This paper studies the fundamental limit of semantic communications over the discrete memoryless channel. We consider the scenario to send a semantic source consisting of an observation state and its corresponding semantic state, both of which are recovered at the receiver. To derive the performance limitation, we adopt the semantic rate-distortion function (SRDF) to study the relationship among the minimum compression rate, observation distortion, semantic distortion, and channel capacity. For the case with unknown semantic source distribution, while only a set of the source samples is available, we propose a neural-network-based method by leveraging the generative networks to learn the semantic source distribution. Furthermore, for a special case where the semantic state is a deterministic function of the observation, we design a cascade neural network to estimate the SRDF.For the case with perfectly known semantic source distribution,we propose a general Blahut-Arimoto(BA)algorithm to effectively compute the SRDF.Finally,experimental results validate our proposed algorithms for the scenarios with ideal Gaussian semantic source and some practical datasets.

Radio map is an advanced technology that mitigates the reliance of multiple-input multiple-output (MIMO) beamforming on channel state information (CSI). In this paper, we introduce the concept of deep learning-based radio map,which is designed to be generated directly from the raw CSI data. In accordance with the conventional CSI acquisition mechanism of MIMO,we first introduce two baseline schemes of radio map,i.e.,CSI prediction-based radio map and throughput predictionbased radio map.To fully leverage the powerful inference capability of deep neural networks, we further propose the end-to-end structure that outputs the beamforming vector directly from the location information. The rationale behind the proposed end-to-end structure is to design the neural network using a task-oriented approach,which is achieved by customizing the loss function that quantifies the communication quality. Numerical results show the superiority of the task-oriented design and confirm the potential of deep learning-based radio map in replacing CSI with location information.

Telecommunication has undergone significant transformations due to the continuous advancements in internet technology, mobile devices, competitive pricing,and changing customer preferences. Specifically,the most recent iteration of OpenAI’s large language model chat generative pre-trained transformer (ChatGPT) has the potential to propel innovation and bolster operational performance in the telecommunications sector.Nowadays, the exploration of network resource management,control, and operation is still in the initial stage. In this paper,we propose a novel network artificial intelligence architecture named language model for network traffic (NetLM), a large language model based on a transformer designed to understand sequence structures in the network packet data and capture their underlying dynamics. The continual convergence of knowledge space and artificial intelligence (AI) technologies constitutes the core of intelligent network management and control. Multi-modal representation learning is used to unify the multi-modal information of network indicator data, traffic data, and text data into the same feature space. Furthermore, a NetLM-based control policy generation framework is proposed to refine intent incrementally through different abstraction levels. Finally, some potential cases are provided that NetLM can benefit the telecom industry.

With the emerging applications of the Internet of things, artificial intelligence, and satellite communications, the future network will be featured as the Internet of everything around the globe. The network heterogeneity, applications cloudification, and personalized user services demand a revolutionary change in the network architecture. With the rapid development of cloud native technology,the new network should support heterogeneous networks and personalized quality of services for users. In this paper,we propose a Cybertwinbased cloud native network (CCNN) that merges the radio access network (RAN), the IP bearer network, and the data center network and is based on the cloud native data center network using Kubernetes as a network operating system for unified virtualization of computing, storage, and network resources, unified scheduling and allocation,and unified operation and management. Then, we propose a fully decoupled RAN architecture that can flexibly and efficiently utilize the resource for personlized user services. We also propose a Cybertwin-based management framework built on Kubernetes for integrated networking, computing and storage resource scheduling. Finally, we design an immunology-inspired intrinsic security architecture with zero trust security system and adaptive defense system. The proposed CCNN is a new network architecture expected to address future generation communications and networks challenges.

In recent years,with the rapid development of Internet and hardware technologies, the number of Internet of things(IoT)devices has grown exponentially. However,IoT devices are constrained by power consumption,making the security of IoT vulnerable.Malware such as Botnets and Worms poses significant security threats to users and enterprises alike. Deep learning models have demonstrated strong performance in various tasks across different domains, leading to their application in malicious software detection. Nevertheless, due to the power constraints of IoT devices, the well-performanced large models are not suitable for IoT malware detection. In this paper we propose a malware detection method based on Markov images and MobileNet, offering a cost-effective, efficient, and high-performing solution for malware detection. Additionally, this paper innovatively analyzes the robustness of opcode sequences.

Deep learning enables real-time resource allocation for ultra-reliable and low-latency communications (URLLC), one of the major use cases in the next-generation cellular networks. Yet the high training complexity and weak generalization ability of neural networks impede the practical use of the learning-based methods in dynamic wireless environments. To overcome these obstacles, we propose a parameter generation network(PGN)to efficiently learn bandwidth and power allocation policies in URLLC. The PGN consists of two types of fully-connected neural networks (FNNs). One is a policy network, which is used to learn a resource allocation policy or a Lagrangian multiplier function. The other type of FNNs are hypernetworks, which are designed to learn the weight matrices and bias vectors of the policy network. Only the hypernetworks require training.Using the well-trained hypernetworks,the policy network is generated through forward propagation in the test phase. By introducing a simple data processing, the hypernetworks can well learn the weight matrices and bias vectors by inputting their indices, resulting in low training cost. Simulation results demonstrate that the learned bandwidth and power allocation policies by the PGNs perform very close to a numerical algorithm.Moreover,the PGNs can be well generalized to the number of users and wireless channels, and are with significantly lower memory costs,fewer training samples,and shorter training time than the traditional learning-based methods.

Low-earth orbit (LEO) satellite networks ignite global wireless connectivity. However, signal outages and co-channel interference limit the coverage in traditional LEO satellite networks where a user is served by a single satellite. This paper explores the possibility of satellite cooperation in the downlink transmissions.Using tools from stochastic geometry, we model and analyze the downlink coverage of a typical user with satellite cooperation under Nakagami fading channels. Moreover, we derive the joint distance distribution of cooperative LEO satellites to the typical user.Our model incorporates fading channels, cooperation among several satellites, satellites’ density and altitude, and co-channel interference. Extensive Monte Carlo simulations are performed to validate analytical results. Simulation and numerical results suggest that coverage with LEO satellites cooperation considerably exceeds coverage without cooperation. Moreover,there are optimal satellite density and satellite altitude that maximize the coverage probability, which gives valuable network design insights.

Recently, holographic multiple-input multiple-output (HMIMO) has motivated its potential use to support high-capacity data transmission with spatially quasi-continuous apertures. As a practical instance of HMIMO, reconfigurable refractive surfaces (RRSs) equipped with numerous metamaterial elements are utilized as antennas by refracting incident signals from signal sources.In this paper,we investigate a multi-user communication system with an RRS deployed as the base station (BS)’s transmit antenna. To mitigate the high overhead of accurate channel state information (CSI) acquisition, the codebook design and beam training are employed to perform beamforming. Given the large scale of RRS, users are likely to be randomly distributed in both the near and far fields around the BS, which is unknown in advance. By considering radiation characteristics in both fields, a near-far field codebook is designed to be applicable to all users, regardless of their locations. To reduce overhead, a multi-user beam training is proposed to serve all users simultaneously by enhancing each codeword capable of covering multiple areas. Considering a general case that includes users in both fields,simulation results indicate that,without prior knowledge of user distribution, the proposed scheme outperforms state-of-the-art ones in terms of sum rate and overhead.

Penetration testing(PT)is an active method of evaluating the security of a network by simulating various types of cyber attacks in order to identify and exploit vulnerabilities. Traditional PT involves a time-consuming and labor-intensive process that is prone to errors and cannot be easily formulated. Researchers have been investigating the potential of deep reinforcement learning (DRL) to develop automated PT (APT) tools. However, using DRL in APT is challenged by partial observability of the environment and the intractability problem of the huge action space.This paper introduces RLAPT,a novel DRL approach that directly overcomes these challenges and enables intelligent automation of the PT process with precise control. The proposed method exhibits superior efficiency, stability, and scalability in finding the optimal attacking policy on the simulated experiment scenario.

We analyzed the performance of a freespace optical(FSO)system in this study, considering the combined effects of atmospheric turbulence, fog absorption, and pointing errors. The impacts of atmospheric turbulence and foggy absorption were modeled using the Fisher-Snedecor F distribution and the Gamma distribution,respectively.Next,we derived the probability density function (PDF) and cumulative probability density function of the optical system under these combined effects. Based on these statistical findings, closed-form expressions for various system metrics, such as outage probability, average bit error rate (BER), and ergodic capacity, were derived. Furthermore, we used a deep neural network(DNN)to predict the ergodic capacity of the system,achieving reduced running time and improved accuracy. Finally, the accuracy of the prediction results was validated by comparing them with the analytical results.

This paper introduces a deep learning(DL) algorithm for estimating doubly-selective fading channel and detecting signals in orthogonal frequency division multiplexing (OFDM) communication systems affected by hardware impairments (HIs). In practice, hardware imperfections are present at the transceivers, which are modeled as direct current(DC) offset, carrier frequency offset (CFO), and in-phase and quadrature-phase (IQ) imbalance at the transmitter and the receiver in OFDM system. In HIs, the explicit system model could not be mathematically derived, which limits the performance of conventional least square (LS) or minimum mean square error (MMSE) estimators. Thus, we consider time–frequency response of a channel as a 2D image, and unknown values of the channel response are derived using known values at the pilot locations with DL-based image super-resolution,and image restoration techniques. Further, a deep neural network (DNN) is designed to fit the mapping between the received signal and transmit symbols, where the number of outputs equals to the size of the modulation order. Results show that there are no significant effects of HIs on channel estimation and signal detection in the proposed DL-assisted algorithm. The proposed DL-assisted detection improves the OFDM performance as compared to the conventional LS/MMSE under severe HIs.

This paper analyses the performance of fullduplex(FD)dual-hop vehicular cooperative network with decode-and-forward (DF) protocol. At FD relay nodes, we examine the effects of non-linear hybrid power time splitting (PTS) based energy harvesting (EH). All three nodes—source (S), relay (R), and destination (D)—are assumed to be moving vehicles. The expressions for the system’s outage probability (OP) over double (cascaded) Rayleigh fading channels are derived. We also analyse the impact of residual self-interference (RSI) caused at FD relay on system’s performance. We compare the performance of system with two relay selection techniques, namely,maximum channel gain-based(Max-G)relay selection and minimum RSI-based(Min-SI)relay selection. This paper considers the joint effect of time splitting ratio and self-interference cancellation (SIC) level to find the optimum EH duration. Additionally, the effect of time splitting ratio and average signal-to-noise ratio(SNR)on outage and throughput performance of the system are also investigated in this paper. The derived expressions are validated through Monte Carlo simulations.

This paper explores the ergodic channel capacity of multiple-input single-output(MISO)free-space optical (FSO) communication systems, assisted by (optical) re-configurable intelligent surfaces[(O)RIS], made of concave reflectors. On the one hand, RIS technology mitigates dead zones in communication systems. Additionally, it increases the data rate and communication range, enhances the communication channel by making it intelligent,and improves the system’s capacity. Finally, the RIS technology improves the spectrum and energy efficiencies of the considered systems. On the other hand, transmitting diversity mitigates deep fade and helps to achieve beamforming to regulate the beam sent in a specific direction. Finally, multiple light sources help to send different versions of the same information at other time slots. Furthermore, compared to flat reflectors, concave mirrors provide economic advantages enabled by their natural shape, which helps converge the impinging light beams into the same focal point. In this paper, we harness the full potential of ORIS and MISO technologies in an FSO system by exploiting the hollow of concave reflectors to focus the reflected beams on a single user.We derive an approximated closed-form expression, provide results of the proposed ORIS-aided FSO systems’ergodic channel capacity,and discuss the suitable type of concave reflector. These results show that all types of concave mirrors provide similar results except when the thickness of the reflector is large enough to impact the reflected light.

This paper introduces an innovative approach to address the trajectory optimization challenge for cellular-connected unmanned aerial vehicles (UAVs) operating in three-dimensional(3D)space. In most cases, optimizing UAV trajectories necessitates ensuring reliable network connectivity. However, achieving dependable connectivity in 3D space poses a significant challenge due to terrestrial base stations primarily designed for ground users. Additionally, UAVs possess network information only for the areas they have visited, with global network information being inaccessible. To address this issue, we propose a collaborative approach in which multiple UAVs create a global model of outage probability using federated learning, enabling more precise and effective trajectory design. Building upon the constructed global information, we conduct the trajectory design. Initially, we introduce A-star(A*)algorithm for trajectory design in small-scale scenarios. Nevertheless, recognizing the limitations of A* algorithm in large-scale scenarios, we further introduce improved rapidly-exploring random trees (RRTs) algorithm for weighted path optimization. Simulation results are provided to validate the effectiveness of the proposed algorithms.

Domain name system (DNS) amplification distributed denial of service(DDoS)attacks are one of the popular types of intrusions that involve accessing DNS servers on behalf of the victim. In this case, the size of the response is many times greater than the size of the request, in which the source of the request is substituted for the address of the victim. This paper presents an original method for countering DNS amplification DDoS attacks. The novelty of our approach lies in the analysis of outgoing traffic from the victim’s server. DNS servers used for amplification attacks are easily detected in Internet control message protocol(ICMP)packet headers (type 3,code 3)in outgoing traffic. ICMP packets of this type are generated when accessing closed user datagram protocol (UDP) ports of the victim, which are randomly assigned by the Saddam attack tool. To prevent such attacks, we used a Linux utility and a software-defined network (SDN) module that we previously developed to protect against port scanning. The Linux utility showed the highest efficiency of 99.8%, i.e., only two attack packets out of a thousand reached the victim server.

Intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) is a promising technology for prolonging the lifetime of the users and improving system performance. However, it is challenging to obtain the perfect channel state information (CSI) at the base station (BS) due to the lack of radio frequency(RF)chains at the IRS,which becomes even harder when eavesdroppers (Eves) exist. In this paper,we study the power issues in the IRS-aided SWIPT system under the imperfect CSI of BS-IRS-Eves links. We aim to minimize the transmitting power at the BS by jointly designing transmit beamforming and reflective beamforming with subject to the rate outage probability constraints of the Eves,the energy harvesting constraints of the energy receivers (ERs), and the minimum rate constraints of the information receivers (IRs). We use Bernstein-type inequality to solve the rate outage probability constraints. Afterward, the formulated nonconvex problem can be efficiently tackled by employing alternating optimization(AO)combined with semidefinite relaxation (SDR) and penalty convex-concave procedure (CCP).Numerical results demonstrate the effectiveness of the proposed scheme.

In order to increase the capacity of future satellite communication systems, faster-than-Nyquist (FTN) signaling is increasingly considered. Existing methods for compensating for the high power amplifier (HPA) nonlinearity require perfect knowledge of the HPA model. To address this issue, we analyze the FTN symbol distribution and propose a complex-valued deep neural network (CVDNN) aided compensation scheme for the HPA nonlinearity,which does not require perfect knowledge of the HPA model and can learn the HPA nonlinearity during the training process. A model-driven network for nonlinearity compensation is proposed to further enhance the performance.Furthermore,two training sets based on the FTN symbol distribution are designed for training the network.Extensive simulations show that the Gaussian distribution is a good approximation of the FTN symbol distribution. The proposed model-driven network trained by employing a Gaussian distribution to approximate an FTN signaling can achieve a performance gain of 0.5 dB compared with existing methods without HPA’s parameters at the receiver. The proposed neural network is also applicable for non-linear compensation in other systems,including orthogonal frequency-division multiplexing(OFDM).

Due to the gradual scarcity of spectrum resources, orbital angular momentum (OAM) technology has been proposed and developed continuously to broaden channel capacity. To solve this problem, some ultra-wideband reflective phase metasurface antennas working in millimeter band are designed to generate high purity vortex waves which carry OAM. Based on the Pancharatnam-Berry (PB) phase concept, the unit cell is composed of a metasurface, dielectric, metal grounding layer. Through the optimization design of the unit structure parameters,the reflected wave efficiency can be as high as 95% when covering 2π rotating phase, which realizes the basic requirements of PB phase concept and the relative bandwidth of 116%. Then the metasurface arrays are arranged according to vortex wave generation formula and phase compensation principle.In the 25 GHz to 35 GHz frequency wide band,integer(l=±1,±2,±3) decimal(l=±1.5)and high-mode(l=±8)OAM vortex beams are generated, respectively. The OAM purity analysis shows that the antennas can generate millimeter wave OAM beams with high purity in a wide band range, and with a maximum gain of up to 23.6 dBi.

Channel knowledge map(CKM)has recently emerged as a viable new solution to facilitate the placement and trajectory optimization for unmanned aerial vehicle (UAV) communications, by exploiting the siteand location-specific radio propagation information. This paper investigates a CKM-assisted multi-UAV wireless network, by focusing on the construction and utilization of CKMs for multi-UAV placement optimization. First, we consider the CKM construction problem when data measurements for only a limited number of points are available. Towards this end, we exploit a data-driven interpolation technique, namely the Kriging method, to construct CKMs to characterize the signal propagation environments. Next, we study the multi-UAV placement optimization problem by utilizing the constructed CKMs,in which the multiple UAVs aim to optimize their placement locations to maximize the weighted sum rate with their respectively associated ground base stations(GBSs). However, the weighted sum rate function based on the CKMs is generally non-differentiable, which renders the conventional optimization techniques relying on function derivatives inapplicable. To tackle this issue,we propose a novel iterative algorithm based on derivative-free optimization, in which a series of quadratic functions are iteratively constructed to approximate the objective function under a set of interpolation conditions, and accordingly, the UAVs’ placement locations are updated by maximizing the approximate function subject to a trust region constraint. Finally, numerical results are presented to validate the performance of the proposed designs.It is shown that the Kriging method can construct accurate CKMs for UAVs. Furthermore, the proposed derivative-free placement optimization design based on the Kriging-constructed CKMs achieves a weighted sum rate that is close to the optimal exhaustive search design based on ground-truth CKMs, but with much lower implementation complexity.In addition,the proposed design is shown to significantly outperform other benchmark schemes.

The existing radio access network (RAN) is facing many challenges to meet the very strict speed and latency requirements by different mobile applications in addition to the increasing pressure to reduce operating cost. Innovation and development in RAN have been accelerated to tackle these challenges and to define how next generation mobile networks should look like. The role of machine learning (ML) and artificial intelligence (AI) driven innovations within the RAN domain is strengthening and attracting lots of attention to tackle many of the challenging problems. In this paper we surveyed RAN network base stations(BSs)clustering and its applications in the literature. The paper also demonstrates how to leverage community detection algorithms to understand underlying community structures within RAN. Tracking areas(TAs)novel framework was developed by adapting existing community detection algorithm to solve the problem of statically partitioning a set of BSs into TA according to mobility patterns. Finally, live network dataset in dense urban part of Cairo is used to assess how the developed framework is used to partition this part of the network more efficiently compared to other clustering techniques. Results obtained showed that the new methodology saved up to 34.6% of inter TA signaling overhead and surpassing other conventional clustering algorithms.

This paper investigates the secure transmission for reconfigurable intelligent surface (RIS)-assisted wireless communication systems. In the studied model, one user connects to the access point via a RIS while an eavesdropper eavesdrops on the signal sent from the user to the access point. Therefore, it is necessary to design an appropriate RIS reflection vector to solve the eavesdropping problem. This problem is formulated as an optimization problem whose goal is to maximize the secure energy efficiency which is defined as the ratio of the secure rate to the total energy consumption of the system via jointly optimizing the RIS reflection reflector as well as the number of RIS elements, which results in a non-convex optimization problem that is intractable to solve by traditional methods. To tackle this issue, a new algorithm by leveraging the advance of the established deep learning (DL) technique is proposed so as to find the optimal RIS reflection vector and determine the optimal number of RIS reflection elements. Simulation results show that the proposed method reaches 96% of the optimal secure energy efficiency of the genie-aided algorithm.

In this work, evolutionary algorithms are applied for the first time to achieve better radiation characteristics over the conventional beamforming algorithm in the concentric hexagonal antenna array (CHAA), which improves the performance of wireless communication. Multiple signal classification(MUSIC)algorithm is employed for direction of arrival (DoA) estimation. The conventional adaptive beam steering algorithm, least mean-square (LMS) algorithm, is used to steer the beam. Further, the proposed approach is employed by novel particle swarm optimization (NPSO) to reduce sidelobe level(SLL)even further. A six-ring CHAA with 126 elements for DoA estimation and beam steering is simulated. The simulation results of the MUSIC, LMS, NPSO,and particle swarm optimization(PSO)algorithms are provided for various DoAs.

Most of the existing automatic modulation recognition (AMR) studies focus on optimizing the network structure to improve performance, without fully considering cooperation among the basic networks to play their respective advantages. In this paper, we propose a robust and efficient collaboration framework based on the combination scheme(CFCS).This scheme effectively explores the spatial and temporal characteristics of complex signals by associating the advantages of convolutional neural network (CNN) and long and short-term memory(LSTM)network. In addition,the robustness of the CFCS is verified by transfer learning. Experiments demonstrate that the recognition rate of CFCS for highorder modulation signals such as 64QAM,128QAM,and 256QAM is more than 90% at high signal-to-noise ratios (SNRs),and 24 modulation types are effectively identified. Moreover, CFCS was transferred from RML2018.01a to RML2016.10b using transfer learning, which can still be deployed efficiently while reducing the training time by 20%. The CFCS has strong generalization ability and excellent recognition performance.

This paper considers the problem of joint beamformer design for a two-tier wireless network, whereby a set of cache-enabled access points (APs) are connected to the base station (BS) via wireless backhaul links. The APs can prefetch and store the files requested by users,to serve users directly in the access links. Thus low-latency transmissions are enabled as the transmission in the backhaul links is saved.However,due to the limited cache capacity,not all requested files can be stored in the APs,some of the non-cached APs then should be utilized as long as their transmission delays in the access and backhaul links can be well addressed. Two delay optimal beamformer design(DOBD)problems are formulated to minimize the overall delay incurred in the backhaul and access link transmissions via a joint optimization of the beamformer at the BS and APs. We consider the DOBD problem under non-fragment and fragment caching policies, both involving nonconvex link rate constraints. The semi-definite relaxation(SDR)and sequential convex approximation (SCA) schemes are adopted to approximate the nonconvex problems into convex ones,which are then iteratively solved. Numerical results demonstrate the convergence and improved transmission delay performance of the proposed scheme under various network settings.

The space-air-ground integrated networks (SAGIN) has emerged as a critical paradigm to address the growing demands for global connectivity and enhanced communication services. This paper gives a thorough review of the strategies and methodologies for resource allocation within SAGIN, focusing on the challenges and solutions within its complex structure. With the advent of technologies such as 6G, the dynamics of resource optimization have become increasingly complex, necessitating innovative approaches for efficient management. We examine the application of mathematical optimization, game theory, artificial intelligence (AI), and dynamic optimization techniques in SAGIN,offering insights into their effectiveness in ensuring optimal resource distribution, minimizing delays, and maximizing network throughput and stability. The survey highlights the significant advances in AI-based methods,particularly deep learning and reinforcement learning, in tackling the inherent challenges of SAGIN resource allocation. Through a critical review of existing literature, this paper categorizes various resource allocation strategies, identifies current research gaps, and discusses potential future directions. Our findings highlight the crucial role of integrated and intelligent resource allocation mechanisms in realizing the full potential of SAGIN for next-generation communication networks.

The emergence of massive ultra-reliable and low latency communications (mURLLC) as a category of age/time/reliability-sensitive service over 6G wireless networks has received considerable research attention, which has presented unprecedented challenges. As one of the key enablers for 6G,satellite-terrestrial integrated networks (STIN) have been developed to offer more expansive connectivity and comprehensive 3D coverage in space-aerial-terrestrial domains for supporting 6G mission-critical mURLLC applications while fulfilling diverse and rigorous quality of service (QoS) requirements. In the context of these mURLLC-driven satellite services, data freshness assumes paramount importance, as outdated data may engender unpredictable or catastrophic outcomes.To effectively measure data freshness in satellite-terrestrial integrated communications,age of information(AoI)has recently surfaced as a new dimension of QoS metric to support time-sensitive applications. It is crucial to design new analytical models that ensure stringent and diverse QoS metrics bounded by different key parameters,including AoI,delay,and reliability,over 6G satellite-terrestrial integrated networks. However,due to the complicated and dynamic nature of satellite-terrestrial integrated network environments, the research on efficiently defining new statistical QoS provisioning schemes while taking into account varying degrees of freedom has still been in their infancy. To remedy these deficiencies, in this paper we develop statistical QoS provisioning schemes over 6G satellite-terrestrial integrated networks in the finite blocklength regime. Particularly, we firstly introduce and review key technologies for supporting mURLLC.Secondly,we formulate a number of novel fundamental statistical-QoS metrics in the finite blocklength regime.Finally,we conduct a set of simulations to validate and evaluate our developed statistical QoS provisioning schemes over satellite-terrestrial integrated networks.

Maritime communication network is affected by many factors such as meteorological environment,resource constraints, communication distance, and so on, resulting in task packet loss, failure, and node overload when the task is offloaded and executed. In this paper, in order to improve quality of service(QoS)of maritime edge computing networks, a task offloading scheme is proposed. Considering the characteristic of the ship, we analyze ship position and network connectivity, and the ship motion model is constructed. The task offloading link formed with ship as node. With delay and energy consumption as constraints,the revenue function for task offloading path is designed.The function comprehensively considers network resources, channel transmission and node execution ability,thereby achieving the reliable task offloading. Simulation results prove that the proposed scheme can effectively improve the success rate of task execution,reduce the network packet loss rate and ensure the network resources load balancing, which effectively improve the QoS of maritime edge computing networks.

Edge computation offloading has made some progress in the fifth generation mobile network (5G). However, load balancing in edge computation offloading is still a challenging problem. Meanwhile, with the continuous pursuit of low execution latency in 5G multi-scenario, the functional requirements of edge computation offloading are further exacerbated. Given the above challenges,we raise a unique edge computation offloading method in 5G multi-scenario, and consider user satisfaction. The method consists of three functional parts: offloading strategy generation,offloading strategy update, and offloading strategy optimization. First, the offloading strategy is generated by means of a deep neural network (DNN), then update the offloading strategy by updating the DNN parameters. Finally, we optimize the offloading strategy based on changes in user satisfaction. In summary,compared to existing optimization methods, our proposal can achieve performance close to the optimum. Massive simulation results indicate the latency of the execution of our method on the CPU is under 0.1 seconds while improving the average computation rate by about 10%.

In the advent of the 6G era, non-terrestrial networks (NTN) with expansive coverage are being increasingly recognized as a vital supplement to cellular networks for facilitating seamless communication. The intricate interplay between network performance and service quality necessitates a thorough investigation into the modeling and analysis of services for efficient construction of NTN.Previous studies on service analysis, predominantly focused on terrestrial networks,fall short in addressing the unique challenges posed by NTN,particularly those related to platform distribution and antenna gain modeling. This deficiency in research,coupled with the varying preferences of users for different network types, forms the basis of this study. This paper explores the spatio-temporal characteristics of services within a multi-layered NTN framework.In this context,the spatial distribution of the platforms is modeled using a binomial point process, and the antennas are characterized by a sectorized beam pattern. We derive the closed-form expressions for the association probability,the number of accessed users, and the arrival rate of services with certain delay requirements towards different types of NTN. Simulation results are provided to evaluate the influence of various parameters on the association probability, the number of accessed users, and the total arrival rate of services. The number of satellites can be determined to achieve the optimal system utility,balancing the accessed services, offloading effects, and launching costs. This initial investigation lays the groundwork for further theoretical progress in the service analysis and platform deployment of NTN.

The development of space-air-ground integrated networks (SAGIN) requires sophisticated satellite Internet emulation tools that can handle complex, dynamic topologies and offer in-depth analysis. Existing emulation platforms struggle with challenges like the need for detailed implementation across all network layers, real-time response, and scalability. This paper proposes a digital twin system based on microservices for satellite Internet emulation,namely Plotinus,which aims to solve these problems. Plotinus features a modular design, allowing for easy replacement of the physical layer to emulate different aerial vehicles and analyze channel interference. It also enables replacing of path computation methods to simplify testing and deploying algorithms. In particular, Plotinus allows for real-time emulation with live network traffic,enhancing practical network models. The evaluation result shows Plotinus’s effective emulation of dynamic satellite networks with real-world devices. Its adaptability for various communication models and algorithm testing highlights Plotinus’s role as a vital tool for developing and analyzing SAGIN systems, offering a cross-layer,real-time,and scalable digital twin system.

Reconfigurable intelligent surface(RIS)is a promising solution to deal with the blockage-sensitivity of millimeter wave band and reduce the high energy consumption caused by network densification. However, deploying large scale RISs may not bring expected performance gain due to significant channel estimation overhead and non-negligible reflected interference.In this paper,we derive the analytical expressions of the coverage probability, area spectrum efficiency(ASE)and energy efficiency (EE)of a downlink RIS-aided multi-cell network.In order to optimize the network performance, we investigate the conditions for the optimal number of training symbols of each antenna-to-antenna and antenna-to-element path (referred to as the optimal unit training overhead) in channel estimation.Our study shows that:1)RIS deployment is not“the more, the better”, only when blockage objects are dense should one deploy more RISs; 2) the coverage probability is maximized when the unit training overhead is designed as large as possible;3)however,the ASE-and-EE-optimal unit training overhead exists. It is a monotonically increasing function of the frame length and a monotonically decreasing function of the average signal-to-noise-ratio (in the high signal-to-noise-ratio region). Additionally,the optimal unit training overhead is smaller when communication nodes deploy particularly few or many antennas.

Underwater optical wireless communication (UOWC) technology facilitates high-speed data transmission among multiple nodes in underwater networks. Nevertheless, the absence of a common clock poses a challenge to achieving systematic and reliable access for multiple nodes within these networks. This paper presents a time synchronization method for UOWC networks to ensure the successful execution of the media access control (MAC) protocol. In this method, the node obtains timestamps by exchanging messages with the optical access point (OAP). Subsequently, the node calculates the clock drift relative to the OAP and the propagation time,ensuring that transmitted data packets can arrive approximately at the time specified by the OAP. To validate the effect of the proposed method, an experimental UOWC prototype, including the OAP and nodes, is implemented using field programmable gate array (FPGA). The experimental results demonstrate that the maximum difference between the actual arrival times of two data packets that are expected to reach the OAP simultaneously according to the MAC protocol meets the requirements of the quasi-synchronous code division multiple access (QS-CDMA) system, thereby substantiating the effectiveness of this synchronization method.

Resonant beam communications (RBCom), which adopts oscillating photons between two separate retroreflectors for information transmission, exhibits potential advantages over other types of wireless optical communications (WOC). However, echo interference generated by the modulated beam reflected from the receiver affects the transmission of the desired information. To tackle this challenge, a synchronization-based point-to-point RBCom system is proposed to eliminate the echo interference, and the design for the transmitter and receiver is discussed. Subsequently,the performance of the proposed RBCom is evaluated and compared with that of visible light communications(VLC)and free space optical communications (FOC). Finally, future research directions are outlined and several implementation challenges of RBCom systems are highlighted.

To solve the problem of large positioning error and discontinuous positioning of special forces members when moving in cross-region indoors and outdoors, and to compensate for the linearization error and local convergence problem that may exist in the extended Kalman filter(EKF)in nonlinear systems, that is,the iterative results may be trapped in a local optimum situation, a seamless indoor-outdoor switching localization algorithm based on cubature Kalman filter (CKF) is proposed. CKF does not require the computation of Jacobian matrices, which can improve computational efficiency and filtering accuracy to a certain extent. In the system, an inertial measurement unit (IMU) is employed to correct the positioning errors of ultra-wideband (UWB)and BeiDou navigation satellite system(BDS).The positioning data from UWB and BeiDou in cross-region are weighted fused and then fused with the data from the IMU using CKF to obtain the final accurate positioning information. This study designs a scene-switching mechanism to achieve seamless switching between indoor and outdoor positioning scenes. By jointly analyzing the positioning accuracy of UWB and BeiDou,the positioning scene is determined, and appropriate counting thresholds are set to avoid frequent erroneous scene switches. Experimental results show that the proposed algorithm achieves a positioning accuracy of approximately 21.7 cm in cross-region,which can enable seamless integration of indoor and outdoor positioning,avoid positioning jumps, and enhance positioning accuracy.