead of raw data. However, FL in medical settings faces challenges of client drift due to the data heterogeneity across dispersed institutions. Although there exist studies to address this challenge, they mainly focus on the classification tasks that learn global representation of an entire image. Few have been studied on the dense prediction tasks, such as object detection. In this study, we propose dense contrastive-based federated learning (DCFL) tailored for dense prediction tasks in FL settings. DCFL introduces dense contrastive learning to FL, which aligns the local optimization objectives towards the global objective by maximizing the agreement of representations between the global and local models. Moreover, to improve the performance of dense target prediction at each level, DCFL applies multi-scale contrastive representation by utilizing multi-scale representations with dense features in contrastive learning. We evaluated DCFL on a set of realistic datasets for pulmonary nodule detection. DCFL demonstrates an overall performance improvement compared with the other federated learning methods in heterogeneous settings-improving the mean average precision by 4.13% and testing recall by 6.07% in highly heterogeneous settings.

The continuous aperture of Holographic MIMO enables us to encode and transmit information spatially. This paper investigates the achievable rate of linear Holographic MIMO with arbitrary aperture-length using the finite blocklength information theory. Specifically, we first employ the prolate spheroidal wave functions to expand the received wavenumber band-limited electromagnetic field. This orthogonal representation enables two schemes to convey information related to the normal additive white Gaussian noise (AWGN) channel and the non-normal AWGN channel, namely the NA and NNA schemes, respectively. Then we derive the accurate achievable rates and the converse bounds of the two schemes by extending the$\kappa \beta $bound in finite blocklength information theory. Moreover, we derive an approximate closed-form expression of the achievable rate in the large aperture-length regime based on normal approximation. The approximation indicates that for a given space efficiency, the error probability decreases rapidly as the aperture length L increases, with the rate of decline determined by$Q\left ({{O\left ({{\sqrt {L}}}\right)}}\right)$. Finally, we obtain the asymptotic results when the aperture-length tends to infinity. Numerical results demonstrate that the NA scheme outperforms the NNA scheme when the blocklength is small, while the NNA scheme excels in the large blocklength regime. The accuracy of the approximation and the validity of the asymptotic results are verified.

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.

Downlink reconfigurable intelligent surface (RIS)-assisted multi-input-multi-output (MIMO) systems are considered with far-field, near-field, and hybrid-far-near-field channels. According to the angular or distance information contained in the received signals, 1) a distance-based codebook is designed for near-field MIMO channels, based on which a hierarchical beam training scheme is proposed to reduce the training overhead; 2) a combined angular-distance codebook is designed for hybrid-far-near-field MIMO channels, based on which a two-stage beam training scheme is proposed to achieve alignment in the angular and distance domains separately. For maximizing the achievable rate while reducing the complexity, an alternating optimization algorithm is proposed to carry out the joint optimization iteratively. Specifically, the RIS coefficient matrix is optimized through the beam training process, the optimal combining matrix is obtained from the closed-form solution for the mean square error (MSE) minimization problem, and the active beamforming matrix is optimized by exploiting the relationship between the achievable rate and MSE. Numerical results reveal that: 1) the proposed beam training schemes achieve near-optimal performance with a significantly decreased training overhead; 2) compared to the angular-only far-field channel model, taking the additional distance information into consideration will effectively improve the achievable rate when carrying out beam design for near-field communications.

Remote zero-shot object recognition, which involves offloading the zero-shot recognition task from one mobile device to a remote mobile edge computing (MEC) server or another mobile device, is crucial for 6G. To address this challenge, this paper presents a lightweight semantic knowledge base (SKB)-enabled multi-level feature extractor that projects the image into visual, semantic, and intermediate feature spaces. Then, this paper proposes a novel SKB-enabled multi-level feature transmission framework, which utilizes SKB and multi-level feature extractor at both transmitter and receiver. The semantic loss and required transmission latency at each level are characterized, and a multi-level feature transmission optimization problem is formulated to minimize the semantic loss under transmission latency constraint. However, this optimization problem is a multi-choice knapsack problem, which is challenging to solve optimally. To overcome this issue, an enhanced convex concave procedure is proposed to obtain an efficient solution. Furthermore, this paper theoretically analyzes the effects of SKBs on the communication performance when the feature extractors at both ends are the same. Numerical results demonstrate that the proposed design outperforms the benchmarks and provide insights into the impact of SKBs at both ends on performance as well as the tradeoff between transmission latency and zero-shot classification accuracy.

To support future 6G mobile applications, the mobile edge computing (MEC) network needs to be jointly optimized for computing, pushing, and caching to reduce transmission load and computation cost. To achieve this, we propose a framework based on deep reinforcement learning that enables the dynamic orchestration of these three activities for the MEC network. The framework can implicitly predict user future requests using deep networks and push or cache the appropriate content to enhance performance. To address the curse of dimensionality resulting from considering three activities collectively, we adopt the soft actor-critic reinforcement learning in continuous space and design the action quantization and correction specifically to fit the discrete optimization problem. We conduct simulations in a single-user single-server MEC network setting and demonstrate that the proposed framework effectively decreases both transmission load and computing cost under various configurations of cache size and tolerable service delay.

Remote zero-shot object recognition, i.e., offloading zero-shot object recognition tasks from one mobile device to remote mobile edge computing (MEC) server or another mobile device, has become a common and important task to conquer for 6G. With this goal, this paper first establishes a zero-shot multi-level feature extractor, which projects the image into the visual, semantic, as well as intermediate feature space in a lightweight way. Then, this paper proposes a novel multi-level feature transmission framework powered by a semantic knowledge base (SKB), and characterizes the semantic loss and required transmission latency at each level. Under this setup, this paper formulates the multi-level feature transmission optimization problem to minimize the semantic loss under the end-to-end latency constraint. Such a problem, however, is a multi-choice knapsack problem, and thus very difficult to solve. To resolve this issue, this paper proposes an efficient algorithm based on the convex concave procedure to find an efficient solution. Numerical results show that the proposed design outperforms the benchmarks, and illustrate the tradeoff between the transmission latency and zero-shot classification accuracy, as well as the effects of the SKBs at both the transmitter and receiver on classification accuracy.

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.

This paper considers smart-duplex (SD) powered user-centric ultra dense networks (UC-UDN), which shifts the conventional access point-centric paradigm to the user-centric one by de-cellular concept, to provide good quality-of-service for a large number of users via flexibly designing the user association, power allocation, and duplex mode. The maximization average ratio of satisfied users for the considered SD UC-UDN in the long-term time scale is firstly formulated as a Markov decision process (MDP) problem with large discrete action space. To reduce the action space, the user association and power allocation processes are modeled as a two-layer tree structure, and then selecting an action is equivalent to finding the path from root to one of the leaf nodes of the tree. A multi-agent tree-structured policy gradient (MATSPG) based deep reinforcement learning (DRL) algorithm is proposed to solve this problem by directly mapping the action space for user association and power allocation to the two layers of the tree, respectively, whose training is shown to be equivalent to the training of neural networks on two-layer paths. The time and space complexity for searching one action in the proposed MATSPG is also proved to be lower than other conventional DRL algorithms. Simulations show that the proposed MATSPG algorithm significantly improves the average ratio of the satisfied users than the conventional DRL methods in typical scenarios.

An adaptive next generation multiple access (NGMA) downlink scheme is provided, where non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) users are served with the same orthogonal time and frequency resource to address the energy constraints and massive connectivity issues of Internet-of-Things networks. Based on this scheme, the long-term power-constrained sum rate maximization problem is investigated, where beamforming, power allocation, and user clustering are jointly optimized, subject to a long-term total power constraint. To solve the formulated problem, a spatial correlation-based user clustering approach is proposed and a resource allocation algorithm is designed based on the trust region policy optimization (TRPO) algorithm, which demonstrates stable convergence under large learning rates. Numerical results verify that the sum rate of the proposed NGMA scheme outperforms the conventional NOMA and SDMA schemes. Moreover, the spatial correlation-based clustering algorithm achieves an increasing sum rate gain compared to the channel correlation-based baseline algorithm as the spatial correlation in the channel model increases.

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.

This paper investigates the uplink secure transmission in an intelligent reflecting surface (IRS) aided Cell-Free Multiple Input Multiple Output network. To maximize the minimum secrecy rate (SR) among legitimate users, we jointly optimize the uplink power control vector and the passive beamforming vector at IRS with consideration of resource allocation fairness. We propose an alternating optimization based SR max-min fairness algorithm to solve the non-convex problem. Based on semidefinite relaxation, the sub-problem of phase optimization at IRS is solved. Geometric programming is utilized to handle the optimization of power control with the assist of condensation method. Simulation results verify that the proposed algorithm can converge to obtain the solution. The minimum SR of the proposed scheme is increased by 14% compared with random phase scheme and the max-min fairness among users is realized.

Space-Air-Ground Integrated Network (SAGIN) has recently attracted extensive attention as a new type of network architecture, which can meet the ever-increasing demands of users for ubiquitous access. However, due to different coverage performance of various networks and the demands of huge capacity in ultra-dense regions, frequent passive group handover will occur, thereby decreasing the quality of service (QoS) and causing signaling storms. To tackle this problem, we introduce low Earth orbit (LEO) satellites, high-altitude platforms and ground base stations to cover ultra-dense regions. The mobility management functions are configured in LEO satellites, which serve as a central controller and compute the average utility based on QoS. We propose an evolutionary game-based vertical handover scheme, where the users covered by SAGINs are modeled as players to compete limited network resources. Simulation results verified the effectiveness of the proposed scheme in meeting the QoS while improving the utility of networks.

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 integration of unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) is a promising solution to provide flexibility in deploying the networks while reconstructing the wireless propagation environment proactively and cost effectively. We propose a UAV-RIS-assisted finite blocklength transmission framework for machine-type communications (MTCs), where downlink nonorthogonal multiple access (NOMA)-based coordinated multipoint (CoMP) is considered to mitigate intercell-interference and improve cell-edge transmission performance. Considering the cell-edge performance, we aim to maximize the minimum achievable rate of cell-edge devices (CEDs) by jointly optimizing the base stations’ transmission power allocation ratio, subchannel-device matching scheme, RIS reflecting coefficient, and UAV trajectory. To solve the highly coupled nonconvex optimization problem, we propose a double-layer alternating optimization algorithm for maximizing the minimum rate (DLAO-MM) in an iterative manner. Specifically, in theinner layer, we first derive the closed-form solution of power allocation, the propose a low-complexity priority-based subchannel-device matching scheme, and finally solve the RIS phase optimization subproblem. In theouter layer, we propose a successive convex approximation (SCA)-based optimization algorithm for the UAV trajectory planning subproblem. The convergence and effectiveness of the proposed DLAO-MM scheme for UAV-RIS-aided CoMP transmission are evaluated by simulations, which show that: 1) the proposed DLAO-MM scheme is capable of improving the cell-edge performance compared to the benchmark schemes; 2) the combination of UAV and RIS improves the cell-edge performance compared with RIS deployed in a fixed location; and 3) adopting the NOMA scheme in the UAV-RIS-aided CoMP system achieves a higher minimum CED rate than orthogonal multiple access.

Recently, learning (DL)-based physical-layer authentication (PLA) has attracted much attention since artificial neural networks (ANNs) can be built to extract useful features from complex wireless environments, thus achieving high authentication performance and lightweight deployment in mobile edge computing (MEC)-Industrial Internet of Things (IIoT) scenario. However, the low latency characteristic of MEC makes it impossible to have much time to obtain sufficient signals for training the authentication system, which will cause over-fitting issues and deteriorate the authentication performance. Data augmentation is an effective method to address this problem. However, existing PLA with data augmentation can not generate representative and high-quality samples, consequently lacking generality in the actual identity authentication. To tackle this problem, a novel channel impulse response (CIR)-based multiuser authentication named latent perturbed neural networks (LPNNs) is proposed in this article, aiming at achieving high authentication performance even when trained a few data. Instead of relying on the generation of synthetic samples, the proposed LPNN adds Gaussian noise in the smooth latent space to avoid underdetermined and poor generalization, which has better interpretability. Specifically, to obtain a better understanding than a black box that connects input CIRs to authentication results, we defined Fingerprint Library and provided post-hoc explanations to answer the following question: which library examples explain the authentication results issued for a given CIR sample? Moreover, the simulations under the static and dynamic IIoT scenarios verify the superiority in authentication accuracy of the proposed LPNN over vanilla deep neural network (DNN) and convolutional neural network (CNN).

Recently, physical-layer authentication (PLA) has attracted much attention since it takes advantage of the channel randomness nature of transmission media to achieve communication confidentiality and authentication. In the complex environment, such as the Industrial Internet of Things (IIoT), machine learning (ML) is widely employed with PLA to extract and analyze complex channel characteristics for identity authentication. However, most PLA schemes for IIoT require attackers’ prior channel information, leading to severe performance degradation when the source of the received signals is unknown in the training stage. Thus, a channel impulse response (CIR)-based PLA scheme named “hierarchical variational autoencoder (HVAE)” for IIoT is proposed in this article, aiming at achieving high authentication performance without knowing attackers’ prior channel information even when trained on a few data in the complex environment. HVAE consists of an autoencoder (AE) module for CIR characteristics extraction and a variational AE (VAE) module for improving the representation ability of the CIR characteristic and outputting the authentication results. Besides, a new objective function is constructed in which both the single-peak and the double-peak Gaussian distributions are taken into consideration in the VAE module. Moreover, the simulations are conducted under the static and mobile IIoT scenario, which verify the superiority of the proposed HVAE over three comparison PLA schemes even with a few training data.

This article considers a smart-duplex (SD) powered user-centric ultra dense networks (UC-UDNs), where each user is served cooperatively by multiple access points (APs) adopting the de-cellular concept to achieve desired Quality-of-Service (QoS). The average QoS satisfaction ratio maximization problem for the considered SD UC-UDN is formulated as a Markov decision process (MDP) with large discrete action space by designing the user association and power allocation. To reduce the action space, user association and power allocation are modeled as a two-layer tree, and selecting an action for each user is equivalent to finding the path from the root to one leaf of the constructed tree. Then, a multiagent tree-structured policy gradient (MATSPG)-based deep reinforcement learning (DRL) algorithm is proposed to solve the MDP problem, whose training process is shown to be equivalent to that of the two-layer neural networks. Next, the time and space complexity of searching one action in the proposed MATSPG are also proved to be lower than the conventional DRL algorithms. Finally, simulations show that the proposed MATSPG algorithm significantly improves the average QoS satisfaction ratio than the conventional multiagent deep deterministic policy gradient and multiagent deep Q-network methods in typical scenarios.

The fast development of mobile communication and artificial intelligence (AI) technologies greatly promotes the prosperity of the Internet of Things (IoT), where various types of IoT devices can perform more intelligent tasks. Considering the privacy leakage and limited communication resources, federated learning (FL) has emerged to enable devices to collaboratively train AI models based on their local data without raw data exchanges. Nevertheless, it is still challenging for guaranteeing any FL models to be effective due to the sluggish willingness of IoT devices and the model poisoning attacks in the FL. To address these issues, in this article, we introduce blockchain technology and propose a blockchain-based FL framework for supporting a trustworthy and reliable FL paradigm in IoT. In the proposed framework, we design a committee-based participant selection mechanism that selects the aggregate node and local model updates dynamically to construct the global model. Moreover, considering the tradeoff between the energy consumption and the convergence rate of the FL model, we perform the channel allocation, block size adjustment, and block producer selection jointly. Since the remaining resources, handling transactions, and channel conditions are dynamically varying (i.e., stochastic environment), we formulate the problem as a Markov decision process (MDP) and adopt a deep reinforcement learning (DRL)-based algorithm to solve it. The simulation results demonstrate the effectiveness of the proposed framework and show the superior performance of the DRL-based resource allocation algorithm compared with other baseline methods in terms of energy consumption.

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.

A non-orthogonal multiple access (NOMA)-aided joint communication, sensing, and multi-tier computing (JCSMC) framework is proposed. In this framework, a multi-functional base station (BS) simultaneously carries out target sensing and provide edge computing services to the nearby users. To enhance the computation efficiency, the multi-tier computing structure is exploited, where the BS can further offload the computation tasks to a powerful Cloud server (CS). The potential benefits of employing NOMA in the proposed JCSMC framework are investigated, which can maximize the computation offloading capacity and suppress inter-functionality interference. Based on the proposed framework, the transmit beamformer of the BS and computing resource allocation among the BS and CS are jointly optimized to maximize the computation rate subject to the communication-computation causality and the sensing quality constraints. Both partial and binary computation offloading modes are considered: 1) For the partial offloading mode, a weighted minimum mean square error based alternating optimization algorithm is proposed to solve the corresponding non-convex optimization problem. It is proved that a Karush–Kuhn–Tucker optimal solution can be obtained; 2) For the binary offloading mode, the resultant highly-coupled mixed-integer optimization problem is first transformed to an equivalent but more tractable form. Then, the reformulated problem is solved by utilizing the alternating direction method of multipliers approach to obtain a nearly optimal solution. Finally, numerical results verify the effectiveness of the proposed algorithms and reveal that: i) the computation rate can be significantly enhanced by exploiting the multi-tier computing architecture when the BS is resource-limited, and ii) the proposed NOMA-aided JSCMC framework is superior in inter-functionality interference management and can achieve high-quality sensing and computing performance simultaneously compared with other benchmark schemes.

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.

In this paper, we propose a reconfigurable intelligent surface (RIS) assisted secure finite blocklength transmission framework in machine-type communications (MTC) networks, where the integration of millimeter-wave (mmWave) communication and non-orthogonal multiple access (NOMA) technology is considered to alleviate the problem of insufficient spectrum resources caused by massive MTC devices (MTCDs). For improving the ability of anti-eavesdropping, we aim to maximize the achievable sum secrecy capacity (SC) by jointly optimize the MTCDs’ transmission power, RIS phase coefficient and receive beamforming design. To handle the nonconvexity of the proposed optimization problem, we decouple it into three sub-problems, where the first two are solved by successive convex approximation (SCA) method. A minimum mean squared error successive interference cancellation (MMSE-SIC) scheme is proposed to tackle the receive beamforming problem for uplink NOMA networks. Furthermore, an alternating optimization based joint power, phase, and beamforming allocation (AO-JPPBA) algorithm is developed to implement joint optimization. Simulation results show that: 1) the security performance of the proposed AO-JPPBA is improved by 612.26% than the baseline scheme; 2) the proposed MMSE-SIC beamforming scheme is more effective in improving sum-SC of uplink NOMA networks; 3) RIS’s location has an obvious impact on sum-SC when considering eavesdroppers with strong wiretapping ability.

Distributed power allocation is important for interference-limited wireless networks with dense transceiver pairs. In this paper, we aim to design low signaling overhead distributed power allocation schemes by using graph neural networks (GNNs), which are scalable to the number of wireless links. We first apply the message passing neural network (MPNN), a unified framework of GNN, to solve the problem. We show that the signaling overhead grows quadratically as the network size increases. Inspired from the over-the-air computation (AirComp), we then propose an Air-MPNN framework, where the messages from neighboring nodes are represented by the transmit power of pilots and can be aggregated efficiently by evaluating the total interference power. The signaling overhead of Air-MPNN grows linearly as the network size increases, and we prove that Air-MPNN is permutation invariant. To further reduce the signaling overhead, we propose the Air message passing recurrent neural network (Air-MPRNN), where each node utilizes the graph embedding and local state in the previous frame to update the graph embedding in the current frame. Since existing communication systems send a pilot during each frame, Air-MPRNN can be integrated into the existing standards by adjusting pilot power. Simulation results validate the scalability of the proposed frameworks, and show that they outperform the existing power allocation algorithms in terms of sum-rate for various system parameters.

With the prosperity of emerging applications, the$6^{th}$Generation mobile communication systems (6G) is coming at an unimaginable speed. It is expected to provide more intelligent, flexible, and secure services. As an essential pillar of 6G networks, ultra-Reliable Low Latency Communication (uRLLC) has promoted the vigorous development of intelligent communications. However, the existing networks cannot fully satisfy the strict and various requirements of uRLLC services, including delay, reliability and security. Considering the interaction between the physical layer and the upper layer, we propose a Cross-layer Flexible Security Solution (CFSS), which includes initiative waiting strategy, flexible transmission time interval scheduling strategy, and flexible pre-backup transmission strategy. While considering secure communication, CFSS could flexibly provide customized services to the users through cross-layer parameters configuration and resource allocation. In addition, we extend the Stochastic Network Calculus (SNC) modeling to the security field, and use Finite Blocklength Coding (FBC) to analyze the service process of uRLLC. Two cases of FBC are considered comprehensively, namely, given decoding error probability and given transmission rate. Finally, Experienced Meta-Asynchronous Advantage Actor-Critic (EM-A3C) algorithm is proposed to solve the complex optimization problem, the establishment of experience pool effectively improves the algorithm efficiency.

A non-orthogonal multiple access (NOMA)-aided joint communication, sensing, and multi-tier computing (JCSMC) framework is proposed. In this framework, a multi-functional base station (BS) simultaneously carries out target sensing and provide edge computing services to the nearby users. To enhance the computation efficiency, the multi-tier computing structure is exploited, where the BS can further offload the computation tasks to a powerful Cloud server (CS). The potential benefits of employing NOMA in the proposed JCSMC framework are investigated. Based on the proposed framework, the transmit beamformer of the BS and computing resource allocation among the BS and CS are jointly optimized to maximize the computation rate subject to the communication-computation causality and the sensing quality constraints. A weighted minimum mean square error based alternating optimization algorithm is proposed to solve the corresponding non-convex optimization problem. Fi-nally, numerical results show the significant performance gain achieved by the proposed schemes over the benchmark schemes.

Blockchain-based dynamic spectrum sharing (DSS) is viewed as a robust measure to address the management and allocation of spectrum resources for Internet of Things (IoT) ecosystems in a secure and auditable manner. However, existing blockchain empowered DSS systems commonly assume the perfect communications among the nodes without any interference and throughput constraints, which will mislead the spectrum owner into making suboptimal decisions, especially under the time-varying and unstable channel situation in IoT. To overcome the influence of the imperfect channel situations on DSS, we propose a novel reputation mechanism-based blockchain empowered DSS system in this paper. By considering both the historical transaction successful rate and the communication throughput and the real-time channel situations, the reputation mechanism enables the spectrum owner to make the appropriate decisions even under the occurrence of the deep decay. Furthermore, the spectrum trading process is formulated as a Stackelberg game to encourage the participation of the spectrum owner and the requester. A pricing scheme is proposed by maximizing the utilities of all nodes jointly. Simulation results confirm that our proposed scheme is superior to existing works in revenue utility.

With the commercial application of 5th generation, the coexistence scenario of enhanced Mobile Broadband (eMBB) and ultra-Reliable and Low Latency Communication (uRLLC) is facing significant challenges in utilizing limited resources. The existing scheme of only using puncturing or superposition cannot meet the heterogeneous requirement of eMBB and uRLLC. In this paper, we propose a cooperative multiplexing mode dynamic selection and resource allocation scheme to achieve the trade-off between the transmission quality (the transmission rate and the transmission accuracy ratio) of eMBB and the reliability of uRLLC, which considers power limitation. In the scheme, the multiplexing mode includes puncturing mode and Non-Orthogonal Multiple Access (NOMA) mode. After the multiplexing mode is selected, the resource block and power allocation are carried out. Furthermore, we propose a CoDueling Deep Q-learning Network to obtain the expected long-term benefits of the formulated scheme. Simulation results show that the proposed algorithm reduces the computation time by 27.3% and outperforms the compared scheme. Moreover, the proposed scheme improves the overall transmission quality of the coexistence scenario, where both eMBB and uRLLC services do not occupy the resources selfishly.

Key generation rate and bit disagreement ratio are two main indicators to evaluate the performance of physical layer key generation (PLKG). This work explores the key generation performance in reconfigurable intelligent surface (RIS)-assisted PLKG, where a concrete scheme of key generation is proposed. Specifically, we first deduce the key generation rate expression based on estimation theory in RIS-assisted PLKG. Then we optimize the RIS reflecting coefficients with instantaneous channel state information to maximize the key generation rate. Finally, aiming at minimizing transmit power while guaranteeing key generation rate target, we formulate a power minimization problem in RIS-assisted PLKG. An alternating optimization algorithm is applied to solve the non-convex mixed-integer nonlinear programming. Simulation results demonstrate that the key generation rate of the proposed scheme is up to 197.5% higher than the existing relay-assisted scheme, and we successfully reduce the required transmit power by at least 6.6 dBm than without power minimization scheme.

The millimeter wave (mmWave) band has sufficient bandwidth resources, while it needs a dense base station (BS) deployment for coverage due to higher path loss and lower signal penetration through obstacles. Integrated Access and Backhaul (IAB) becomes the hot spots of research and standardization as a feasible and economical way to achieve dense base station deployment. However, the IAB-node may be affected significantly by congestion problem when transferring data packets, which can result in packet discard and longer transmission delay. This work provides a congestion mitigation scheme based on multi-connectivity (MC) on uplink, which allows congestion nodes utilizing multi-links to improve their backhaul capacity. However, once the IAB-node use multi-links, it will occupy the resources of other IAB-nodes. Therefore, we proposed a Multi-Agent Dueling Deep Q Network (DDQN) based Route Selection Algorithm to help IAB-node decide which links should be selected under different network states. The simulation results present the proposed algorithm outperforms the comparison algorithms in term of packet loss rate and the designed reward.

As one of the key communication scenarios, ultrareliable low-latency communication (uRLLC) has become an important pillar to promote the vigorous development of intelligent mobile communications. In the practical scenarios, uRLLC services have strict and diverse Quality-of-Service (QoS) requirements. However, the existing networks are difficult to meet the various delay and reliability requirements of uRLLC services. Moreover, the improvement of performance should not ignore the shortage of resources. A flexible and on-demand network solution is quite necessary, which could provide customized services according to the specific requirements and maximize the utilization efficiency of network resources. In this article, we propose an intelligent and flexible network solution (IFNS) based on the stochastic network calculus (SNC) model. Three key technologies are considered in the IFNS, that are flexible transmission time interval scheduling, flexible packet duplication transmission, and rate-adaptive reliable transmission. While providing customized services for users with various requirements, it realizes the balance between system energy efficiency and spectral efficiency and improves the resource utilization efficiency of the network. Based on the basic domain knowledge and the past experience, we propose the knowledge-assistance meta actor–critic (K-MAC) algorithm to solve the complex optimization problem caused by SNC modeling. Finally, simulation results show that the performance of the IFNS is improved 23.15%, the K-MAC algorithm has good convergence performance and reduces the complexity up to 89.4% compared with common learning algorithms.

With the continuous popularization and deepening of the Internet-of-Things (IoT) technologies, trillions of IoT Devices (IoTD) are connected to the network. The huge growth of wireless communication traffic and the surge of energy consumption make it a great challenge to support various requirements of IoTDs, such as ultrareliable and low latency. The 6th-generation (6G) network has put forward new goals and visions for green communication, network flexibility and intelligence, which are expected to solve these key challenges. In this article, we propose a cross-layer optimization scheme to achieve the trade-off between energy efficiency (EE) and spectral efficiency (SE) of the 6G enabled IoT networks, where the ultrareliable and low-latency applications are considered. Flexible self-organization of three parameters is realized, namely, transmission time interval (TTI), packet duplication (PD), and resource block (RB) allocation. The key technology of flexible TTI scheduling guarantees the reduction of latency, and the PD transmission can effectively improve the reliability. Furthermore, based on machine learning (ML) method, we propose the transfer asynchronous advantage actor–critic (TA3C) algorithm to realize parameter configuration and resource allocation. The simulation results show that the EE and SE tradeoff performance of our proposed flexible scheme is improved by at least 39.29% compared with the fixed parameter configuration. In addition, the TA3C algorithm has better convergence performance and reduces the algorithm complexity by up to 91.23% compared with other ML algorithms.

Machine-type communication (MTC) is one of the main enabling technologies to support various applications with diverse quality of service (QoS) requirements. Finite blocklength transmission has great potential in meeting the strict delay requirements of delay-sensitive MTC devices (MTCDs), while also causing loss of network capacity due to the decoding error probability. Aiming at this problem, we introduce uplink non-orthogonal multiple access (NOMA) and buffer-aided relaying to assist the finite blocklength transmission with delay requirements for improving the achievable effective capacity (EC), which is defined as the maximum short-packet constant arrival rate under specific statistical QoS constraints. To solve the EC maximization problem, we derive the closed-form expression of time allocation coefficient. Then we establish a concave lower bound of EC using successive convex approximation (SCA) for power allocation of MTCDs, and formulate a non-cooperative game based distributed power allocation algorithm for relay. Furthermore, a joint time and power allocation (JTPA) algorithm is proposed to implement joint resource allocation. Simulation results show that under finite blocklength and statistical QoS constraints, adopting buffer-aided relaying can improve EC by 41.82% compared with no-buffer relaying. Moreover, the achievable EC of JTPA algorithm is only 3.12% lower than that of exhaustive search while reducing complexity.

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.

The digital twin (DT) bridges the physical world with the digital world in real-time for the Industrial Internet of Things (IIoT) and federated learning (FL) enables edge intelligence services for IIoT under the premise of avoiding privacy leakage. The fusion of two technologies can extremely accelerate the development of Industry 4.0 by enabling instant intelligence services. However, in the resource-constrained IIoT, the energy consumption of performing FL and maintaining the virtual object in the digital space by DT technology become the bottlenecks and can not be ignored. To address these issues, in this paper, we proposed an energy-efficient FL framework for DT-enabled IIoT. In the proposed framework, IIoT devices choose different training methods considering dynamic time-varying environment status to achieve energy-efficient FL, i.e., either train locally or connect to the virtual object by DT in the corresponding server of a small base station (SBS) to train mapped data using computing resources of SBS. Then, we investigate the joint training method selection and resource allocation problem to minimize the energy consumption while satisfying the convergence rate of the training model. Considering the problem is intractable using traditional approaches, we use a deep reinforcement learning (DRL)-based algorithm to solve it. Simulation results show that the proposed framework decreases greatly energy consumption compared with the static framework while satisfying the convergence rate of FL.

With the fast developing of Health-IoT supported by massive Machine Type Communication (mMTC) of 5G, personal physiological data of patients endure various malicious attacks from intelligent eavesdropper during the transmission procedure. The leakage of patients' data will threaten their personal information security. In this paper, we propose a hybrid Relay Selection and Cooperative Jamming (RS-CJ) scheme to resist intelligent eavesdroppers in the sensor network tier in Healthcare-IoT systems. In the hybrid RS-CJ scheme, the relay is adaptively selected from bio-sensors before the physical data are transmitted, and the unselected bio-sensors act as friendly jammers. Moreover, we derived the security capacity of the legitimate and eavesdropping channels under the RS-CJ scheme. Then, we obtained the closed-form expressions of the Ergodic Achievable Security Rate (EASR). We also analyzed the negative influence on EASR when the intelligent eavesdropper holds different active jamming power. The simulation results verify the correctness of EASR, and our proposed RS-CJ scheme is feasible in general Healthcare-IoT systems. Compared with related solutions, EASR of the proposed hybrid RS-CJ scheme is 64% higher than the existing relay selection and interference scheme, as well as 3.38 times higher than that of the optional relay selection scheme proposed by other scholars.

As climate and energy issues are increasingly serious, energy efficiency in fifth generation (5G) communication networks has attracted tremendous attention. With the development of heterogeneous ultra-dense networks (H-UDNs), the growing number of base stations (BSs) will lead to more energy consumption. In this paper, we propose a BS sleeping strategy based on device-to-device (D2D) clustering communication. First, we obtain the optimal D2D cluster Head (CH) density by maximizing the total number of users which are served by the D2D CHs. Then, we consider the Received Signal Strength (RSS) and the Signal to Interference plus Noise Ratio (SINR) of the user to select the optimal D2D CHs. Furthermore, we adjust the load of the lightly loaded BSs and switch them into sleeping mode to save the energy of the network. The simulation results show that the strategy not only reduces energy consumption but also improves the user coverage probability of the network.

Industrial 4.0 will be supported by Internet of Things (IIoT), which will bring profound revolutions to the industrial manufacturing. The fifth generation wireless communication system (5G) will be one of the key technologies to support IIoT. However, the connectivity-massive, computation-intensive and time-critical features of IIoT pose great challenges to the spectrum and computation resource in 5G IIoT networks. Non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) are regarded as promising paradigms to tackle these problems, called NOMA-based MEC. To enhance computing performance of MEC system, we consider that devices can also offload their computation tasks to some idle devices with rich computation resources through machine-to-machine (M2M) communication, called M2M-assisted NOMA-based MEC scheme. We formulate an optimization problem under tasks delay constraints to minimize the system energy consumption through sleep-scheduling and joint computation-communication resource allocation. Specifically, we propose a deep reinforcement learning (DRL) based sleep-scheduling scheme to arrange some idle devices to work at sleep-mode for saving energy while satisfies the system computation requirements. Furthermore, we design an iterative algorithm for the joint computation-communication resource allocation problem. Numerical results demonstrate our proposed scheme and algorithm achieve significantly reduction of system energy consumption, while satisfying network computation requirements.

The ever-growing number of Internet-of-Things (IoT) devices makes multiaccess edge computing (MEC)-assisted status update system more and more attractive, which can be deployed to enable remote data acquisition and analysis from urban space. The ambient computing resource at edge automatically extracts valuable status update information from the data collected by IoT devices, which supports the real-time remote monitoring applications. In this article, we employ the concept of Age of Information (AoI) to quantify the freshness of status updates. To combat the limited battery capacity at IoT devices, energy harvesting (EH) is leveraged to capture the green energy from ambient environment. Specifically, we investigate an age minimization problem by considering the randomness in energy arrivals, heterogeneity in harvesting mode, and the stochasticity in transmission and computing process. The formulated problem is a long-term stochastic optimization problem. Then, we transform the original problem into a series of per-time slot deterministic optimization problem. An online scheduling policy is proposed to obtain the energy management decisions at devices, and the transmission and computing scheduling decisions among multiple devices without any prior knowledge on the network dynamics, which is facilitated to be implemented. Simulation results show that the performance of our proposed algorithm is competitive when compared with other existing schemes.

The integration of mobile-edge computing (MEC) and energy harvesting (EH) can potentially improve the network performances and prolong the battery life of the device. In this article, we study the resource management problem in the device-to-device (D2D)-aided wireless powered MEC networks where one device can forward or execute computation data for other devices with its resources. Our problem seeks to optimize the computation offloading strategy, transmission power, energy transmit power, as well as CPU speed to maximize the long-term utility energy efficiency (UEE). UEE is defined as the achieved computation data per unit energy. Since the formulated problem is in fractional form and hard to solve, we employ the Dinkelbach algorithm to transform the problem into a parametric subtractive form. Furthermore, considering that the formulated problem is time varying and stochastic due to the dynamic task arrival rate and battery level, we transform the long-term problem into deterministic drift-plus-penalty subproblems for each time slot by introducing virtual queues and adopting the Lyapunov optimization theory. The proposed scheme can balance the optimal UEE and stable data queue by introducing the control parameter$V$. Theoretically, we reveal the tradeoff between the UEE and stable queue length for wireless powered MEC systems as$[O(1/V), O(V)]$. Finally, the simulations illustrate the efficiency of the proposed scheme compared with the existed work in terms of the UEE, stable queue length, and battery level.

Thanks to the inherent characteristics of flexible mobility and autonomous operation, unmanned aerial vehicles (UAVs) will inevitably be integrated into 5G/B5G cellular networks to assist remote sensing for real-time assessment and monitoring applications. Most existing UAV-assisted data collection schemes focus on optimizing energy consumption and data collection throughput, which overlook the temporal value of collected data. In this article, we employ Age of Information (AoI) as a performance metric to quantify the temporal correlation among data packets consecutively sampled by the Internet of Things (IoT) devices, and investigate an AoI-energy-aware data collection scheme for UAV-assisted IoT networks. We aim to minimize the weighted sum of expected average AoI, propulsion energy of UAV, and the transmission energy at IoT devices, by jointly optimizing the UAV flight speed, hovering locations, and bandwidth allocation for data collection. Considering the system dynamics, the optimization problem is modeled as a Markov decision process. To cope with the multidimensional action space, we develop a twin-delayed deep deterministic (TD3) policy gradient-based UAV trajectory planning algorithm (TD3-AUTP) by introducing the deep neural network (DNN) for feature extraction. Through simulation results, we demonstrate that our proposed scheme outperforms the deep$Q$-network and actor–critic-based algorithms in terms of achievable AoI and energy efficiency.

In the era of the Internet of Things (IoT), reinforcement learning (RL)-based techniques are promising candidates to handle the intrusion response through the interaction between the IoT device and its environment. Given a large number of devices in the IoT network, wireless software-defined networking (W-SDN) is widely agreed to be introduced to facilitate network management, such as launching a new intrusion response scheme (IRS) on massive IoT devices. To guarantee the scalability and security of IoT services on the extended devices in the W-SDN-based network, a distributed RL-based IRS is proposed in this article, called game-theoretic actor–critic-based IRS (GTAC-IRS). GTAC-IRS employs a game-theoretic-based response selection matrix, aiming at reducing training time and facilitating the convergence of the response scheme. GTAC-IRS constructs a well-designed state representation of observed environment status, a low-dimension response matrix, and a simplified response selection policy to lower the complexity of the algorithms. Simulation results reveal that benefiting from local environment observation, GTAC-IRS achieves effective intrusion response without sophisticated feature engineering. Instead of the “warm-start” training adopted in conventional RL-based IRS, the low-dimension response matrix in GTAC-IRS can significantly improve the convergence speed. Thus, GTAC-IRS outperforms other popular IRSs in terms of the response time under the circumstance of the node’s behavior changing or malicious nodes ratio changing.

As a typical Internet-of-Things (IoT) scenario, massive machine-type communications (mMTC) services are expected to grow exponentially and create a multibillion-dollar industry spanning a broad range of vertical sectors. In literature, wireless software-defined network (SDN) is viewed as a promising approach to facilitate the degree of reconfigurability on extended sets of mMTC devices via centralized software updates. However, most of the current anomaly detection scheme (ADS) in SDN suffers from the high risk of overwhelming of the controller as well as excessive energy consumption if directly applied in the network with enormous devices. To address the scalability issues in centralized ADS, we propose a localized ADS scheme, called scalable and energy-efficient anomaly detection scheme (SEE-ADS), comprising of a detection activation module, a lightweight predetection module, a heavyweight anomaly detection module, and a dynamic strategy selection module. Through the cooperation among these modules, the proposed ADS is capable of detecting attacks dynamically and effectively without the risk of energy depletion via discontinuous activation of the heavyweight detection. The lower complexity is fulfilled by developing a localized and adaptive heavyweight detection module, called a localized evolving semisupervised learning-based anomaly detection scheme (LESLA). Besides, the proposed scheme makes full use of feedback from the previous heavyweight activation and the indication of predetection on each packet. The simulation results show that the proposed scheme greatly reduces the overall energy consumption over heavyweight detection. Furthermore, the proposed scheme shows higher sensitivity on abnormal packets and similar false alarm compared with the literature work.

Millimeter-wave (mmWave) and ultra-dense networks are two key technologies for the fifth-generation (5G) and beyond communication system. However, the ultra-dense deployment of small base stations (SBSs) might introduce severe interference to users that connect to SBSs. This paper analyzes the performance of 5G communication networks where the SBSs with coordinated beamforming, operating at mmWave frequency band and macro base stations (MBSs) operating at sub-6 GHz coexist. First, by utilizing a stochastic geometry approach, we obtain the cell association probability expressions in terms of different cell association biases, base station density ratios and probabilities of line of sight (LoS) link. Furthermore, we propose a clustering method to choose some SBSs to eliminate intra-cell interference. Then, we put forward an average distance from the Kth SBS to a user to obtain signal-to-interference-ratio (SINR) and rate coverage probability expressions. The simulation results validate the correctness of the expressions, and indicate that the optimal cardinality of coordinated SBSs increases with the density of SBSs. In addition, the relationship between the cluster size K and the average energy efficiency is obtained, which can be used to guide the coordination principle in 5G and beyond communication systems.

The millimeter wave (mmWave) band has been considered as an effective way to meet the increasing traffic demand by sufficient bandwidth and high spatial reuse. The harsh propagation experienced at such high frequencies requires a dense base station deployment, which is not feasible to provide wired backhaul due to the unavailability of fiber drops. To address this issue, the integrated access and backhaul (IAB) architecture is proposed by the third generation partnership project (3GPP). In this paper, we investigate the spectrum and power allocation for IAB mmWave enabled cellular network to maximize the network capacity with considering the data rate requirements. A novel resource allocation scheme based on the sequential convex programming approach (RASCPA) is proposed. The simulation results show that our proposed scheme is superior to other schemes. The proposed scheme can increase the network capacity while satisfying the user data rate requirements.

As the deepening of 5G's support for the e-health industry, more and more wireless medical devices will suffer from various attacks and threats. Especially, the security of implantable medical devices (IMDs) which have limited computational capabilities and stringent power constraints becomes a critical issue. According to the channel state information, we exploit the special characteristics of the received signal strength (RSS) ratio between wearable proxy devices (WPDs) and IMDs in wireless body area networks (WBANs) to distinguish legitimate users and attackers. Moreover, based on the idea of proposed authentication request filtering (ARF), we design two corresponding light-weight security protocols to defend the forced authentication (FA) attacks and enhance the accessibility of IMD in emergency mode respectively. Simulation results show that the proposed ARF scheme to defend FA attacks achieves a high authentication response rate (ARR) with 99.2% for legitimate users and a low ARR with 2.4% for attackers at the maximum gap threshold point. Furthermore, when applied in emergency mode, the ARF scheme allows up to 96.3% emergency rescue devices to access the IMDs with only one attempt.

The ultra-dense deployment of small cells has been applied to the 5th-generation (5G) mobile networks. A large number of base stations (BSs) will lead to a dramatic increase in energy consumption, and network resources will be more difficult to fully utilize. In this paper, we propose the dynamic load adjustments (DLA) algorithm for small cells in heterogeneous ultra-dense networks. The proposed algorithm applies Q-learning to learn effective offloading policies which could combine the energy-saving function and the load balancing function. Based on the DLA algorithm, the heterogeneous ultra-dense networks could adjust the traffic load to turn off some redundant BSs or balance the load between heavily loaded BSs and lightly loaded BSs. The simulation results show that the algorithm not only improves the network energy efficiency when the average load of the networks is light, but also improves the network throughput when the average load of the networks is heavy.

Moving networks (MNs) with moving base stations (BSs) provide ubiquitous and constant services to cellular devices/user equipment (UEs) in 5-th generation systems. Moving BSs are mounted on top of vehicles. To describe the randomness of the BSs, a tractable stochastic geometry model for MNs is proposed. A definition of the conditional success probability and meta distribution (MD) of the signal-to-interference ratio (SIR) for MNs is proposed. The MD is used to assess the benefits of MNs. In single-tier MNs with high mobility, we determine the moments of the conditional success probability given the point process for the calculation of the MD and the mean local delay. The results show that the mean local delay is finite and the variance is reduced to 0. A closed-form approximation of the variance is proposed for general mobility levels. Using the approximated variance, we propose a beta approximation of the MD. The single-tier model is then extended to a two-tier heterogeneous MN model. Tractable expressions of the mean success probability and the variance for both the overall network and the typical UE in each tier are obtained. They reveal that moving BSs can reduce the variance among UEs while keeping the mean success probability constant.

As the versatile applications emerge, traffic offloading is an urgent issue to improve the performance for the fifth generation (5G) and beyond networks. We focus on the scenario where a device is enabled to transmit to more than one device simultaneously. The device-to-device (D2D) enabled traffic offloading scheme is studied by employing non-orthogonal multiple access (NOMA) and unlicensed access technologies. Our target is to maximize the capacity of the D2D network by optimizing subchannel assignment and power control while guaranteeing the capacity of NOMA-based cellular links and the WiFi system. The formulated problem is a non-convex mixed integer programming problem, which is hard to solve within a rational time. The problem is decomposed into subchannel assignment and power control subproblems. A matching based licensed subchannel allocation algorithm and an unlicensed subchannel access mechanism are proposed. Furthermore, we propose a centralized power control algorithm and a distributed power control algorithm based on global and local information, respectively. Besides, the unlicensed resource management scheme based on Stackelberg game is proposed to achieve the near-optimal utility of both D2D links and the WiFi system. The simulations illustrate that the proposed scheme can increase the throughput of D2D networks efficiently compared with other works.

Caching on the wireless edge is a promising way to alleviate the backhaul burden of the heterogeneous ultra-dense network (H-UDN) and reduce the transmission delay. However, user mobility makes the content distribution more challenging for the association to small base stations (SBSs) always changes. In this paper, we propose a mobility-aware proactive caching strategy named MoPC in H-UDNs where each SBS can perceive the mobility pattern of users in neighbor SBSs. Moreover, by taking advantage of the hierarchical network feature, files requested by different speed users will be cached in different cache layers by amending content popularity. The cache placement problem is formulated with the aim of effective capacity maximization and solved by a genetic algorithm based approach. Simulation results prove that the proposed MoPC strategy outperforms other existing caching strategies in terms of both the transmission delay and cache hit ratio.

As one of the three main scenarios of 5G, massive machine-type communications (mMTC) is expected to play an essential role in the future. However, the limited battery of the machine-type communication devices (MTCDs) restricts the network performance. Unmanned aerial vehicles (UAVs) attract more attention recently in cellular networks. The collaboration between the UAV and MTCDs has been a crucial topic to enhance the network performance. In this paper, an adaptive and cost-efficient model is proposed to collect data from MTCDs in a large area assisted by the UAVs. We propose the strategies for higher energy efficiency of MTCDs and longer lifetime of the network based on the model. According to the strategies, we propose a Global Energy-efficient ant colony optimization (ACO) routing algorithm for UAV-assisted mMTC networks (GEAU) to reduce the energy consumption and prolong the lifetime of the networks. Furthermore, in order to reduce the complexity, we propose a practical algorithm with similar performances. Simulations are carried out and illustrate that the proposed algorithms have superior performances compared with existing works.

Multi-Connectivity (MC) is a key technology in the deployment of the upcoming Fifth Generation (5G). MC means that one user can connect to a Master Evolved NodeB (MeNB) as well as several Secondary Next Generation NodeBs (SgNBs) simultaneously. According to MC, MeNB will forward a portion of traffic to SgNBs through non-ideal backhaul, while the remaining traffic will be served by itself. An efficient traffic splitting scheme has great impacts on the performance of MC. In this paper, we propose a goodput-aware traffic splitting scheme based on queuing theory and goodput model. Taking the nonideal backhaul delay as well as the buffer status of MeNB and SgNBs into consideration, the proposed traffic splitting scheme can adaptively optimize the system goodput. Simulation results verify that the proposed goodput-aware traffic splitting scheme effectively contributes to the goodput increment.

In the era of Internet of Everything, massive connectivity and various demands of latency for Internet of Things (IoT) devices will be supported by the massive machine type communication (mMTC). Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) have the advantages of improving network capacity, reducing MTC devices' (MTCDs) latency and enhancing quality of service. Exploiting these benefits, we focus on the energy efficient secure computation offloading in NOMA-based mMTC networks for IoT, where the relay equipped with an MEC server and a passive malicious eavesdropper are presented. We optimize the joint computation and communication resource allocation to maximize the secrecy energy efficiency of computation offloading while guaranteeing the delay requirements of MTCDs. Furthermore, we model the subchannels allocation problem as MTCD-to-subchannel matching. Exploiting difference of convex programming and successive convex approximation, we formulate the Dinkelbach-based SEE optimization algorithm and obtain the closed-form expression of power allocation for MTCDs' on each subchannel. Based on the communication resources allocation schemes, we propose the Knapsack algorithm to solve the problem of computation resource allocation. Furthermore, we formulate the joint computation and communication resource allocation algorithm for secure computation offloading. Simulation results demonstrate the effectiveness of proposed algorithm for supporting IoT devices energy efficient secure computation offloading.

This paper presents a Rate-based Cell Range Expansion (CRE) for mmWave massive Multiple Input Multiple Output (MIMO) enabled two-tier heterogeneous networks (Het-Nets) where macro base stations (BSs) operate at sub-6 GHz and small BSs operate at mmWave band. We design a new user association scheme with the consideration of disparities between macro BSs and small BSs, chiefly bandwidths combined with Signal to Interference plus Noise Ratio (SINR), although conventional CRE provides a fixed Cell Selection Offset (CSO) for all user equipments (UEs). A downlink model is put forth to analyze the performance of the proposed Rate-based CRE. Our scheme consists of two parts: CRE between small cells and CRE between macro cells and small cells. We also investigate the proposed method by evaluating the average user throughput and cell edge user throughput. System-level computer simulation results such as average user throughput and 5-percentile user throughput are provided. The results confirm that the proposed method can improve the average user throughput compared with the conventional CRE scheme and maintain the cell edge user throughput for such multi-frequency cooperative networks. Thus, this scheme is an effective solution for user association in 5G mmWave HetNets.

In this paper, we investigate homogeneous clustering algorithm for the machine-type communication networks to minimize device energy consumption and prolong the network battery lifetime. First, we present a homogeneous clustering approach that considers both the average residual energy and the distance from device to the base station. Second, we obtain the optimal number of the clusters to minimize the energy consumption for single round and formulate an energy-efficient cluster head selection scheme. At last, we propose an improved algorithm based on the optimal result to make the formulated homogeneous clustering algorithm more feasible. To evaluate the performance of our algorithms, simulations are conducted to compare its performance with the low-energy adaptive clustering hierarchy (LEACH) algorithm. Results show that the proposed algorithm greatly extends the network lifetime.

Moving relay (MR) has been widely studied to extended the coverage of base stations (BS). In this paper, we investigated a distance-based hybrid-duplex scheme for MR and BS system. The key idea is that UE can access to MR or BS based on its received RSRP, and select its working mode based on the transmit distance. By setting appropriate switching distance between two modes, UE can have a probability working in full-duplex to increase throughput within rather low outage. The simulation results show good agreements with our analyze and verify the proposed scheme can keep rather low outage.

To avoid the Radio Access Network overload caused by massive random access attempts from Machine-Type Communication (MTC), we propose a Quality of Service (QoS)-based Dynamic and Adaptive Mechanism (QDAM). Moreover, we propose a more practical algorithm based on QDAM to solve the difficulty in obtaining the total number of accessing devices in reality. Both of the proposed algorithms combine dynamic allocation of Random Access Channel (RACH) resource scheme and adaptive Access Class Barring (ACB) scheme. First, we give priority to the delay-sensitive devices when allocating preambles. Afterwards, based on the number of allocated preambles and the number of devices failed to access, we adaptively adjust the ACB factors of delay-sensitive devices and delay-tolerant devices. In addition, to minimize the delay of delay-sensitive devices and improve the resource efficiency, we derive the required number of preambles for them. Simulation results demonstrate that the proposed two algorithms outperform other reference algorithms in terms of access delay, resource efficiency and average throughput.

In 5G ultra dense heterogeneous network, the scheme of Passive Reception Detection (PRD) can exploit more busy bands and enhance the small base station (SBS) access opportunity by identifying the distance between the active macro vehicle (MVE) and the macro base station (MBS). In this paper, with the assistance of the MBS, the MVE distribution area can be obtained by the SBS joint-PRD scheme. And the probability density function of the distance between the MVE and SBS is derived leveraging stochastic geometry. By combining the trajectory prediction of co-channel MVE location with levy flight mobile model, the opportunity of the SBS accessing the busy bands can be derived. It is useful to maximize the number of concurrent vehicle-to-vehicle transmissions. The simulation results show that by taking advantage of the proposed scheme in the mobile network, the overall system throughput can be improved more than 20% regardless of the co-channel MVE velocity.

Exponential rise in wireless data demands is already creating a significant burden on current mobile networks. Meanwhile, the upcoming 5G system is believed to consist of heterogeneous access networks. The user satisfaction and radio resource efficiency are the main challenges for the network convergence. Network virtualization technology is regarded as an effective solution to above challenges. Heterogeneous networks are virtualized to multiple logical slices to best serve specific services. Two fundamental questions are how to virtualize mobile resources among different slices, and how to shield the impact of time-varying wireless channel on slicing with Quality of Service (QoS) guaranteed. In order to solve these two problems, we propose a two-timescale Virtualized Radio Resource Pre-Allocation mechanism, consisting of the inter-slice pre-allocation and the intra-slice scheduling. Specifically, the subcarrier and transmission power are jointly optimized and the base station coordination is also supported. For different network deployment scenarios, both the centralized and distributed solutions are provided. Simulation results demonstrate that both solutions are able to guarantee the QoS with the minimum requirements of radio resource.

UE-to-Network relay leverages the proximity communication between user equipments (UEs) and allows certain UEs to provide relay assistance for others, which can greatly improve system energy efficiency. In this paper, we consider the scenario where UEs suffering from bad channel condition and low battery level can communicate with the base station directly or via the help of other UEs in heterogeneous networks. The optimal power allocation and connectivity among UEs are studied, which aims at minimizing the system transmission energy while guaranteeing the minimum data rate requirement of each UE. An optimization framework is presented to formulate the system transmission energy minimization problem, which can be converted into a weighted one-to-one matching problem. And a practical joint transmission mode with relay selection and power allocation (JMRP) algorithm is developed to solve it. Simulation results show that the proposed algorithm outperforms the existing works in terms of system transmission energy and throughput.

The mobile traffic explosion leads to dense deployed heterogeneous networks that can support peak time traffic. However, the utilization of base stations (BSs) is very inefficient during off-peak time due to the traditional strategy, which associates users with BSs based only on the received signal power. In this paper, we investigate the problem and design the joint cell switching-on/off with user association to maximize the network EE. During modeling the optimization problem, we consider the quality-of-service requirements of users and inter-cell interferences under the control plane (CP) and user plane (UP) split. The constraints between the CP and UP are considered. Since the optimization problem belongs to inter programming problem and is NP-hard, we propose a heuristic algorithm named DSUA, to maximize the system EE step by step. The DSUA algorithm deactivates the unnecessary BSs into sleep mode and constructs the user association with the active BSs. Simulation results show that the proposed algorithm can improve the network EE, especially in the light traffic load scenario.

Denser deployments of heterogeneous networks (HetNets) lead to more frequent handovers, which results in a decline of user experience as well as heavy signaling overheads to the network. Therefore, the study on handover is of great importance especially in dense HetNets. In this paper, we focus on the analysis of cross-tier handover from the macro cell tier to the small cell tier, which shows the highest handover failure rate in current standards and industry studies. Using the stochastic geometry, we propose an analytical model for the cross-tier handover processes in the HetNet. Based on the derived analytical model, the closed-form expressions for the key handover performance metrics, including the handover rate, handover failure rate, and ping-pong rate, are deduced as functions of the base station density, time to trigger and user mobility. Simulation results verify the accuracy of the proposed analytical model and closed-form theoretical analyses, which provide guidance for the deployments of HetNets and corresponding handover strategies.

The cloud radio access network (C-RAN) has been proposed recently as a promising network architecture to meet the explosive data traffic growth in 5G wireless communication systems. In C-RAN, all baseband signal processing is centralized at one Baseband Unit (BBU) pool powered by cloud computing technologies. Whilst the Remote Radio Heads (RRHs), left off on the cell sites, are connected to the BBU pool through fronthaul networks and can be densely deployed with low cost. However, a large number of active RRHs located close to each other may result in severer interference and inefficient energy consumption. To tackle above challenges, we formulate a network power consumption minimization (NPCM) problem, which selects a set of active RRHs and constructs the user association with the active RRHs. The capacity limitation of the fronthaul network is considered in the problem. Since the NPCM problem belongs to the integer programming problem and is NP-hard, we propose a low complexity approximation algorithm that yields the performance guarantees: joint RRH selection and user association (JRSUA) algorithm. A simulation platform is developed to evaluate the network power consumption in three fronthaul network scenarios: fiber, wireless and mixed. Simulation results show that the proposed JRSUA algorithm is able to provide near-optimal performance with reduced complexity and outperforms the other counterparts.

Recently, device-to-device (D2D) communication has been considered as a promising technology to implement vehicle-to-vehicle (V2V) communication. To ensure road safety and traffic efficiency, V2V has more strict requirements for latency, reliability and access availability. In this paper, we consider the problem of resource block (RB) scheduling for maximizing the number of concurrent V2V transmissions instead of sum rate, allowing multiple vehicles to access to one RB. Firstly, the reliability requirement is transformed into a constraint of a matrix spectral radius to limit the accumulated interference. Secondly, utilizing spectral radius estimation theory, a Minimizing the Increment of Spectral Radius (MISR) resource block sharing algorithm is proposed to accommodate as many users as possible. Finally, simulation results show that the proposed MISR algorithm can improve spectrum efficiency by 150% and 96% in rush hour compared with some other existing methods.

In this paper, we make theoretical analysis on cross-tier handover in Small Cell Networks (SCNs). The cross-tier handover is defined as the handover occurred from a macro cell tier to a small cell tier, or vice versa. Based on stochastic geometry, we first propose a linear approximation approach to study the coverage of small cells. Using the proposed method, we then derive analytical expressions for cross-tier handover rate and sojourn time inside a small cell, which are the key parameters in mobility performance evaluation of an SCN. According to the analysis, we show that the small cell coverage area can be well represented by a biased circle, and the expected value of small cell size is inversely proportional to the square root of the BS density in the macro cell tier. Another important observation is that the handover rate and sojourn time change linearly with the reciprocal of small cell radius. These analytical results could provide fundamental supports for improving mobility management in SCNs.

Hybrid automatic repeat request (HARQ) plays an important role in improving the transmission efficiency and the robustness of wireless networks. Considering K-tier heterogeneous networks (HetNets) and modelling the locations of the base stations (BSs) as a homogeneous Poisson point process (PPP), this paper investigates the performance of HetNets implementing HARQ. We give closed- form expressions for the quality of service (QoS) coverage probability which is defined in terms of whether the received signal quality is above a predetermined threshold, and the per-user throughput with HARQ. We show that using HARQ can indeed improve the QoS coverage probability. However, depending on the channel conditions, the per-user throughput of the HetNets may decrease by the implementation of HARQ. Furthermore, we show that the small cell density has negligible effect on the QoS coverage probability and the per-user throughput, and the per-user throughput may increase with the small cell path loss.

In heterogeneous networks, the evolved NodeBs (eNBs) have different downlink transmission power and the power imbalance problem can not be avoided. In order to solve this problem, Uplink/Downlink (UL/DL) split for users in Cell Range Expansion (CRE) region of small cells has been proposed in 3GPP. UL/DL split users are connected with macro cells and small cells simultaneously. In this paper, we propose an uplink power control scheme based on game theory for UL/DL split users. In the scheme, the convex pricing function is an exponential pricing function of users' transmission power, which reflects the interference that macro users are suffering from UL/DL split users. It can ensure that UL/DL split users will be penalized when they cause serious interference to macro users. The power control scheme based on non-linear convex pricing obtains more improvements than the linear one with numerical simulations. Furthermore, a Dynamic Power Adjustment (DPA) algorithm is added to the scheme in order to mitigate interference in uplink and to speed up the convergence process.

With Device to Device (D2D) user equipment (DUE) sharing the same resources with traditional cellular user equipment (CUE), intra-cell interference is becoming a challenging issue nowadays in D2D underlaying network. However, due to the lack of convexity, globally optimal resource sharing scheme is generally unattainable. In this paper, we propose a genetic algorithm (GA) based user matching scheme (GAM) with optimal power allocation (OPA) to achieve the multi-dimension optimization. Firstly, we demonstrate that the optimal power allocation between DUE and CUE can be obtained by searching several finite solution sets in the feasible region boundries. Secondly, genetic algorithm is applied to obtain the near-optimal user matching in the whole network scope. Simulation results show that the GA based scheme can achieve 30Mbps system throughput gain comparing to the traditional greedy searching, while with a limited complexity increase.

In LTE-Advanced (LTE-A) and beyond networks, deploying complementary small cells on an existing macro layer is recognized as an attractive solution to improve the network capacity and provide seamless broadband services in local areas. However, existing cell discovery mechanism is tailored for homogeneous networks (macro only). User Equipment (UE) can't energy-efficiently detect the small cells on a dedicated carrier or maximally exploit the offloading opportunities provided by such heterogeneous deployments. In this paper, we propose a Predictive Connection time based Inter-frequency Measurement (PCIM) solution to cope with the problems. With the aid of the positioning results, the small cell connection time is derived, both user velocity and moving direction are taken into account. Using 3rd Generation Partnership Project (3GPP) LTE-A Heterogeneous Network (HetNet) mobility evaluation methodology, the proposed PCIM scheme is compared with some currently standardized techniques. Simulation results provide insights on the small cell discovery schemes in terms of energy efficiency and small cell usage efficiency, and demonstrate the benefits of the proposed PCIM scheme over the other alternatives.

With the growing concern over energy consumption, green communication is becoming more and more important. Lots of efforts have been put into investigating in energy-efficient wireless systems. Since Fractional Frequency Reuse (FFR) is a widely-used Inter-cell-interference coordination scheme in the OFDMA wireless network, we consider the energy-efficient power control in the FFR system. With demanded user rate constraints, we aim at minimizing the Joule consumed per transmission bit per-cell in the downlink. Based on Dinkelbach's theory, we transform the objective function into a parametric programming problem. By proving the problem is convex, we derive the optimal analytical solution by Karush-Kuhn-Tucker (KKT) conditions, along with an energy-efficient power control algorithm. Numerical results are given to verify our analysis, and demonstrate that the power control algorithm significantly improves the energy efficiency in FFR. The optimal frequency reuse factor is also given when considering the fairness of energy efficiency and power consumption.

In this paper, we investigate the beam squint problems in 60 GHz mm-wave wireless communication beamforming process. To minimize the degree of beam squint we formulate the objective task into a constrained optimization problem and derived a phase improvement scheme based on IEEE 802.15.3c codebook. For further improvement of system performance, main response axis (MRA) alignment scheme is proposed, the direction of MRA can be adjusted alignment exactly in different frequencies and can achieve higher array antenna gain with a lower side lobe level (SLL) and it is more effective than the first schemes, but requires more phase shifters. In order to reduce the complexity of MRA alignment scheme, we proposed MRA alignment simplify scheme, the scheme only require 1/2 phase shifters has a negligible performance loss compared with the second scheme. Extensive simulations have demonstrated that the proposed three schemes can suppress beam squint, improves system throughput and performs better than the existing scheme.

In this paper, we study the energy efficiency in the multiple access points (AP) coherent cooperative transmission (CCT) system with frequency-selective fading. Supposing the per antenna power constraint is sufficient, we derive the achievable optimal energy efficiency by distributing the transmit power among subchanels and cooperative APs, aiming at minimizing the Joule consumed per transmission bit of the CCT system. Based on Dinkelbach's theory, we solve our main problem with the associated parametric programming problem, and prove that the problem for the CCT can be transformed into an equivalent problem for the single AP transmission (SAT), which is much easier to be solved. The analytical solution is derived, along with an optimal power allocation algorithm. Numerical results are given to verify our analysis, and demonstrate that with the proposed scheme, the CCT outperforms the SAT in terms of the energy efficiency.

In this paper, we propose a novel compressive sensing (CS) based indoor positioning approach, which uses the signal strength differentials (SSDs) as location fingerprints (LFs). The target location is regarded as an unknown sparse location vector in the discrete spatial domain. Then it just takes a little number of online noisy SSD measurements for the exact recovery of the sparse location vector by solving an ℓ1-minimization program. In order to mitigate the influences of large measurements noise on the recovery accuracy, an LF space denosing algorithm is proposed to discriminate the localization contribution rate of every LF according to its SSD variation. Moreover, an LF space filtering strategy is also exploited to lower the high computational complexity of the CS recovery algorithm. Both experimental results and simulations demonstrate that we achieve remarkable improvements on the positioning performance of the CS based approach by using the two proposed algorithms.

This paper considers the scheduling and resource allocation with adaptive modulation in downlink coordinated multi-point transmission (CoMP) systems, where multiple users are served via zero-forcing precoding simultaneously by several cooperative base stations (BSs). The joint scheduling and resource allocation to maximize the sum rate is formulated as a combinational optimization problem under constraints. As the searching space for this problem is extremely large, which prohibits an exhaustive search (ES), a genetic algorithm (GA) based solution is proposed. In particular, a two-dimension-binary chromosome coding scheme is designed to denote potential user selection and bit loading strategies across multiple subchannels. To handle per-BS power constraint, a penalty based fitness function is used, and in doing so GA can search for optimal solution in a larger region. To ensure convergence, a super individual named elite is added to each population. Simulation results indicate that the proposed algorithm leads to significant sum rate gain compared to existing schemes, and provides close to ES performance but with much lower computational complexity.

In this paper, we study the subchannel power allocation problem to maximize the total throughput of the downlink orthogonal frequency division multiplexing (OFDM) multiple access points (APs) systems with non-coherent cooperative transmission. Although the problem has been claimed as a non-convex optimization problem, we prove that the duality gap between the original problem and its dual optimization problem is nearly zero when the number of subchannel is large, which implies solving the problem in the dual domain. Then, the problem is solved with the standard Lagrange dual decomposition method with an acceptable deviation. In addition, in order to reduce the complexity of the dual decomposition based method, we propose a suboptimal low-complexity algorithm, at a minor cost of the total throughput. Numerical results are also given to verify the proposed schemes.

The majority studies on resource allocation are based on continuous power allocation. However, the transmit power can be assumed as a finite set of discrete power levels only, especially for practical digital cellular systems. By simply rounding up or down, the conventional continuous power allocation algorithm will not suffice. In this paper, via transmitting signal on discrete power levels, multi-cell power allocation is modeled as a combinatorial optimization problem, which proved to be NP-hard. Ant colony optimization (ACO) is applied to get near-optimal solution of the problem. In particular, a multiple power level based searching graph is designed, and conventional ACO is improved that ant colonies in each cell cooperate to maximize system throughput. Simulation results indicate that with only 4 power levels, the proposed algorithm can achieve a significant rate gain over the existing schemes.

In this paper, we study the subchannel (SC) power allocation for orthogonal frequency division multiplexing (OFDM) multiple access points (APs) systems with non-coherent cooperative transmission. The objective is to maximize the total capacity under per-AP power constraints. It can be proved that the optimal solution can be obtained by the combination of an optimal SC partition search and the power allocation across SCs for each feasible partition. Existing work exhaustively searched the optimal SC partition and used Lagrange dual method to compute the power allocation across SCs. Since the entire complexity increases exponentially with the number of SCs, the existing method is unsuitable for practical implementation. In this paper, we propose a novel optimal power allocation algorithm for non-coherent cooperative transmission with a much lower complexity. Firstly, a concept of “cut-off SC” is proposed for searching the optimal SC partition. Then, an efficient optimal power allocation algorithm across SCs is proposed for any given cut-off SC. Simulation results demonstrate that the proposed algorithm is optimal with a polynomial complexity, and ends within an acceptable number of iterations.

In this paper, we address the problem of designing a joint scheduling and power control algorithm in a downlink coordinated multi-point (CoMP) cluster supporting CoMP joint transmission. The objective is to maximize the cell-edge throughput under per-point power constraints. By an analytical derivation, binary power control is proved to be the optimal solution for any given selected user group. Utilizing this analytical result, a centralized and a semi-distributed version of joint user selection and power control algorithms are proposed. Compared to algorithms without considering joint transmission and algorithms without considering power control, simulation results show that the proposed algorithms achieve a good trade-off between joint transmission and interference coordination, which helps to improve the cell-edge performance.

Multi-User Multiple Input Multiple Output (MU- MIMO) technique is introduced into 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) to enhance the system capacity and spectrum efficiency. On the other hand, Coordinated Multi-Point transmission and reception (CoMP) technique which can reduce inter-cell interference (ICI) significantly, has been adopted in LTE-Advanced as one of the promising techniques to increase the throughput of the cell edge user. However, some new problems appear with the promotion of MU-MIMO technique in the CoMP scenario. Considering the necessity that an enhanced algorithm should be proposed to fit the CoMP scenario, this paper presents a novel capacity maximization criterion for multiuser pairing strategy based on particle swarm optimization algorithm for CoMP MU-MIMO in uplink LTE-A system. Under this criterion, this paper gives two kinds of receivers which are zero forcing (ZF) equalizer and minimum mean square error (MMSE) equalizer in the frequency-domain. Simulation results show that the proposed PSO strategy achieves nearly the same performance to the exhaustive search (ES) algorithm in terms of capacity performance, but with lower complexity.

The two-tier femtocell network is comprised of a central macrocell underlaid with shorter range femtocell hotspots. Due to the universal frequency reuse, this kind of new system architecture brings about urgent problems of the interference management and the resource allocation. Motivated by these problems, the following contributions are made in this paper: 1) a novel joint power and subchannel allocation problem for Orthogonal Frequency Division Multiple Access (OFDMA) downlink based femtocells is formulated on the premise of minimizing Femto BSs' radiating interference; 2) a pseudo-handover based scheduling information exchange method is proposed to avoid the collision interference; 3) an iterative scheme of subchannel allocation and power control is proposed to solve the formulated problem, which is an NP-complete problem. Through simulations and comparisons with three other schemes, the proposed scheme shows better performance in reducing interference and the Femto BS's transmit power, and improving the spectrum efficiency.

In this paper, three joint processing schemes for the downlink are characterized and compared within a cluster of base stations. The motivation of this study is to analyze the performance of these schemes over the cluster area, as a first step towards designing an adaptive joint processing scheme supporting dynamic usage scenarios. Each one of the analyzed schemes, the centralized, partial and distributed joint processing approaches, requires a different amount of available channel knowledge at the transmitter side, inter-base information exchange and feedback from the users. In addition, these schemes show varying capabilities to serve the users depending on their location in the cluster area. Therefore, in a real scenario, an adaptive joint processing scheme encompassing the three schemes could be used by the cluster of base stations. Simulation results show that, assuming coherent transmission, the centralized joint processing scheme outperforms with 25% the partial joint processing scheme and with 50% the distributed joint processing approach in the cell edge when a backhaul-load weighted average sum-rate per cell metric is taken into account.

In this paper, we consider a static cluster of base stations where joint processing is allowed in the downlink. The partial joint processing scheme is a user-centric approach where subclusters or active sets of base stations are dynamically defined for each user in the cluster. In frequency selective channels, the definition of the subclusters or active set thresholding of base stations can be frequency adaptive (per resource block) or non-adaptive (averaged over all the resource blocks). Frequency adaptive thresholding improves the average sum-rate of the cluster, but at the cost of an increased user data interbase information exchange with respect to the non-adaptive frequency thresholding case. On the other hand, the channel state information available at the transmitter side to design the beamforming matrix is very limited and rank deficiency problems arise for low values of active set thresholding and users located close to the base station. To solve this problem, an algorithm is proposed that defines a cooperation area over the cluster where the partial joint processing scheme can be performed, frequency adaptive or non-adaptive, for a given active set threshold value.

Coordinated Multi-Point (CoMP) transmission is considered in 3GPP LTE-Advanced as a key technique to improve the cell-edge performance. In order to support joint resource allocation among coordinate cells in CoMP systems, efficient frequency reuse schemes need to be designed. However, most of the existing frequency reuse schemes are not suitable for CoMP transmission due to not considering multi-cell joint transmission scenario in their frequency reuse rule. To solve this problem, a cooperative frequency reuse (CFR) scheme is proposed in this paper, which divides the cell-edge area of each cell into two types of zones, and defines a frequency reuse rule to support CoMP transmission for users in these zones. Compared with the conventional soft frequency reuse (SFR) scheme, simulation results demonstrate that the CFR scheme reduces the blocking probability by more than 50%, and improves the cell-edge throughput by 30~40%, with 5~9% additional cell-average throughput.

The problem of resource allocation in multiuser OFDM system is a combinatorial optimization problem, difficult to solve in polynomial time. For the sake of reducing complexity, it can be solved either by relaxing constraints and making use of linear algorithms or by metaheuristic methods. In this paper, ant colony optimization, a typical algorithm of metaheuristic methods, is applied to solve the problem of resource allocation in multiuser OFDM system. The system model for the application of ACO on the problem, as well as two algorithms based on ACO, is proposed. Comparing to traditional strategies, it is indicated by numerical results that the proposed algorithms can significantly increase the throughput of the system and simultaneously guarantee fairness.

This paper presents Customer Satisfaction (CS) based resource allocation strategy in orthogonal frequency-division multiplexing (OFDM) wireless system with multimedia traffic. The risk aversion utility functions are analyzed, based on which, the CS utility and the CS resource allocation strategy are proposed. Compared with the Proportional Fairness (PF) utility, the CS utility enables the system to adjust its resource allocation according to both the traffic requirements and the resource situation. Numerical results demonstrate that the CS resource allocation strategy outperforms the PF strategy in both real-time (RT) traffic and best effort (BE) traffic.

In this paper, a novel channel allocation method is proposed to reduce the time-varying effects in downlink OFDMA systems. This method makes use of a Kalman filter in order to predict the user SINR and rate in the next time slot. Based on this estimated rate, the available subcarriers are assigned according to a rate greeding algorithm. Compared with the rate-craving greedy (RCG) algorithm and the rate allocation with fixed increase (RAFI) algorithm, the numerical results show that the proposed algorithm can effectively improve the throughput for a single user, averagely 3 to 7 % more than the RCG algorithm and 1 to 3 % more than the RAFI algorithm. Moreover, the total outage probability is also reduced as the number of users increases, averagely 8 to 11% more than the RCG algorithm and 3 to 7% more than the RAFI algorithm.

This paper proposes maximum utility principle slide handover strategy for multi-antenna cellular architecture. Based on generalized distributed cellular architecture-group cell, slide handover strategy is illustrated and its merits are presented. Slide antenna window is applied by slide handover in the handover process, which makes users always in the cell centre and eliminates cell-edge effect. But for traditional slide handover, the handover rules of adding new antenna elements and replacing or releasing existing antenna elements are only by the pilot strength of each antenna element. This will constrain the performance of slide handover. Therefore, the rules need to be enhanced. Maximum utility principle slide handover strategy, proposed by this paper, can effectively solve this problem. The utility function in the slide handover and steps for handover are described in this paper and system-level performance evaluation is provided with comparison of traditional slide handover to verify the merits of maximum utility principle slide handover strategy.

Abstract With current research focusing on beyond 3G (B3G)/4G mobile systems, many advanced techniques are investigated by world‐wide research institutes and standard organization, such as multi input multi output (MIMO), orthogonal frequency division multiplex (OFDM), and multi‐antenna distributed cellular network architecture. Based on these novel techniques, the radio resource management (RRM) strategies, such as access control, also need to be developed. This paper proposes the maximum utility principle access control (MUPAC) basing on Dijkstra's Shortest Path Algorithm for multi‐antenna cellular network architectures. In the accessing process of the proposed algorithm, the shortest path in Dijkstra's Algorithm is replaced by the cost of accessing process, which is represented by utility function. Taking Generalized Distributed Cellular Architecture—Group Cell as an example, MUPAC is described in details with the utility function, maximum utility principle, flow chart of accessing process. Performance evaluation and analyses verify the merits of MUPAC algorithm in improving system capacity, accessing success probability, and efficiency of system resources usage. Copyright © 2007 John Wiley & Sons, Ltd.

The inter-cell interference of orthogonal frequency division multiple access (OFDMA) based multi-cell distributed network architecture is analyzed. Based on generalized distributed cellular architecture-group cell, the interference condition without power control and with power control is analyzed respectively, and the system outage probability compared to traditional cellular structure is evaluated. Analyses and simulation results indicate that the inter-cell interference of group cell architecture does not increase more than traditional cellular structure. Moreover, the system resources of group cell architecture are centralized scheduled and allocated by the access point (AP), which makes it flexible to apply centralized SRA power control algorithm to improve the system performance further.

This paper introduces a general framework of B3G (Beyond 3G) mobile communication system in China TDD (time division duplex) Special Work Group. System architecture is described that allows the integration of multiple antenna techniques, e.g., MIMO (multiple input multiple output), in physical layer and the novel cellular architecture, e.g., group cell, in network layer. MIMO techniques based on group cell architecture, including spaced MIMO, distributed MIMO and virtual MIMO, are presented. What's more, a multi-MIMO matrix detection algorithm for distributed MIMO structure in Group Cell B3G TDD system is also presented and analyzed with the corresponding mathematical model. Link-level and system-level simulation results show the performance and capacity of this group cell MIMO system respectively

Noise variance and hence signal to noise ratio (SNR) estimates are very important for the channel quality control in communication systems. Noting that in mobile communications the multipath time delays are slowly varying in time, in this paper we derive a subspace-based estimation method for orthogonal frequency division multiplexing (OFDM) systems, which is based on an eigenvector decomposition of the estimated channel correlation matrix. Simulation results show that the proposed estimator can obtain accurate real time measurements of the noise variance and SNR after an observation interval of about 20 OFDM symbols for various fading channels.