We introduce a template-driven triangulation framework for embedding discrete boundaries into a regular triangular grid, enabling raster- or segmentation-derived domains to support structure-preserving, numerically stable PDE discretization. Unlike constrained Delaunay triangulation (CDT), which requires global connectivity updates, our method retriangulates only boundary-intersecting triangles, preserving the base mesh and enabling synchronization-free parallel execution. To ensure determinism and scalability, all local intersection patterns are classified under discrete equivalence and triangle symmetry, forming a finite symbolic lookup table mapping each case to a conflict-free retriangulation template. The resulting mesh is provably closed, angle-bounded, and compatible with cotangent-based discretizations and finite element methods. Numerical experiments – including elliptic and parabolic PDEs, signal interpolation, and structural evaluation – demonstrate fewer slivers, more equilateral elements, and greater geometric fidelity near complex boundaries. These properties make the framework well suited for real-time geometric analysis and physically grounded simulation over image-derived domains.

This paper investigates latency-constrained resource synergization for mission-oriented non-terrestrial networks (NTNs) in post-disaster emergency scenarios. When terrestrial infrastructures are damaged, unmanned aerial vehicles (UAVs) equipped with edge information hubs (EIHs) are deployed to provide temporary coverage and synergize communication and computing resources for rapid situation awareness. We formulate a joint resource configuration and location optimization problem to minimize overall resource costs while guaranteeing stringent latency requirements. Through analytical derivations, we obtain closed-form optimal solutions that reveal the fundamental tradeoff between communication and computing resources, and develop a successive convex approximation method for EIH location optimization. Simulation results demonstrate that the proposed scheme achieves approximately 20% cost reduction compared with benchmark approaches, validating its optimality and effectiveness for mission-critical emergency response applications in the sixth-generation (6G) era.

Artificial intelligence-agent communication networks (ACNs) in the sixth-generation (6G) enable collaborative task execution among agents and butler. However, compared with traditional mobile edge computing (MEC), in ACNs, a large number of agents possess comparable computing capabilities and task proportions need to be jointly determined rather than being predefined, causing high optimization complexity in large-scale deployment scenarios. In this paper, we propose an effective framework to solve the large-scale coupled optimization problem under acceptable complexity. Specifically, we model the joint task allocation, resource allocation, task offloading and computation frequency adjustment problem as an NP-hard nonconvex mixed-integer nonlinear programming (MINLP) problem, and derive its lower bound through Lagrangian relaxation. We decompose the problem into resource allocation and task offloading subproblems, which are solved via proximal policy optimization (PPO) and minimum-cost models within a block coordinate descent (BCD) framework. Simulations demonstrate the tightness of the lower bound, achieving stable convergence for 30 agents while reducing latency from 150 ms to 50 ms and the energy consumption by 28.18%. Our algorithm has great potential for ACNs with many agents deployed in future 6G scenarios, such as autonomous vehicles, robotic swarms and precision telemedicine.

In industrial automation or emergency rescue, sensors and robots work together with the help of an edge information hub (EIH) containing both communication and computing modules. Typically, the EIH collects the sensing data via the sensor-to-EIH link, processes data and then makes decisions on board before sending commands to the robot via the EIH-to-robot link. This forms a sensing-communication-computing-control (SC3) closed loop. In practice, the inherent openness of wireless links within the closed loop leads to susceptibility to eavesdropping. To this end, this paper refines the conventional physical layer security (PLS) approach with a systematic thinking to safeguard the SC3closed loop. The closed-loop negentropy (CNE), a new metric for the performance of the whole SC3closed loop, is maximized under the closed-loop security constraint. The transmit time, power, bandwidth of both wireless links, and the computing capability, are jointly designed. The optimization problem is non-convex. We leverage the Karush-Kuhn-Tucker (KKT) conditions and the monotonic optimization (MO) theory to derive its globally optimal solution. Simulation results show the performance gain of the proposed systematic approach, and reveal the advantage of exploiting the closed-loop structure-level PLS over the link-level or sum-link-level designs.

Future sixth generation (6G) communications are expected to support robotic control tasks in applications such as industrial automation and emergency response, where sensors, computing units, and robots are interconnected via nervous system-like networks to form sensing-communication-computingcontrol (SC 3 ) closed loops.However, the limited battery capacities of devices within these SC 3 loops constrain operational duration and degrade control efficiency, particularly in remote or postdisaster scenarios.To address this challenge, wireless power transfer (WPT) can be leveraged to provide continuous energy supply for SC 3 closed loops.In this paper, we investigate a wireless-powered SC 3 system, where a satellite transfers energy via radio frequency (RF) signals to support the communication and computing processes of multiple SC 3 closed loops.By accounting for the intricate coupling among computing, communication, and energy transfer, we propose a holistic design framework to enhance overall control performance.Specifically, we adopt the linear quadratic regulator (LQR) cost as the performance metric and formulate a sum LQR cost minimization problem.The uplink/downlink transmit power, bandwidth allocation, computing capability, communication/computing time allocation, and WPT power allocation are jointly optimized.We recast the problem into a more tractable form and develop an iterative algorithm to solve it.For the special case of a single loop, we further analyze the properties of optimal solutions in energylimited scenarios to provide insights for practical parameter configuration.Simulation results demonstrate the performance gains of the proposed scheme.

This paper investigates a secure beam-scanning framework for near-field integrated sensing and communication (ISAC) systems, driven by location division multiple access (LDMA). Specifically, an ISAC base station performs full-map robust beam-scanning within each transmission cycle, aiming to simultaneously detect potential eavesdroppers (Eves) and ensure secure communication for legitimate users (Bobs). Based on the Bobs’ channel state information (CSI) obtained at the cycle’s start and the estimated CSI of Eves sensed in the previous cycle, we formulate a robust optimization problem. This problem jointly optimizes the hybrid analog-digital precoding and time allocation for beam-scanning, with the objective of maximizing the worst-case average sum secrecy rate. To simplify the solution process, we first eliminate or relax the semi-infinite constraints caused by uncertain multipath channels from two perspectives: convex hull and bounded uncertainty. Subsequently, we design a near-field LDMA codebook in both azimuth and distance domains to construct ideal radar beampatterns for covering and partitioning the spatial scanning region. We also develop efficient analog precoders to significantly reduce computational complexity. Based on the convex hull model, we develop a low-complexity alternating optimization (AO) algorithm. In addition, for the bounded uncertainty model, we propose a semidefinite relaxation-based AO algorithm without requiring a rank-one constraint. Simulation results demonstrate that the proposed framework enables effective full-map Eves sensing while guaranteeing secure communication for Bobs. Moreover, the convex hull-based algorithm exhibits superior robustness and scalability compared to conventional bounded uncertainty approaches.

The existing unmanned aerial vehicle (UAV) enabled integrated sensing and communications (ISAC) systems primarily focus on the sensing capabilities of the UAV itself, overlooking the fact that the existing ground access points (APs) can receive the reflected signals for passive sensing, which can enhance the overall sensing performance. To address this issue, this paper introduces a UAV empowered air-ground ISAC system, where a UAV serves multiple communication users and performs detection for a potential target simultaneously, with the help of several ground APs. Specifically, the UAV works in active mode by transmitting ISAC signals and extracting information from echoes reflected from the target. In contrast, the ground APs function as sensing receivers, receiving and processing the reflected sensing signals from the target. Considering the limited capacity of the wireless backhaul links, we propose a two-step joint detection method, which contains local detection and result fusion steps. By incorporating the knowledge about the distribution of the reflection coefficient, we propose a maximum a-posteriori ratio test (MAPRT) detector, which is a generalization of earlier approaches such as the generalized likelihood ratio test detectors. Subsequently, the asymptotic distribution of test statistics of the MAPRT detector is derived. Furthermore, to improve the target detection performance, we propose an optimization algorithm that jointly optimizes the placement and transmit beamformer of the UAV, aiming at minimizing the probability of fusion error while guaranteeing the quality of service requirements of the users. Finally, numerical results demonstrate the effectiveness of the proposed algorithm.

Abstract. We present an analytic approximation model for nonrigid point set registration, grounded in the multivariate Taylor expansion of vector-valued functions. By exploiting the algebraic structure of Taylor expansions, we construct a structured function space spanned by truncated basis terms, allowing smooth deformations to be represented with low complexity and explicit form. To estimate mappings within this space, we develop a quasi-Newton optimization algorithm that progressively lifts the identity map into higher-order analytic forms. This structured framework unifies rigid, affine, and nonlinear deformations under a single closed-form formulation, without relying on kernel functions or high-dimensional parameterizations. The proposed model is embedded into a standard ICP loop—using (by default) nearest-neighbor correspondences—resulting in Analytic-ICP, an efficient registration algorithm with quasi-linear time complexity. Experiments on 2D and 3D datasets demonstrate that Analytic-ICP achieves higher accuracy and faster convergence than classical methods such as CPD and TPS-RPM, particularly for small and smooth deformations.

Multi-hop networks are vital for establishing emergency communications, in case the terrestrial communication infrastructures are compromised. Organizing nodes into specific structures can enhance system resilience and efficiency. To this end, we consider using directional antennas to enhance transmission, and the nodes form a Z-chain structure based on the location information provided by the satellite. The core mechanism for performance gain lies in the spatial separation that shifts the dominant interference from the high-gain antenna main lobes to the attenuated side lobes. An exhaustive search demonstrates the optimality of the Z-chain configuration. To combat performance degradation from antenna pointing errors and node drift, we introduce an alternating Newton method (ANM) that jointly optimizes network structure and beam configurations by minimizing single-hop outage probability. Simulations show that the proposed Z-chain structure surpasses existing linear relay designs by converting most intra-chain interference from main-lobe to side-lobe directions, albeit with additional relay nodes. The Z-chain structure offers a promising paradigm for high-performance wireless networks with potential applications in terrestrial, aerial, and space communications.

The maritime environment is uniquely challenged by sparse scattering, sea wave movement, and ducting effects, which significantly impede communication efficiency. Anticipating channel dynamics proactively presents a solution to these challenges. Therefore, this paper investigates the problem of predicting wireless channel path loss in the complex maritime environment. First, a holistic framework that accounts for the synergistic effects of meteorological and radio frequency factors on maritime channels is introduced. Then, an evolutionary-enhanced ensemble neural networks approach is developed that dynamically tailors neural network-based models for the accurate prediction of the maritime channel’s path loss. Numerical results demonstrate that the proposed method outperforms existing state-of-the-art techniques in both prediction accuracy and reliability.

Federated learning (FL) and split learning (SL) are two effective distributed learning paradigms in wireless networks, enabling collaborative model training across mobile devices without sharing raw data. While FL supports low-latency parallel training, it may converge to less accurate model. In contrast, SL achieves higher accuracy through sequential training but suffers from increased delay. To leverage the advantages of both, hybrid split and federated learning (HSFL) allows some devices to operate in FL mode and others in SL mode. This paper aims to accelerate HSFL by addressing three key questions: 1) How does learning mode selection affect overall learning performance? 2) How does it interact with batch size? 3) How can these hyperparameters be jointly optimized alongside communication and computational resources to reduce overall learning delay? We first analyze convergence, revealing the interplay between learning mode and batch size. Next, we formulate a delay minimization problem and propose a two-stage solution: a block coordinate descent method for a relaxed problem to obtain a locally optimal solution, followed by a rounding algorithm to recover integer batch sizes with near-optimal performance. Experimental results demonstrate that our approach significantly accelerates convergence to the target accuracy compared to existing methods.

Maritime channel modeling is crucial for designing robust nearshore communication systems, yet reliable models that account for the dynamic marine environment with varying sea waves, wind conditions, and vessel motions remain scarce. This article investigates land-to-ship maritime wireless channel characteristics at 5.8 GHz based upon an extensive measurement campaign, with concurrent hydrological and meteorological information collection. First, a novel large-scale path loss model with physical foundation and high accuracy is proposed for dynamic marine environments. Then, we introduce the concept of sea-wave-induced fixed-point (SWIFT) fading, a peculiar phenomenon in maritime scenarios that captures the impact of sea surface fluctuations on received power. An enhanced two-ray model incorporating vessel rotational motion is propounded to simulate the SWIFT fading, showing good alignment with measured data, particularly for modest antenna movements. Next, the small-scale fading is studied by leveraging a variety of models including the two-wave with diffuse power (TWDP) and asymmetric Laplace distributions, with the latter performing well in most cases, while TWDP better captures bimodal fading in rough seas. Furthermore, maritime channel sparsity is examined via the Gini index and RicianKfactor, and temporal dispersion is characterized. The resulting channel models and parameter characteristics offer valuable insights for maritime wireless system design and deployment.

Cooperation between satellite and terrestrial wireless networks promises great potential in meeting fast-growing demands for ubiquitous communications coverage. To tackle spectrum scarcity, spectrum sharing is studied for a hybrid satellite-terrestrial network where satellite links share the same group of time-slotted subcarriers with terrestrial links opportunistically. In particular, with coarse network-wide time synchronization, a time-scale-adaptable spectrum sharing framework is proposed based on a satellite-terrestrial cooperation time scale that can be flexibly adjusted according to practical requirements. For generality, it is assumed that both full and partial frequency reuse could be adopted among the base stations (BSs) and satellite selection is supported when multiple satellites are available. Relying on only statistical channel state information (CSI), joint link scheduling and power control are explored to maximize the average sum rate of the network while ensuring quality of service (QoS) for users. To solve the complicated mixed integer programming (MIP) problem, a low-complexity spectrum sharing scheme is presented based on link-feature-sketching-aided hierarchical link clustering and Monte-Carlo-and-successive-approximation-aided transmit power optimization. Simulation results demonstrate that by link feature sketching, diversity of the links brought by the spatial distribution of the users could be well utilized. The proposed scheme promises a significant performance gain even under strict inter-link interference constraints.

Satellite clustering has the potential to enhance inter-satellite cooperation, resist satellite malfunction, and enable more agile space-air-ground integrated applications. This paper investigates a promising model for clustered low Earth orbit (LEO) systems, in which one typical unmanned aerial vehicle (UAV) can assist one satellite cluster to serve one random terrestrial user. Particularly, intra-cluster satellites can communicate user, while inter-cluster satellites are regarded as interference. Two types of satellites and users are randomly deployed at three visible spherical spaces by adopting three independent spherical Poisson point processes. In the modeling, an adaptive selecting mechanism is proposed to pick the strongest received signal between direct and cooperative transmissions. To facilitate a simpler analysis, we firstly transform the three spaces into the three planes through modifying their respective density. Next, assuming that the shadowed-Rician fading is employed in the satellite channel, two Gamma random variables are utilized to approximately express the aggregated power of interference and noise received by the UAV and user, respectively. Subsequently, the exact conditional user association and approximate Laplace transform of the accumulated signal power are derived to further investigate the conditional coverage probability. Finally, simulation results illustrate that: 1) Moderate satellite cluster sizes combined with a UAV altitude of about 200m are beneficial for achieving higher coverage probability; and 2) The adaptive selection mechanism generally achieves comparable or better performance than traditional transmissions by leveraging spatial diversity.

The existing unmanned aerial vehicle (UAV) enabled integrated sensing and communications (ISAC) systems primarily focus on the UAV’s own sensing capabilities, overlooking the potential of existing ground access points (APs) in performing passive sensing via the reflected signals—thus limiting the overall performance. To address this issue, this paper introduces a UAV empowered air-ground ISAC system, where a UAV cooperates with several ground APs in detecting a potential target, with the UAV and APs working in active and passive modes, respectively. Different from the conventional generalized likelihood ratio test detectors, we exploit the reflection coefficient’s distribution to design a maximum a-posteriori ratio test (MAPRT) detector and derive its asymptotic test statistic distribution. To break through the capacity limitations of the wireless backhaul links, we introduce a two-step joint detection method involving local detection and result fusion. Further, we propose an optimization algorithm to jointly optimize the UAV’s placement and transmit beamforming, aiming at enhancing the target detection performance. Numerical results demonstrate the effectiveness of the proposed algorithm.

The software-defined wide area network (SD-WAN) has emerged as a promising architecture for the next-generation WANs, where the Internet service provider (ISP) leases network resources from the infrastructure provider (InP) and offers various applications through service function chaining. Taking the perspective of an ISP, this study focuses on two closely coupled issues. 1) Resource Allocation (RA): In bandwidth-constrained SD-WANs, network resources need to be flexibly allocated according to link congestion status. 2) Resource Provisioning (RP): Due to service dynamics, ISPs need to adjust their resource leasing schemes in response to request variations. The joint consideration of RA and RP enables bidirectional adaptation between the network and services. In this work, an ILP problem is formulated to unify RA and RP within one framework. For RA, a congestion-aware heuristic algorithm is proposed to achieve load balancing through probabilistic dispersion of data flows across different requests. For RP, a Bayesian Optimization-based algorithm is designed to facilitate resource capacity planning for ISPs. Simulations demonstrate that the proposed methods can reduce request rejection by 50% in bandwidth-constrained networks, improve resource utilization efficiency, enhance the ISP’s benefit, and mitigate performance fluctuations caused by service dynamics.

The growth of maritime activities has boosted the demand for efficient marine communications and computation offloading. Mobile edge computing (MEC)-powered space-air–ground-sea integrated network (SAGSIN) is a promising solution to satisfy these demands. To the best of our knowledge, the existing research on high altitude platforms (HAPs) in SAGSIN remains open. To exploit the HAPs’ advantages of facilitating communication at shorter distances and more stable computing services than terrestrial base stations, this work considers multi-HAP-assisted computation offloading in SAGSIN and formulates an optimization problem, which turns out to be a mixed integer nonlinear programming (MINLP) due to the joint optimization among task-HAP association, computational resource allocation of HAPs and task-satellite association. To this end, the problem is decoupled into three single-variable subproblems by relaxing the delay constraint, and the subproblems are solved by graph theory, the Lagrange multiplier method with Karush-Kuhn–Tucker (KKT) constraints, and the alternating optimization, respectively. Simulation results demonstrate the efficiency of the proposed scheme with superior performances compared to benchmark schemes.

In recent decades, cyber-physical systems (CPSs) have received great attention due to their broad applications. This paper investigates CPS deployment in remote areas, specifically focusing on a digital twin-empowered unmanned robotic system. The system consists of a multifunctional unmanned aerial vehicle (UAV), sensors, and actuators. The UAV carries communication and computing modules, acting as an edge information hub (EIH) that connects sensors and actuators—forming reflex-arc-like sensing-communication-computing-control (SC3) loops. A digital twin is integrated into the EIH to emulate the system’s behavior and assist in the decision-making. To alleviate resource limitations in remote areas, we propose a goal-oriented closed-loop optimization scheme. The proposed scheme takes the SC3loop as an integrated structure and jointly optimizes uplink and downlink (UL&DL) communication and computing resources to minimize the total linear quadratic regulator (LQR) cost. To address the non-convex optimization problem, we derive the closed-form solution for intra-loop allocation and propose an efficient iterative algorithm for inter-loop optimization. Under the condition of adequate CPU frequency, we derive an approximate closed-form solution for inter-loop bandwidth allocation. Simulation results demonstrate the superiority of the proposed scheme, which achieves a two-tier task-level balance within and across the SC3loops.

An increasing number of field robots would be used for mission-critical tasks in remote or post-disaster areas. Due to the limited individual abilities, these robots usually require an edge information hub (EIH), with not only communication but also sensing and computing functions. Such EIH could be deployed on a flexibly-dispatched unmanned aerial vehicle (UAV). Different from traditional aerial base stations or mobile edge computing (MEC), the EIH would direct the operations of robots via sensing-communication-computing-control ($\textbf {SC}^{3}$) closed-loop orchestration. This paper aims to optimize the closed-loop control performance of multiple$\textbf {SC}^{3}$loops, with constraints on satellite-backhaul rate, computing capability, and on-board energy. Specifically, the linear quadratic regulator (LQR) control cost is used to measure the closed-loop utility, and a sum LQR cost minimization problem is formulated to jointly optimize the splitting of sensor data and allocation of communication and computing resources. We first derive the optimal splitting ratio of sensor data, and then recast the problem to a more tractable form. An iterative algorithm is finally proposed to provide a sub-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm. We also uncover the influence of$\textbf {SC}^{3}$parameters on closed-loop controls, highlighting more systematic understanding.

Spectrum sharing among the satellite, unmanned aerial vehicle (UAV), and terrestrial components is crucial to alleviate spectrum scarcity in a hybrid maritime communication network (MCN). In the time domain, spectrum sharing optimization based on fine-grained time slices is widely envisioned. However, fine-grained time synchronization is rather challenging due to the large diversity in the link delay. In this paper, we focus on link-cluster-based spectrum sharing based on coordinated link scheduling in terms of subcarrier and time slice allocation. By link-cluster-based scheduling for the satellite links, time-slice-oriented spectrum sharing is realized with coarse time synchronization at time scales much larger than single time slice duration. Only large-scale channel state information (CSI) is utilized for saving cost. An NP-hard mixed integer programming (MIP) problem is formulated, and with the aid of link clustering, a suboptimal spectrum sharing scheme, with only a small performance gap to the optimal one, is proposed. Simulations show that a significant improvement in both energy efficiency and spectrum efficiency could be achieved by the proposed scheme.

In emergency rescue scenarios, field robots can be dispatched to enhance rescue operations, and unmanned aerial vehicles (UAVs) can be utilized to serve field robots thanks to their flexibility and on-demand deployment. To support the robots efficiently, UAVs should be equipped with sensors, base stations (BSs), and mobile edge computing (MEC) servers. The whole process of a typical rescue task can be regarded as a sensing-communication-computing-control (SC3) closed loop. In this paper, we focus on the closed-loop performance of SC3loops, which is essential for mission-critical tasks. Specifically, we propose a joint communication and computing resource allocation problem, aiming to minimize the sum linear quadratic regulator (LQR) cost of SC3loops. We prove the convexity of the optimization problem by introducing auxiliary variables. Numerical results are provided to show that our proposed scheme can enhance the system’s control performance. Our work also shows that it is essential to jointly consider the communication, computing, and sensing capabilities in unmanned rescue tasks.

Ambient backscatter communication enables low-cost low-rate wireless interconnections for Internet of Things (IoT) applications. In this work, new signal detectors for different cases of ambient backscatter communications are derived. Specifically, both coherent and partially coherent detectors are obtained for Gaussian ambient signals and phase shift keying (PSK) ambient signals. Maximum likelihood detection method and improved energy detection method (including energy detection and magnitude detection as special cases) are adopted. Numerical results show that the energy detection method has the best performance when the ambient signals are Gaussian, while the magnitude detection method has the best performance when the ambient signals are PSK modulated. Both are comparable to the optimum maximum likelihood detection. Numerical results also show that the improved energy detection method is very flexible and that detectors for PSK ambient signals are slightly better than those for Gaussian ambient signals.

In unmanned space exploration, the cooperation among space robots requires advanced communication techniques. In this paper, we propose a communication optimization scheme for a specific cooperation system named the “mother-daughter system”. In this setup, the mother spacecraft orbits the planet, while daughter probes are distributed across the planetary surface. During each control cycle, the mother spacecraft senses the environment, computes control commands and distributes them to daughter probes for actions. They synergistically form sensing-communication-computing-control ($\mathbf {SC^{3}}$) loops. Given the indivisibility of the$\mathbf {SC^{3}}$loop, we optimize the mother-daughter downlink for closed-loop control. The optimization objective is the linear quadratic regulator (LQR) cost, and the optimization parameters are the block length and transmit power. To solve the nonlinear mixed-integer problem, we first identify the optimal block length and then transform the power allocation problem into a tractable convex problem. We further derive the approximate closed-form solutions for the proposed scheme and two communication-oriented schemes: the max-sum rate scheme and the max-min rate scheme. On this basis, we analyze their power allocation principles. In particular, for time-insensitive control tasks, we find that the proposed scheme demonstrates equivalence to the max-min rate scheme. These findings are verified through simulations.

Spectrum sharing among the satellite, unmanned aerial vehicle (UAV), and terrestrial components is crucial to alleviate spectrum scarcity in a hybrid maritime communication network (MCN). Fine-grained spectrum sharing based on ms-level time-domain slices is widely envisioned. However, ms-level time synchronization is challenging in the hybrid MCN due to the large diversity in the link delay. To tackle this challenge, we propose a fine-over-coarse spectrum sharing framework based on coordinated link scheduling, which is realized by joint subcarrier and time slice allocation. Specially, by link-cluster-based scheduling with grouped time slice allocation for the satellite links, time-slice-oriented spectrum sharing is realized with coarse time synchronization at time scales much larger than a single time slice duration. In the framework, only large-scale channel state information (CSI) is utilized for saving cost. A worst-case model is introduced to depict interference caused by satellite link clusters, and an NP-hard mixed integer programming (MIP) problem is formulated. Based on analysis on the characteristics of the optimal solution, a novel link clustering algorithm is proposed to form a group of shaped virtual cells within the coverage area of the MCN. A suboptimal spectrum sharing scheme with only a small performance gap to the optimal one is then proposed. Simulations show that a significant improvement in both energy efficiency and spectrum efficiency can be achieved by the proposed framework.

This paper investigates an emergency rescue system, which comprises a multi-functional unmanned aerial vehicle (UAV) and multiple robots. The UAV carries sensing, communication, and computing modules. It senses system states, calculates commands, and instructs field robots to take actions. In this way, the UAV and robots form multiple sensing-communication-computing-control $(\mathbf{SC} ^{3})$ loops, which could finish many mission-critical tasks without human participation. To activate these $\mathbf{SC} ^{3}$ loops, we propose a closed-loop optimization scheme. Unlike traditional studies that primarily focus on the communication link, the proposed scheme emphasizes the $\mathbf{SC} ^{3}$ loop and adopts the linear quadratic regulator (LQR) cost as the objective. Focusing on the UAV-robot downlink, we model the data transmission in the finite block length regime and take the transmit power and block lengths as optimization variables. We solve the nonlinear integer problem by exploiting the monotonicity and convexity of the objective rate-cost function. The closed-form solution of the transmit power is derived in the assure-to-be-stable region. On this basis, we compare the proposed scheme with the max-sum rate scheme. Through comparisons, the fairness-minded nature of the proposed scheme is revealed.

In this paper, we address the service function chaining (SFC) problem for software-defined wide area networks (SD-WANs). Due to its NP-hardness, the service chaining problem is simplified by most existing works to apply to practical-size networks. However, in SD-WAN scenarios, these simplifications may lead to link congestion due to the heterogeneity of geographically distributed networks. Different from previous studies, we propose a congestion-aware algorithm, where more comprehensive and practical constraints are considered, and load balancing is introduced to reduce link congestion. First we formulate an integer linear programming model for exact solution, then the original problem is simplified and heuristic algorithms are designed to deploy the SFC requests in batches. Specifically, load balancing is implemented by updating the routing rules according to network congestion status, and data flows from different requests are dispersed in a probabilistic manner, which to our knowledge is first adopted for load balancing in SFC problems. Simulation results show that our algorithm can significantly reduce link congestion, thus improving the acceptance rate of SFC requests and obtaining higher benefits.

Network-enabled robots have become important to support future machine-assisted and unmanned applications. To provide high-quality services for wide-area robots, hybrid satellite-terrestrial networks are a key technology. Via hybrid networks, computation-intensive and latency-sensitive tasks of robots can be offloaded to mobile edge computing (MEC) servers. However, due to the mobility of mobile robots and unreliable wireless network environments, excessive local computations and frequent service migrations may significantly increase the service delay. To address this issue, this paper aims to minimize the average task completion time for MEC-based offloading for satellite-terrestrial-network-enabled robots. Different from conventional mobility-aware schemes, the proposed scheme is to make the offloading decision by jointly considering the mobility control of robots. A joint optimization problem of task offloading and velocity control is formulated. Using Lyapunov optimization, the original optimization is decomposed into a velocity control subproblem and a task offloading subproblem. Then, based on the Markov decision process (MDP), a dual-agent reinforcement learning (RL) algorithm is proposed. Simulation results show that the proposed scheme can effectively reduce the service delay.

While 5G networks have accelerated the development of the Internet of Things (IoT), they have also introduced a large number of vulnerable IoT devices into the network, which would lead to severe Distributed Denial-of-Service (DDoS) attacks. The newly emerging DDoS attack methods generally have a shorter duration, which imposes higher requirements for the response time of DDoS mitigation technologies. Existing DDoS defense methods cannot achieve real-time detection due to the difficulty of reducing the delay of feature extraction and large-scale data processing. In this article, we focus on the timeliness of DDoS detection and mitigation. We hope that deploying effective defense countermeasures at the source side will block the majority of DDoS attack traffic in real time before it enters the data network (DN). To this end, we propose a real-time DDoS defense framework based on multidomain collaboration that combines multisource information to detect attack sessions with high accuracy in 5G networks. To operate the framework at line rate, we propose an optimal packet sampling strategy based on the accurate session size estimation, which can greatly reduce the detection overhead while ensuring good accuracy. In a typical scenario with an attack session size larger than 10, this method can achieve a 99% detection rate while reducing the packet inspection rate (PIR) to less than 37%.

The sixth-generation (6G) network is envisioned to integrate communication and sensing functions, so as to improve the spectrum efficiency and support explosive novel applications. Although the similarities of wireless communication and radio sensing lay the foundation for their combination, there is still considerable incompatible interest between them. To simultaneously guarantee the communication capacity and the sensing accuracy, the multiple-input and multiple-output (MIMO) technique plays an important role due to its unique capability of spatial beamforming and waveform shaping. However, the configuration of MIMO also brings high hardware cost, high power consumption, and high signal processing complexity. How to efficiently apply MIMO to achieve balanced communication and sensing performance is still open. In this survey, we discuss joint communication and sensing (JCAS) in the context of MIMO. We first outline the roles of MIMO in the process of wireless communication and radar sensing. Then, we present current advances in both communication and sensing coexistence and integration in detail. Three novel JCAS MIMO models are subsequently discussed by combining cutting-edge technologies, i.e., cloud radio access networks (C-RANs), unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfaces (RISs). Examined from the practical perspective, the potential and challenges of MIMO in JCAS are summarized, and promising solutions are provided. Motivated by the great potential of the Internet of Things (IoT), we also specify JCAS in IoT scenarios and discuss the uniqueness of applying JCAS to IoT. In the end, open issues are outlined to envisage a ubiquitous, intelligent, and secure JCAS network in the near future.

Due to the heterogeneity of Internet of Things (IoT) devices and the diversity of IoT communication protocols, it is challenging to model the communication behaviors of IoT devices to facilitate attack defense. Considering the complex correlation between the IoT device types and the patterns of their communication behaviors, one possible solution is to cluster IoT devices into different types based on the characteristics of their communication behaviors and deal with each type, respectively. However, IoT traffic includes a significant proportion of abnormal traffic, such as attack traffic sourcing from compromised devices, which cannot reflect the behavioral characteristics of the source device. In this article, we propose a Transformer-based IoT device-type identification method to address the above challenges. Specifically, our approach consists of three main components. First, we classify the traffic data from IoT devices into normal and abnormal types by a Transformer-based traffic diagnosis model. Next, another Transformer-based model is adopted on the normal traffic to identify the IoT device type. Finally, considering the immutability of IoT device types, a results-ensemble algorithm is designed to improve the accuracy of IoT device-type identification. Experimental results verify the effectiveness of our method, which brings a noticeable improvement in terms of both accuracy and macro$F1$-score compared to other methods. Moreover, by applying the results-ensemble algorithm in the test phase, we can achieve 100% accuracy under certain conditions.

We investigate the performance of massive ultra-reliable and low-latency communications (mURLLC) under massive active users, and non-uniform small-scale and shadow fading in the uplink (UL) of a next-generation multiple access (NGMA) system that integrates massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) techniques. We first derive new closed-form expressions to accurately approximate the probability density function (PDF) and cumulative distribution function (CDF) of the channel gains in MIMO systems under the$\kappa $-$\mu $shadowed fading. Then, we derive the post-processing signal-to-noise ratio (SNR) and its closed-form PDFs and CDFs in the NGMA system, under both perfect and imperfect channel state information of the$\kappa $-$\mu $shadowed fading channel. Given the post-processing SNRs and their PDFs, the general expressions are established for the error probability (EP) to analyze the mURLLC of NGMA by applying finite blocklength information theory. Corroborated by extensive simulations, our analysis reveals that with the increasing reliability requirements of the users, the relative gaps in EPs enlarge between users experiencing different fading channels, and the feasible system configurations (i.e., the transmit powers of the users, and the numbers of antennas, active users, and subcarriers) also increasingly differ between the users. The impact of different fading on mURLLC implementations cannot be overlooked, and the research of mURLLC under the$\kappa $-$\mu $shadowed fading model is indispensable. The NGMA system considered in this paper is capable of achieving mURLLC under non-uniform small-scale and shadow fading.

Current fifth-generation (5G) networks do not cover maritime areas, causing difficulties in developing maritime Internet of Things (IoT). To tackle this problem, we establish a nearshore network by collaboratively using on-shore terrestrial base stations (TBSs) and tethered unmanned aerial vehicles (UAVs). These TBSs and UAVs form virtual clusters in a user-centric manner. Within each virtual cluster, non-orthogonal multiple access (NOMA) is adopted for agilely including various maritime IoT devices, which are sparsely distributed over the vast ocean. The nearshore network also shares the spectrum with marine satellites. In such a NOMA-based hybrid satellite-UAV-terrestrial network, interference among different network segments, different clusters, and different users occurs. We thereby formulate a joint power allocation problem to maximize the sum rate of the network. Different from existing studies, we use large-scale channel state information (CSI) only for optimization to reduce system overhead. The large-scale CSI is obtained by using the position information of maritime IoT devices. The problem is non-convex with intractable non-linear constraints. We tackle these difficulties by adopting max-min optimization, the auxiliary function method, and the successive convex approximation technique. An iterative power allocation algorithm is accordingly proposed, which is shown to be effective for coverage enhancement by simulations. This shows the potential of NOMA-based hybrid satellite-UAV-terrestrial networks for maritime on-demand coverage.

Benefiting from the fusion of communication and intelligent technologies, network-enabled robots have become important to support future machine-assisted and unmanned applications. To provide high-quality services for robots in wide areas, hybrid satellite-terrestrial networks are a key technology. Through hybrid networks, computation-intensive and latency-sensitive tasks can be offloaded to mobile edge computing (MEC) servers. However, due to the mobility of mobile robots and unreliable wireless network environments, excessive local computations and frequent service migrations may significantly increase the service delay. To address this issue, this paper aims to minimize the average task completion time for MEC-based offloading initiated by satellite-terrestrial-network-enabled robots. Different from conventional mobility-aware schemes, the proposed scheme makes the offloading decision by jointly considering the mobility control of robots. A joint optimization problem of task offloading and velocity control is formulated. Using Lyapunov optimization, the original optimization is decomposed into a velocity control subproblem and a task offloading subproblem. Then, based on the Markov decision process (MDP), a dual-agent reinforcement learning (RL) algorithm is proposed. The convergence and complexity of the improved RL algorithm are theoretically analyzed, and the simulation results show that the proposed scheme can effectively reduce the offloading delay.

Due to the heterogeneity of Internet of Things (IoT) devices and the diversity of IoT communication protocols, it is challenging to defend against malicious traffic from IoT devices. In this paper, a novel malicious traffic detection method is proposed based on the deep learning method. Specifically, a Transformer-based encoder is designed to automatically select key features of IoT traffic for the detection task, which avoids the cumbersome feature screening process that has been widely used in traditional machine learning methods. To address the complexity of the feature space and improve the efficiency of model training, we exploit the correlation between the characteristics of malicious traffic and the device type of IoT bots to further improve the detection accuracy by introducing a device classification auxiliary loss in the training phase. Experimental results show that our method outperforms the state-of-the-art machine learning-based methods in terms of accuracy, precision, recall and f1-score on real IoT traffic traces. In addition, the benefit of device type information on detection efficiency is verified.

Machine-type communication (MTC) networks face the challenges of massive access and diverse quality of service (QoS) requirements. In this paper, we focus on satisfying various QoS requirements for massive number of MTC devices. By dividing these devices into multiple clusters based on their QoS characteristics, we formulate an access control problem to maximize the access efficiency while satisfying both the access and transmission delay requirements. An efficient algorithm is proposed to solve the problem by adaptively adjusting the access time intervals and back-off factors of the clusters. Simulation results show that the proposed scheme outperforms other schemes in terms of access efficiency. The impacts of various parameters including delay and traffic rate on the performance are disclosed.

The link flooding attack (LFA) has emerged as a new category of distributed denial of service (DDoS) attacks in recent years. Along with the massive deployment of low-cost insecure Internet-of-Things (IoT) devices, the fast proliferation of IoT botnets dramatically increases the risk of LFAs. However, how to efficiently defend against LFAs in IoT still remains as an open problem. To overcome this challenge, we model the interaction between an LFA attacker and the network manager as a two-person Bayesian game in this article to precisely characterize the behaviors of both sides. Then, the rational behaviors of the attacker and the optimal strategies of the defender are unveiled by deriving the Bayesian Nash equilibrium (BNE). Inspired by the obtained BNEs, a cost-effective decision framework is proposed for the defender to make defense decisions. Furthermore, we numerically analyze the effect of all the related factors and present feasible suggestions to deter attack motivations fundamentally. Experimental results demonstrate that the proposed method not only consistently outperforms baseline methods in terms of the defender’s utilities under different attack intensities, but also is robust to the changes in important parameters, including the value of benign traffic and the latency of traffic scrubbing.

The proliferation of Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) not only threatens the security of digital devices and infrastructure but also severely degrades IoT system performance due to the overly consumed network resources. With the knowledge of identity information of devices and signaling data, Internet service providers (ISPs) can detect and block DDoS traffic by monitoring the upstream IoT packets, and thereby, improve network efficiency. However, inspecting all data packets online for DDoS detection will significantly increase both the network delay and the computational overhead. Therefore, the packet sampling strategy is crucial for the defenders to detect DDoS attacks. To this end, this article formulates a Stackelberg game model to analyze the collaborative IoT packet sampling against DDoS attacks. Through the equilibrium analysis of the DDoS game, we derive the lower bound of packet sampling rate (PSR) that can effectively deter potential attackers. Unlike traditional offline detection, our proposed packet sampling strategy can support both the online detection and proactive prevention of DDoS traffic. As a use case, a multipoint DDoS defense framework is developed to address the IP spoofing in 5G networks based on the proposed packet sampling strategy, which deters DDoS attacks and reduces the packet sampling cost, and thereby, maximizes the IoT utility, compared with existing methods. In typical reflection attacks (in which no more than five packets of response are triggered by a request packet), our proposed scheme not only reduces more than 70% of the sampling rate but also demonstrates superior robustness against boundary condition variation.

Radio sensing has become increasingly important, as the demand for “smartness” is drastically increasing. Unlike conventional sensing, radio sensing uses existing radio signals or devices to passively sense the ambient environment for low cost and wide deployment. In this article, a comprehensive overview of radio sensing using the recent fifth-generation (5G) signals is provided. 5G systems have many merits, such as high frequency, large bandwidth, massive antenna array, and dense network, making them ideal for radio sensing. In the overview, basic theories and concepts of 5G radio sensing are first introduced. Then, different state-of-the-art 5G sensing works are discussed based on their applications. These applications show that 5G radio sensing represents a step change in radio sensing. After that, several open challenges in 5G radio sensing are illustrated with relevant insights. These insights manifest that 5G radio sensing has great potentials to explore.

Massive connections and diverse Quality of Service (QoS) requirements pose a major challenge for machine-type communication (MTC) networks. In this article, to satisfy the various QoS requirements of a massive number of MTC devices (MTCDs), the devices are divided into multiple clusters based on the QoS characteristics. The cluster access control and intracluster resource allocation problems are studied to satisfy the double delay requirements in the access and data transmission phases in a cross-layer approach. Specifically, we formulate an access control problem to maximize the access efficiency with constraints on access and transmission delays. An efficient algorithm is proposed to adaptively adjust the access time intervals and backoff factors of the clusters for different numbers of active MTCDs and transmission rates. Given the access parameters, nonorthogonal multiple access is adopted in resource allocation to maximize the system utility function while guaranteeing the delay requirements for each accessed MTCD. An efficient sequential convex programming iterative algorithm is proposed to solve the NP-hard nonconvex problem with two typical utility objectives: 1) total throughput and 2) consumed power. Simulation results show that the proposed scheme can achieve better performance in terms of access efficiency, delay, throughput, and consumed power than other schemes. The impacts of various parameters, including delay and traffic rate, on the performance, are disclosed.

Orthogonal time frequency space (OTFS) system constitutes an effective structure conceived for efficiently utilizing the channel information, which is capable of achieving a promising transmission performance in high-mobility environment. To extract enough channel diversity, a two-dimensional Fourier transformation combined with a pulse shape is designed at the OTFS transmitter. Consequently, the amplitude of OTFS signals may fluctuate drastically, owing to the combined dependency of the OTFS transformation and the pulse shape. To quantify the amplitude fluctuation, we investigate the peak-to-average power ratio (PAPR) of OTFS signals, for a large amount of data in the delay-Doppler domain. We first reveal that when the number of data points approaches to infinity, based on central limit theorems for dependent variables, the complex-valued OTFS signals weakly converge to a Gaussian distribution. Then, according to the extremal theory of the Chi-squared process for stationary OTFS signals, an accurate expression of the PAPR distribution is derived, depending on the transmit pulse and the number of data points. It is also demonstrated that upon modifying the exponential factor, the analytical PAPR expression is applicable for the non-stationary Gaussian distribution caused by the bandlimited pulse with a large roll-off factor. Simulation results confirm the accuracy of the analytical PAPR probability for practical conditions.

Outside the coverage of terrestrial cellular networks, non-terrestrial infrastructures, e.g., satellites and unmanned aerial vehicles (UAVs), should be utilized, to efficiently cover the remote areas. This requires a cognitive satellite-UAV network, where satellites and UAVs share the spectrum to save cost, and the network resources are orchestrated in an on-demand manner. In this paper, we focus on the physical layer security issue of the cognitive satellite-UAV networks, which is important due to the openness of both satellite links and UAV links. We formulate a joint power and channel allocation problem, using only the slowly-varying large-scale channel state information (CSI), to maximize the sum secrecy rate of UAV users. By resorting to the random matrix theory, the max-min optimization tool, as well as the bipartite graph matching algorithm, we propose a sub-optimal low-complexity solution, the superiority of which is verified by simulation results.

In the blind areas of current fifth generation (5G) networks, e.g. the remote areas, unmanned aerial vehicles (UAVs) can be used to provide on-demand connectivity. To efficiently serve the sparsely distributed users in these areas, non-orthogonal multiple access (NOMA) could be adopted to exploit the user distinguish ability in the power domain. In this paper, we consider a NOMA-based multi-UAV-aided network, where a swarm of coordinated UAVs transmit messages to unevenly distributed users through a virtual multiple-input-multiple-output (MIMO) channel. We formulate a power allocation problem to maximize the minimum user rate to assure fairness in the transmission. Different from existing studies, we use only the large-scale channel state information (CSI) in the transmission design, which characterizes the basic channel feature, and can be obtained using the location information of UAVs/users. By leveraging the random matrix theory and successive convex optimization tools, we propose an iterative algorithm to solve the problem after a series of problem transformation. Simulation results show that the proposed power allocation scheme outperforms existing methods, which shows the potential of multi-UAV-aided NOMA communications for coverage enhancement in remote areas.

In the coming smart ocean era, reliable and efficient communications are crucial for promoting a variety of maritime activities. While on-shore base stations (BSs) constitute a key infrastructure for maritime communications, the trap of low energy efficiency caused by long transmission distances must be delicately circumvented. In this paper, we try to utilize internet of vessels (IoV) to tackle the problem. Specifically, we investigate the joint link scheduling and rate adaptation problem for a maritime communication network with both shore-to-vessel and vessel-to-vessel links, with the target of minimizing the energy consumption while assuring a quality of service (QoS) guarantee for each vessel. With only large-scale channel state information available, the problem is shown to be an NP-hard mixed integer non-linear programming problem with a group of hidden nonlinear equality constraints. A process-oriented iterative scheme is proposed based on a relaxation and gradually-approaching method following the gentlest-ascent principle, as well as the divide-and-conquer strategy. Simulation results demonstrate that the proposed scheme can achieve a prominent gain in terms of network energy consumption reduction with a rather low complexity.

Time-sensitive services (TSSs) have been widely envisioned for future sixth-generation (6G) wireless communication networks. Due to its inherent low-latency advantage, mobile-edge computing (MEC) will be an indispensable enabler for TSSs. The random characteristics of the delay experienced by users are key metrics reflecting the Quality of Service (QoS) of TSSs. Most existing studies on MEC have focused on the average delay. Only a few research efforts have been devoted to other random delay characteristics, such as the delay-bound violation probability and the probability distribution of the delay, by decoupling the transmission and computation processes of MEC. However, if these two processes could not be decoupled, the coupling will bring new challenges to analyze the random delay characteristics. In this article, a MEC system with a limited computation buffer at the edge server is considered. In this system, the transmission process and the computation process form a feedback loop and could not be decoupled. We formulate a discrete-time two-stage tandem queueing system. Then, by using the matrix-geometric method, we obtain the estimation methods for the random delay characteristics, including the probability distribution of the delay, the delay-bound violation probability, the average delay, and the delay standard deviation. The estimation methods are verified by simulations. The random delay characteristics are analyzed by numerical experiments, which unveil the coupling relationship between the transmission process and computation process for MEC. These results will largely facilitate the elaborate allocation of communication and computation resources to improve the QoS of TSSs.

Terrestrial communication networks mainly focus on users in urban areas but have poor coverage performance in harsh environments, such as mountains, deserts, and oceans. Satellites can be exploited to extend the coverage of terrestrial fifth-generation networks. However, satellites are restricted by their high latency and relatively low data rate. Consequently, the integration of terrestrial and satellite components has been widely studied to take advantage of both sides and enable the seamless broadband coverage. Due to the significant differences between satellite communications (SatComs) and terrestrial communications (TerComs) in terms of channel fading, transmission delay, mobility, and coverage performance, the establishment of an efficient hybrid satellite-terrestrial network (HSTN) still faces many challenges. In general, it is difficult to decompose an HSTN into a sum of separate satellite and terrestrial links due to the complicated coupling relationships therein. To uncover the complete picture of HSTNs, we regard the HSTN as a combination of basic cooperative models that contain the main traits of satellite-terrestrial integration but are much simpler and thus more tractable than the large-scale heterogeneous HSTNs. In particular, we present three basic cooperative models, i.e., model X, model L, and model V, and provide a survey of the state-of-the-art technologies for each of them. We discuss future research directions toward establishing a cell-free, hierarchical, decoupled HSTN. We also outline open issues to envision an agile, smart, and secure HSTN for the sixth-generation ubiquitous Internet of Things.

With the rapid development of marine activities, there has been an increasing number of Internet-of-Things (IoT) devices on the ocean. This leads to a growing demand for high-speed and ultrareliable maritime communications. It has been reported that a large performance loss is often inevitable if the existing fourth-generation (4G), fifth-generation (5G), or satellite communication technologies are used directly on the ocean. Hence, conventional theories and methods need to be tailored to this maritime scenario to match its unique characteristics, such as dynamic electromagnetic propagation environments, geometrically limited available base station (BS) sites and rigorous service demands from mission-critical applications. Toward this end, we provide a survey on the demand for maritime communications enabled by state-of-the-art hybrid satellite-terrestrial maritime communication networks (MCNs). We categorize the enabling technologies into three types based on their aims: 1) enhancing transmission efficiency; 2) extending network coverage; and 3) provisioning maritime-specific services. Future developments and open issues are also discussed. Based on this discussion, we envision the use of external auxiliary information, such as sea state and atmosphere conditions, to build up an environment-aware, service-driven, and integrated satellite-air-ground MCN.

In fifth generation (5G) and beyond Internet of Things (IoT), it becomes increasingly important to serve a massive number of IoT devices outside the coverage of terrestrial cellular networks. Due to their own limitations, unmanned aerial vehicles (UAVs) and satellites need to coordinate with each other in the coverage holes of 5G, leading to a cognitive satellite-UAV network (CSUN). In this paper, we investigate multi-domain resource allocation for CSUNs consisting of a satellite and a swarm of UAVs, so as to improve the efficiency of massive access in wide areas. Particularly, the cell-free on-demand coverage is established to overcome the cost-ineffectiveness of conventional cellular architecture. Opportunistic spectrum sharing is also implemented to cope with the spectrum scarcity problem. To this end, a process-oriented optimization framework is proposed for jointly allocating subchannels, transmit power and hovering times, which considers the whole flight process of UAVs and uses only the slowly-varying large-scale channel state information (CSI). Under the on-board energy constraints of UAVs and interference temperature constraints from UAV swarm to satellite users, we present iterative multi-domain resource allocation algorithms to improve network efficiency with guaranteed user fairness. Simulation results demonstrate the superiority of the proposed algorithms. Moreover, the adaptive cell-free coverage pattern is observed, which implies a promising way to efficiently serve wide-area IoT devices in the upcoming sixth generation (6G) era.

In the coming smart ocean era, reliable and efficient communications are crucial for promoting a variety of maritime activities. Current maritime communication networks (MCNs) mainly rely on marine satellites and on-shore base stations (BSs). The former generally provides limited transmission rate, while the latter lacks wide-area coverage capability. Due to these facts, the state-of-the-art MCN falls far behind terrestrial fifth-generation (5G) networks. To fill up the gap in the coming sixth-generation (6G) era, we explore the benefit of deployable BSs for maritime coverage enhancement. Both unmanned aerial vehicles (UAVs) and mobile vessels are used to configure deployable BSs. This leads to a hierarchical satellite-UAV-terrestrial network on the ocean. We address the joint link scheduling and rate adaptation problem for this hybrid network, to minimize the total energy consumption with quality of service (QoS) guarantees. Different from previous studies, we use only the large-scale channel state information (CSI), which is location-dependent and thus can be predicted through the position information of each UAV/vessel based on its specific trajectory/shipping lane. The problem is shown to be an NP-hard mixed integer nonlinear programming problem with a group of hidden non-linear equality constraints. We solve it suboptimally by using Min-Max transformation and iterative problem relaxation, leading to a process-oriented joint link scheduling and rate adaptation scheme. As observed by simulations, the scheme can provide agile on-demand coverage for all users with much reduced system overhead and a polynomial computation complexity. Moreover, it can achieve a prominent performance close to the optimal solution.

This paper investigates a dual-unmanned aerial vehicle (UAV)-enabled secure communication system, in which, a UAV moves around to send confidential messages to a mobile user while another cooperative UAV transmits artificial noise signals to confuse malicious eavesdroppers. Both UAVs have energy constraints and the location information of eavesdroppers is imperfect. We consider a worst-case secrecy rate maximization problem of the mobile user over all time slots. This optimization problem is solved by jointly designing the three-dimensional (3D) trajectory of UAVs and the time allocation (recharging and service or jamming) under practical constraints including maximum UAV speed, UAV collision avoidance, UAV positioning error, and UAV energy harvesting. Specifically, we adopt a more practical UAV-ground channel model with both large-scale and small-scale fading components. Due to the non-convex feasible region constructed by the complicated constraints, directly finding the optimal solution of the original problem is intractable. To address this issue, we decouple the original optimization problem into three subproblems and develop an iterative algorithm to find its suboptimal solution by using the block coordinate descent technique. To solve each subproblem, certain advanced optimization tools, such as integer relaxation, S-procedure, and successive convex approximation techniques, are utilized. Numerical simulation results are provided to corroborate the theoretical derivations and to evaluate the performance of the proposed algorithm. Additionally, the numerical results assist to draw new insights on the 3D UAV trajectory by comparing the performance with conventional two-dimensional (2D) schemes.

Distributed Reflection Denial of Service (DRDoS) attack has become one of the most serious threats to Internet security. With the ongoing development of 5G, a massive number of insecure Internet of Things (IoT) devices are connected to the Internet, which brings great challenges to defend against DRDoS attacks. To overcome these challenges, we extend the User Plane Function (UPF) of 5G core network, and propose a new framework accordingly for source IP address validation, so as to suppress the source IP address spoofing behaviors of DRDoS attackers. Under this framework, the packet inspection rate (PIR), i.e., the inspection probability of each packet, is crucial to simplify the validation complexity. To unveil the optimal PIR, we establish a two-player game which models the IP address spoofing and detection behaviors. Analysis on the formulated game implies a lower bound of sufficient PIR, which may be used to set PIR in practice. Simulation results show that the proposed method can efficiently deter IP spoofing behaviors. Thereby the derived PIR could achieve low-cost and effective defense of DRDoS.

In extreme communication environments, a highly dynamic channel (HDC) often arises with quite challenging fast time-varying and nonstationary characteristics. Different from existing studies, in this work, we investigate the most intractable HDC case when the coherence time of the channel is smaller than the symbol period. We propose a deep neural network (DNN)-based symbol detector using the long short-term memory (LSTM) neural network. Particularly, the sampling sequence of the received signal per symbol is used as the input data of each LSTM unit, which can take advantage of all received information and thus achieve better performance. Furthermore, a preprocessing unit using the basis expansion model (BEM) is designed to dramatically reduce the number of parameters while training the neural network, and the BEM-DNN-based detector achieves almost the same performance as the DNNbased detector. Finally, simulation results are achieved using the highly dynamic plasma sheath channel (HDPSC) data measured from realistic shock tube experiments. The results show that the proposed DNN-based method outperforms conventional methods and requires no prior channel estimation or knowledge of channel models.

The link flooding attack (LFA) arises as a new class of Distributed Denial of Service (DDoS) attacks in recent years. By aggregating low-rate protocol-conforming traffic to congest selected links, LFAs can degrade the connectivity of target servers indirectly. Due to the fast proliferation of insecure Internet of Things (IoT) devices, the deployment of botnets is getting easier, which dramatically increases the risk of LFAs. Since the attacking traffic may not reach the victims directly and seems to be legitimate, LFAs are extremely difficult to detect and defend using traditional methods. In this work, we model the interaction between the LFA attacker and the defender as an extensive form game with incomplete information. By using action space compression and the divide and conquer method, we analyze the Nash equilibrium of the subgame on each link, which reveals the rational behaviors of attackers and the optimal strategies of defenders. Furthermore, we concretely expound how to adopt local optimal strategies in the Internet-wide scenario. Experimental results show the effectiveness and robustness of our proposed decision-making method in explicit LFA defending scenarios.

Joint radar-communication designs are exploited in applications where radar and communications systems share the same frequency band or when both radar sensing and information communication functions are required in the same system. Finding a waveform that is suitable for both radar and communication is challenging due to the difference between radar and communication operations. In this paper, we propose a new method of designing dual-functional waveforms for both radar and communication using signals from multiplexed communications users. Specifically, signals from different communications users multiplexed in the time, code or frequency domains across different data bits are linearly combined to generate an overall radar waveform. Three typical radar waveforms are considered. The coefficients of the linear combination are optimized to minimize the mean squared error with or without a constraint on the signal-to-noise ratio (SNR) for the communications signals. Numerical results show that the optimization without SNR constraint can almost perfectly approximate the radar waveform in all the cases considered, giving good dual-functional waveforms for both radar and communication. Also, among different multiplexing techniques, time division multiple access is the best option to approximate the radar waveform, followed by code division multiple access and orthogonal frequency division multiple access.

Due to the agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications. In practice, UAV-aided aerial base stations are not separate. Instead, they rely on existing satellites/terrestrial systems for spectrum sharing and efficient backhaul. In this case, how to coordinate satellites, UAVs and terrestrial systems is still an open issue. In this paper, we deploy UAVs for coverage enhancement of a hybrid satellite-terrestrial maritime communication network. Using a typical composite channel model including both large-scale and small-scale fading, the UAV trajectory and in-flight transmit power are jointly optimized, subject to constraints on UAV kinematics, tolerable interference, backhaul, and the total energy of the UAV for communications. Different from existing studies, only the location-dependent large-scale channel state information (CSI) is assumed available, because it is difficult to obtain the small-scale CSI before takeoff in practice and the ship positions can be obtained via the dedicated maritime Automatic Identification System. The optimization problem is non-convex. We solve it by using problem decomposition, successive convex optimization and bisection searching tools. Simulation results demonstrate that the UAV fits well with existing satellite and terrestrial systems, using the proposed optimization framework.

Unmanned aerial vehicles (UAVs) have the capability of supplementing as well as improvising terrestrial cellular communications as aerial base stations to improve network coverage. This puts significance on the effective deployment of multiple UAVs while maximizing the coverage area in presence of co- channel interference generated by these UAVs. To this end, it is important to determine the parameters that affects the coverage area performance. In this paper, we investigate the effect of co-channel interference on the effective coverage of UAV-based small cells (USCs) deployed in a certain geographical area to satisfy a target signal-to-interference-plus-noise ratio (SINR) at the cell edge. We propose a coordinated multi- UAV strategy to evaluate the trade-off between the UAV separation distance and the overall coverage area assuming symmetric placement of UAVs at a common optimal altitude to ensure minimum transmit power. Numerical results unveil that the number of UAVs and the separation distance between them should be carefully designed to achieve the optimal coverage area performance.

Unmanned aerial vehicle (UAV) swarm-enabled aerial network has emerged as an effective solution to ondemand communications, especially in unexpected scenarios. Due to the broadcast nature of the air-to-ground link, UAV swarm-enabled wireless communications are inherently prone to eavesdropping. The paper investigates power allocation for UAV swarm-enabled secure networks. To depict air-to-ground link, a composite channel consisting of small-scale and large-scale fading is taken into account. Because of the difficulty in acquiring the time-varying small-scale fading, we use the large-scale channel state information (CSI). An optimization framework in a whole- trajectory-oriented manner is proposed to maximize secrecy throughput with the constraints on the transmission power and the transmission durations as well as the overall transmission energy per UAV over a given flight period. The formulated problem is not convex. To deal with that, we first derive a closed form of secrecy throughput in the form of high-order fixed-point equations. Then, we propose an iterative algorithm with successive convex approximation technique by alternately optimizing the variables. Numerical results validate the effectiveness of the proposed scheme and show that our proposed scheme can achieve a good secrecy performance.

The unmanned aerial vehicle (UAV) is a promising enabler of the Internet of Things (IoT) vision, due to its agile maneuverability. In this paper, we explore the potential gain of UAV-aided data collection in a generalized IoT scenario. Particularly, a composite channel model, including both large-scale and small-scale fading is used to depict typical propagation environments. Moreover, rigorous energy constraints are considered to characterize IoT devices as practically as possible. A multiantenna UAV is employed, which can communicate with a cluster of single-antenna IoT devices to form a virtual MIMO link. We formulate a whole-trajectory-oriented optimization problem, where the transmission duration and the transmit power of all devices are jointly designed to maximize the data collection efficiency for the whole flight. Different from previous studies, only the slowly varying large-scale channel state information is assumed available, to coincide with the fact that practically it is quite difficult to predictively acquire the random small-scale channel fading prior to the UAV flight. We propose an iterative scheme to overcome the nonconvexity of the formulated problem. The presented scheme can provide a significant performance gain over traditional schemes and converges quickly.

Generalized likelihood ratio test is an effective method for target detection in passive radar systems. The distribution of its decision variable in the presence of a direct path is unknown but is required for the calculation of the detection threshold and the detection probability. In this letter, several new approximations to this distribution are proposed by using moment matching. Numerical results show that the generalized extreme value approximation works consistently well for both null and alternative hypotheses with large or small signal-to-noise ratios. On the other hand, the Gaussian and logistic approximations only work well in the null hypothesis.

Unmanned aerial vehicle (UAV) communication provides a promising solution to emergency recovery and coverage extension. For Internet of Things (IoT) communications system, utilizing UAV as an on-demand gateway is an efficient technique to enable the communication between IoT devices over a long distance. In this paper, we investigate a mobile UAV-aided Internet of Things (IoT) communications system, where one UAV actes as a dynamic aerial base station to serve all the IoT devices. We aim to maximize the sum rate of the mobile UAV by jointly optimizing IoT device-UAV scheduling, the uplink transmission power of the IoT devices and the UAV altitude. This optimization is a mixed-integer non-convex problem, and an efficient iterative algorithm is proposed by means of the block coordinate descent technique. Finally, simulation results are presented to demonstrate that our proposed scheme significantly outperforms the existing one.

The plasma sheath channel has a serious impact on the propagation of electromagnetic waves, resulting in the radio blackout problem in aerospace communications. In existing studies, the plasma sheath channel characteristics are analyzed based on computer simulations, lack of real-world experimental verification. Thereby, these studies failed to fully demonstrate the high-dynamics of the plasma sheath channel. In this paper, an experimental communication system based on the shock tube is proposed to investigate the plasma sheath channel. Then, the characteristics of the plasma sheath channel are analyzed based on the experimental results. In particular, the high-dynamic and fast time-varying channel characteristics are verified by analyzing the signal amplitude, signal phase shift and the coherence time. Finally, we show an example of using the presented channel characteristics. A non-stationary signal segmentation method is proposed based on the reversible jump Markov chain Monte Carlo algorithm, which is applicable to the plasma sheath channel signal segmentation.

The contradiction between ever increasing satellite communication traffic and limited satellite transponder resources motivates a more dynamic allocation and more effective utilization of satellite transponders' resources. In this paper, the link capacity for a smart satellite transponder is maximized with limited available power and spectrum resource at satellite transponder. Specifically, given that the satellite transponder broadcasts the signals from the gateway station to the multiple satellite terminals with minimum transmission rate requirement, the satellite transponder needs to provide as large link capacity as possible to gateway station for the amount data transmission of special demands. The problem is formulated with aim of maximizing link capacity, and subject to minimum transmission rate requirement of link to satellite terminals and available resource allocation. The finely-matched dynamic power and spectrum allocation scheme is proposed to achieve maximization of both target link capacity and transponder resource utilization. Simulations results demonstrate that the proposed scheme outperforms tradition schemes and the superiority is even more remarkable in multi-constrained situations.

Enhancing throughput gain is an important target in the development of millimeter-wave (mmWave) communications. Spatial reuse is an important approach for throughput enhancement, which works well in mmWave networks due to directional links. In addition, multicast transmissions are highly desirable in mmWave systems, and become feasible with recent advances in antenna technology. In this paper, we address the problem of spatial reuse scheduling with multicast transmissions for mmWave communications. We formulate the problem as a Mixed Integer Non-Linear Programming (MINLP) problem and propose a low-computational-complexity heuristic algorithm, where potential links are fully utilized to exploit spatial reuse gain by dividing multicast demands into multiple unicast/multicast transmissions. Extensive simulation results demonstrate that the proposed scheme achieves near-optimal performance, and obtains high throughput gain with low packet loss rate.

The increasing traffic demand in both ground and satellite communication systems will lead to increasing spectrum demand. Spectrum sharing would become a challenging issue in future between terrestrial and satellite systems with frequency reusing, as well as the interference management. Upon this, we propose the concept of the Cloud Based Integrated Terrestrial-Satellite Network (CTSN), where both base stations of the cellular networks and the satellite are connected to a cloud central unit and the signal processing procedures are executed centrally at the cloud. By utilizing the channel state information (CSI), the interference from the mixed signal can be mitigated. When it comes to the case of imperfect CSI, we propose a resource allocation scheme in respect to subchannel and power to maximize the total capacity of the terrestrial system while limiting the total interference to the satellite. The optimization problem is solved by means of the dual decomposition method. Simulation results are provided to evaluate the effectiveness of the algorithm.

Non-orthogonal multiple access (NOMA) is a promising technology for handling massive connectivity in future 5G wireless networks. Even though the spectral efficiency can be promoted, the transmission reliability is sometimes poor, due to the introduced multiple access interference (MAI), and the computational complexity for multiuser detection is usually high in NOMA. To solve this problem, we investigate the NOMA regime with sparse multiple-access sequences, so as to leverage sparse signal processing methods, e.g., the message passing algorithm (MPA) for low-complexity and highly- reliable multiuser detection. Particularly, we formulate an optimization problem to design the sparsity of spreading sequences, while maximizing the efficiency of NOMA subject to the allowable symbol error rate (SER) constraint as well as the affordable detection complexity constraint. To evaluate the detection performance of MPA, we give the large-system limit analysis and derive the density evolution method. The impact of variable and function sparsity on the performance is then analyzed. Based on the uncovered characteristics of the optimization problem, an efficient algorithm is proposed to derive the optimal sparsity achieving the desired trade-off between efficiency, reliability and complexity.

The performance of a frequency division duplexing (FDD) massive multiple input multiple output (MIMO) system is traditionally limited by the large amount of overhead for downlink channel training and uplink channel state information (CSI) feedback. In this paper, we propose an environment-aware scheme to exploit massive MIMO gains in the FDD mode. Under a quasi-static scattering geometry and slow user mobility, the propagation environment can be known at a low cost. Given a priori environment information, the angular domain channel statistics can be obtained accordingly. In the proposed scheme, the angular domain is partitioned into several angular bins and the same number of predefined precoding vectors are generated accordingly. Based on the environment-specific angular domain information, the system topology is modeled as a bipartite graph. An efficient user scheduling algorithm is proposed, which is equivalent to finding a match of the bipartite graph, and a remarkable multiplexing gain is achieved by removing the overlapped angular bins. After user scheduling, each selected user is allocated to one predefined precoding vector. The numerical results have confirmed the validity of the proposed scheme.

In distributed multiple‐input multiple‐output (D‐MIMO) systems, the improvements of spectral and energy efficiencies rely heavily on the accuracy of the channel state information (CSI). In order to enhance the accuracy of CSI acquisition, the problem of pilot design in a D‐MIMO system is addressed in this study. In particular, the authors focus on the optimisation of pilot power adaptation in a single‐cell D‐MIMO system with multiple users served in orthogonal resources. Under the concept of tomographic channel estimation, the problem of pilot power adaptation aiming at maximising the lower bound of the sum capacity is formulated. As the computational complexity of solving the problem is relatively high due to the mutual coupling constraints, the dual decomposition technique is introduced to decouple the constraints and reduce the complexity via parallel computation. An effective pilot power adaptation scheme is further proposed by using the projected subgradient method. The superiority and effectiveness of the proposed scheme are illustrated by the simulation results.

Millimeter-wave (mmWave) communication is envisioned to provide orders of magnitude capacity improvement. However, it is challenging to realize a sufficient link margin due to high path loss and blockages. To address this difficulty, in this paper, we explore the potential gain of ultra-densification for enhancing mmWave communications from a network-level perspective. By deploying the mmWave base stations (BSs) in an extremely dense and amorphous fashion, the access distance is reduced and the choice of serving BSs is enriched for each user, which are intuitively effective for mitigating the propagation loss and blockages. Nevertheless, co-channel interference under this model will become a performance-limiting factor. To solve this problem, we propose a large-scale channel state information (CSI)-based interference coordination approach. Note that the large-scale CSI is highly location-dependent, and can be obtained with a quite low cost. Thus, the scalability of the proposed coordination framework can be guaranteed. Particularly, using only the large-scale CSI of interference links, a coordinated frequency resource block allocation problem is formulated for maximizing the minimum achievable rate of the users, which is uncovered to be an NP-hard integer programming problem. To circumvent this difficulty, a greedy scheme with polynomial-time complexity is proposed by adopting the bisection method and linear integer programming tools. Simulation results demonstrate that the proposed coordination scheme based on large-scale CSI only can still offer substantial gains over the existing methods. Moreover, although the proposed scheme is only guaranteed to converge to a local optimum, it performs well in terms of both user fairness and system efficiency.

Existing channel estimators for amplify-and-forward relaying often transmit pilots to the destination node by using the relay node's own energy. This extra energy consumption discourages the relay node from taking part in relaying. We propose two new estimators for amplify-and-forward relaying channels. In these estimators, the relay node harvests energy from the pilots sent by the source node first and then uses the harvested energy to forward the pilots from the source node as well as transmit its own pilots to the destination node. Both time-switching and power-splitting harvesting strategies are considered. The mean squared error is examined. Numerical results show that these new estimators have very good performances. They also show that optimum choices of certain system parameters exist.

The massively dense distributed antenna system (md-DAS) with virtual cells (VCs) has drawn increasing interests recently. In this paper, we develop an energy-efficiency-oriented coordinated power allocation (PA) scheme considering the inter-VC interference in a downlink md-DAS. The problem can be formulated as a complicated non-convex fractional programming problem. To make it tractable, we recast the problem into quasi-concave fractional programming sub-problems, by applying successive Taylor expansion. Then we transform these subproblems into the equivalent convex ones in a subtractive-form based on fractional programming method. An iterative energy-efficient coordinated PA algorithm is finally proposed. Simulation results illustrate that the proposed scheme can offer a significant performance improvement over the existing methods.

60‐GHz millimetre‐wave (mm‐wave) communication is gradually becoming a promising candidate for the next generation wireless system to meet the demands of mobile applications. To compensate for high path loss, directional links are established in mm‐wave communication system, which adds opportunity for spatial reuse. As one of its most promising protocols for commercial production, IEEE 802.11ad standard provides a mechanism to support space‐time division multiple access (STDMA) within a single network. However, the interference level among different co‐channel networks is much lower than that inside a network, which provides greater potential for spatial reuse. In this study, based on the architecture and timing structure of IEEE 802.11ad, the authors propose a spatial reuse strategy among multiple co‐channel networks. They formulate the problem as a mixed‐integer non‐linear programming problem, and then propose an inter‐network STDMA scheduling algorithm, which considers clustering frame structure in IEEE 802.11ad, and combines greedy principle and mutual interference avoidance strategy. Extensive simulation results have shown that the proposed scheme enlarges total throughput in comparison with STDMA inside each network, as well as non‐STDMA scheme. Meanwhile, it achieves the lowest packet loss rate under heavy traffic load.

The massive multiple input multiple output (MIMO) technique is a promising candidate to enormously increase the capacity of wireless networks. In a massive MIMO system, coordinated signal processing among different antennas is crucial to cope with the inevitable co-channel interference. However, it is normally difficult to perform perfect coordination in practical applications, due to the challenging requirement of global channel state information at the transmitter (CSIT). To solve this problem, this paper considers a massively distributed MIMO model, and presents dynamic-cell (DC)-based macro coordination, which requires only the instantaneous intra-DC CSIT and the slowly-varying large-scale inter-DC CSIT. Particularly, we first divide the system into a number of coupled user-centric DCs. Perfect coordination in the form of maximum ratio transmission (MRT) is adopted locally within each DCs based on instantaneous intra-DC CSIT. Then, we propose an inter-DC coordination approach termed as enhanced MRT to mitigate the inter-DC interference. The coordination is designed on the basis of large-scale inter-DC CSIT, which thus is referred to as macro coordination. Simulation results demonstrate that the proposed DC-based macro coordination can achieve a satisfactory performance gain in terms of system sum rate, while requiring much less CSIT than traditional schemes.

When non-orthogonal pilots are used in multi-cell systems, channel estimation would be corrupted by inter-cell interference. To tackle this problem, the design of pilot sequences for multi-cell distributed multiple-input multiple-output (MIMO) systems is addressed in this paper. We explore this issue by introducing discriminatory treatment of different channel parameters. Generally, the large-scale channel state information (CSI) is predictable and could be regarded as priori information in pilot design, due to its slowly-varying characteristics. In particular, by assuming the large-scale CSI is known a priori, we derive a lower bound on the achievable sum rate in the downlink with a linear detector at the mobile terminal (MT), taking both intercell interference and channel estimation error into account. The problem of pilot sequence design is first formulated, of which the target is to maximize the lower bound of the achievable sum rate with a total pilot power constraint for each cell. Afterwards, we solve the problem by introducing the iterative concave-convex procedure (CCCP) with the demonstration of its convergence. Simulation results illustrate the validity and superiority of the proposed pilot design scheme.

The integrated and/or hybrid satellite and terrestrial network has become more and more important because of its broad application prospect and has received considerable attention. At the same time, the integrated network also brings many challenges, especially the problem of interference. Due to the lack of frequency spectrum, frequency reuse is considered in the satellite network and the terrestrial network for enhancing spectral efficiency. However, this will cause considerable Co-Channel Interference (CCI) and thus interference coordination is imperative. In this paper, we propose an interference coordination scheme for the integrated satellite and terrestrial network. The satellite sends pilots for channel estimation at terrestrial base-stations, and transmits the received data to the terrestrial gateway. Then interference coordination is performed at the terrestrial gateway, where the interference channel is updated according to both the estimated information and the predicted change based on the positions. Furthermore, based on the scheme, we analyze the precision that needs to be reached and obtain a direct view on how the precision may influence the system performance.

In the downlink transmission system with a multi-antenna base station (BS) and a cluster of single-antenna mobile stations (MSs), the throughput can be greatly enhanced by exploiting the cooperating channel among MSs and forming virtual multiple input and multiple output (MIMO). With MSs close together, compress-and-forward is the most commonly used cooperation strategy. Considering the fact that in practical systems the transmit signal is usually finite-alphabet modulated symbols, e.g., quadrature amplitude modulation (QAM), the compress-and-forward cooperation is investigated correspondingly. Regarding the bit error rate (BER) performance of virtual MIMO systems, two regions are identified: the channel-noise-dominant region and the compression-noise-dominant region. In the channel-noise-dominant region, the cooperation system yields almost no loss compared to the BER lower bound. While in the compression-noise-dominant region, the system BER is limited, i.e., with an error floor. Through theoretical analysis, the minimum compression rate to guarantee that the system works in the channel-noise-dominant region is obtained. The minimum compression rate is determined mainly by three terms: the modulation alphabet size, the constellation position and the signal-to-noise ratio (SNR), whose explicit expression has also been derived.

In the implementation of distributed antenna systems (DASs), the antenna elements in some cases may only be deployed in the form of distributed antenna-clusters (ACs), due to various practical limitations. Consequently, correlation usually exists among the antenna elements within each AC. In contrast to most of the previous work that focused on the antenna correlation in a single-cell environment, this paper investigates the impact of the antenna correlation in a more general multi-cell scenario, where the inter-cell interference becomes the key challenge. We formulate a joint multi-cell input covariance optimization problem, accounting for the transmit antenna correlation, the propagation path-loss and the shadow fading. We show that the problem is a complicated non-convex problem. Moreover, the objective function, i.e., the ergodic sum capacity, is found difficult to be expressed in a straightforward form. After mathematical simplification, we propose an iterative inter-cell coordination scheme by adopting the successive approximation method. Simulation results demonstrate that, thanks to much more effective adaptation to the antenna correlation, the proposed scheme outperforms the existing ones and can significantly improve the system performance of a DAS with highly-correlated ACs.

Multicast services are highly desirable in 60-GHz millimeter-wave communications, but multicast beamforming remains a challenge due to special structure of high frequency system. To solve this problem, a transmit and receive multicast beamforming scheme has been proposed in this paper. Unlike conventional multicast models in LTE/EMBMS, every device in proposed system is equipped with a multi-antenna array, which helps enhance beam directivity and reduce interference. Two stages are put forward to generate transmit and receive beams for multicasting. Performance evaluations have shown that the total throughput of the network has been improved largely by using the proposed scheme compared with unicast beamforming. Additionally, the designed algorithm has a minor performance loss compared with previous quasi-optimal algorithm, but with much lower complexity, which makes it advantageous for practical use.

It has been long believed that the turbo equalizer leveraging a soft input/soft output (SISO) detector is effective for system performance enhancement in terms of bit error rate (BER). In practical applications, the implementation of SISO detection is usually challenging, due to its high computational-complexity. To address this issue, this paper proposes a low complexity SISO detector based on QR decomposition (QRD) and the M-search algorithm for single carrier block transmission systems. Benefiting from two unique properties called the aggregation property and the natural ordering property obtained by applying the QRD method into single carrier block transmission systems, the QRD-M based SISO detection algorithm can be dramatically simplified. Detailed analysis shows a linear growing computational-complexity. The extrinsic information transfer (EXIT) chart analysis tool is used to illustrate the performance of the proposed scheme. Both EXIT analysis and simulation results reveal that the turbo equalization with the proposed QRD-M detection algorithm could achieve a sub-optimal system performance close to the BER-optimal maximum a posteriori (MAP) detector at each iteration with a much lower computational-complexity.

While channel state information (CSI) at the transmitter is critical to the system capacity of multiple input multiple output (MIMO) broadcast channels, its acquisition is practically intricate. This paper offers an alternative and investigates a special MIMO broadcast channel, which exploits the benefit of receiver cooperation and renders it unnecessary to acquire CSI at the transmitter. The cooperation gain is studied in a system-wide perspective by taking into account the resource (power and bandwidth) consumed to establish the cooperation links. Built on that, the generalized cooperation spectral efficiency is defined.With a dedicated approximation, a near optimal resource allocation strategy is presented to maximize the cooperation spectral efficiency. Specially, the power allocation problem is simplified into a polynomial rooting problem. Simulation results show that the receiver cooperation can provide a performance gain and achieve the MIMO capacity. Moreover, performance loss incurred by the proposed method is negligible compared to the numerical exhaustive search scheme.

The frequency selective fading channels cause severe inter-symbol interference (ISI) and significantly degrade the bit error rate (BER) performance of broadband wireless communication systems. In this paper, a soft detection algorithm employing the idea of QR decomposition, data grouping and successive interference cancelation (SIC) is proposed for coded single carrier (SC) block transmission. We show that SC block transmission with unique word (UW) brings two unique features while employing the QRD detection method. We refer to them as the natural ordering property and the sparse property, respectively. The data block is divided into small groups and the log-likelihood ratio (LLR) of each element is derived from the bottom up. A flexible tradeoff between BER performance and detection complexity can be provided. Simulation results show that in frequency selective fading channels, the proposed scheme can obtain dramatic performance gain compared to the minimum mean square error (MMSE) single carrier frequency domain equalization(FDE) with low complexity.

Beamforming is necessary in 60GHz millimeter-wave communications in order to compensate for high path loss. A complete beamforming algorithm includes codebook design and beam training. The existing beamforming protocol of IEEE 802.15.3c standard has following flaws: (1) Not all three beamforming pattern types (quasi-omni pattern, sector and beam) have their own codebooks; (2) Almost every beam points to two directions, one of which is expected but the other one is unwanted; (3) Setup time of beam training is long. This paper proposes a codebook design and corresponding training procedure based on circular antenna array with a two-layer-structure. It allows every pattern type to own its corresponding codebook and highly directional antenna radiation pattern. The training setup time is reduced to around half of 3c training time. Performance evaluations also manifest that the proposed algorithm leads to less antenna gain loss and more robust performance than the 3c standard.

The virtual multiple input multiple output (MIMO) technique can dramatically improve the performance of a multi-cell distributed antenna system (DAS), thanks to its great potentials for inter-cell interference mitigation. One of the most challenging issues for virtual MIMO is the acquisition of channel state information at the transmitter (CSIT), which usually leads to an overwhelming amount of system overhead. In this work, we focus on the case that only the slowly-varying large-scale channel state is required at the transmitter, and explore the performance gain that can be achieved by coordinated transmissions for virtual MIMO with large-scale CSIT. Aiming at maximizing the achievable ergodic sum rate, the input covariances for all the mobile terminals (MTs) are jointly optimized, which turns out to be a complicated non-convex problem with a non-closed-form objective function. Further analysis reveals that the coordinated transmission problem can be recast as a Max-Min problem with a closed-form objective function and linear constraints. Then, by appealing to the successive approximation method and the saddle-point theory of concave-convex functions, we propose an iterative algorithm for coordinated transmissions with large-scale CSIT and establish its convergence. Simulation results corroborate that the proposed scheme converges quickly, and it yields significant performance gains compared to the existing schemes. Moreover, it is observed that the proposed scheme can achieve a nearly globally-optimal point under the diagonal input covariance constraint. Since the acquisition of large-scale CSIT is far less demanding than that of full CSIT, we believe that the proposed coordinated transmissions with large-scale CSIT in DASs shed some light on virtual MIMO in the making.

In this paper, the problem of joint power allocation (PA) for the downlink of a multi-cell distributed antenna system (DAS) is addressed. Motivated by practical applications, we focus on a reasonable scenario that only the large-scale channel state information at the transmitter (CSIT) is available. Based on the observation that the capacity-achieving input covariance for each cell is diagonal and that PA optimization is enough to achieve the maximum ergodic sum capacity, we formulate a joint PA optimization problem to maximize the ergodic sum capacity of the system with a total transmit power constraint for each cell. A rather precise closed-form approximation of the ergodic sum capacity is then introduced and taken as the objective function instead so that the original joint PA optimization problem can be simplified. Finally, we propose an iterative PA scheme based on the simplified joint PA optimization problem, in which Signomial Programming (SP) is used. Monte Carlo simulations show that the proposed scheme converges quickly and can offer nearly optimal system ergodic sum capacity. Thus, we refer the proposed PA scheme as a suboptimal one. Moreover, from the simulations we can see that a significant performance gain can be achieved by multi-cell joint PA in DAS with only large-scale CSIT.

In this paper, we address the problem of coordinated user scheduling for the downlink of a multi-cell distributed antenna system (DAS). With the practical assumption that only large-scale channel state information (CSI) is known at the transmitter, a low-complexity greedy scheduling scheme is proposed. In order to provide fairness among users, the proposed scheme adopts round-robin scheduling within each cell and optimizes the scheduling order for each cell to maximize the minimum ergodic capacity of the users. For each user, the selection transmission scheme (just the distributed antenna element (DAE) with the largest channel gain to the user is selected for transmission) is implemented and each selected DAE transmits with equal power. Simulation results demonstrate that the proposed greedy scheme promises much better performance than that without inter-cell coordination and its performance is quite close to the optimal one. Moreover, by simulations we find that the proposed scheme achieves nearly the same system ergodic sum capacity with the one targeted to maximize the system ergodic sum capacity.

In this note, we point out a few problems with the proof for Theorem 1 and Theorem 2 in the above-mentioned paper and give a correct version for the incorrect parts.

In this paper, the uplink of a multi-user distributed antenna system (DAS) with antenna elements deployed on a circle is investigated. We address the problem of calculating the sum-rate capacity with per-user power constraints. Based on system scale-up, we derive a good approximation of the sum-rate capacity by adopting random matrix theory. We also propose an iterative method to calculate the unknown parameters in the approximation. The approximation is illustrated to be quite accurate and the iterative method is verified to be quite efficient by Monte Carlo simulations.

In this paper, the downlink performance of distributed antenna systems (DAS) with random antenna layout is investigated. We consider the composite channel including large-scale fading and small-scale fading. When the large-scale channel state information, which usually varies slowly and is easy to be obtained, is available at the transmitter, the problem of power allocation among distributed antennas with the target of downlink capacity maximization is formulated. Based on system scale-up, we derive a precise approximation of the downlink ergodic capacity by adopting random matrix theory. We also propose an iterative method to calculate the unknown parameter in the approximation. Moreover, the approximation is proved to be concave on the transmit powers of the distributed antennas. Consequently, a simple sub-optimal power allocation scheme is proposed, with which the system capacity is illustrated to be quite close to the optimal one obtained by numerical optimizations.

In this paper, the downlink performance of a distributed antenna system (DAS) with random antenna layout is investigated. We address the problem of characterizing the downlink capacity with the generalized assumptions: (al) per distributed antenna power constraint, (a2) generalized mobile terminals equipped with multiple antennas, (a3) a multi-cell environment. Based on system scale-up, we derive a good approximation of the ergodic downlink capacity by adopting random matrix theory. We also propose an iterative method to calculate the unknown parameter in the approximation. The approximation is illustrated to be quite accurate and the iterative method is verified to be quite efficient by Monte Carlo simulations.