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.

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.

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.

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.

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.

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.

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 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.

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.

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

In 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.

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.

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.

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.

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.

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.

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.

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

In 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.

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.

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.

This paper presents a transformable RHex-inspired robot, RHex-T3, with high energy efficiency, excellent flexibility and versatility. By using the innovative 2-DoF transformable structure, RHex-T3 inherits most of RHex’s mobility, and can also switch to other 4 modes for handling various missions. The wheel-mode improves the efficiency of RHex-T3, and the leg-mode helps to generate a smooth locomotion when RHex-T3 is overcoming obstacles. In addition, RHex-T3 can switch to the claw-mode for transportation missions, and even climb ladders by using the hook-mode. The simulation model is conducted based on the mechanical structure, and thus the properties in different modes are verified and analyzed through numerical simulations.

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.

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.

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.

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.

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.

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.

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.

A non-cooperative game approach is employed to optimize the energy efficiency (EE) for multi-user cognitive radio over multi-input-multi-output (MIMO) interference channels (ICs). Both the per-secondary-user (SU) power constraints and the total interference threshold are taken into consideration in the problem formulation. Although optimizing EE for the formulated multi-constraint fractional problem is non-convex and multi-objective, we show that it can be reformulated as an equivalent multi-objective unconstrained non-fractional problem. A distributed iterative EE optimization algorithm (DIEEOA) for multi-user cognitive radio over MIMO ICs is proposed to achieve the Nash Equilibrium of the non-cooperative game. Effectiveness of the algorithm is validated through computer simulation, and system parameters' impact on the EE is discussed.