Terahertz (THz) wireless communications can support ultra-high data rates and secure wireless links with miniaturized devices for unmanned aerial vehicle (UAV) communications. In this paper, a three-dimensional (3D) non-stationary geometry-based stochastic channel model (GSCM) is proposed for multiple-input multiple-output (MIMO) communication links between the UAVs in the THz band. The proposed channel model considers not only the 3D scattering and reflection scenarios (i.e., reflection and scattering fading) but also the atmospheric molecule absorption attenuation, arbitrary 3D trajectory, and antenna arrays of both terminals. In addition, the statistical properties of the proposed GSCM (i.e., the time auto-correlation function (T-ACF), space cross-correlation function (S-CCF), and Doppler power spectrum density (DPSD)) are derived and analyzed under several important UAV-related parameters and different carrier frequencies, including millimeter wave (mmWave) and THz bands. Finally, the good agreement between the simulated results and corresponding theoretical ones demonstrates the correctness of the proposed GSCM, and some useful observations are provided for the system design and performance evaluation of UAV-based air-to-air (A2A) THz-MIMO wireless communications.

Terahertz (THz) wireless communications can support ultra-high data rate and secure wireless links with miniaturized devices for the unmanned aerial vehicle (UAV) communications. In this paper, a three-dimensional (3D) non-stationary geometry-based stochastic channel model (GSCM) is proposed for the multiple-input multiple-output (MIMO) communication links between the UAVs in THz band. The proposed channel model considers not only the 3D scattering and reflection scenarios (i.e. reflection and scattering fading), but also the atmospheric molecules absorption attenuations, 3D arbitrary trajectory and antenna arrays of both terminals. In addition, the statistical properties of the proposed GSCM, i.e. time auto- correlation function (T-ACF), space cross-correlation function (S- CCF), and Doppler power spectrum density (DPSD), are derived and analyzed with several important UAV-related parameters and different carrier frequencies, i.e., millimeter wave (mmWave) and THz bands. Finally, the good agreement of the simulated results and corresponding theoretical ones shows the correctness of the proposed GSCM, and some useful observations are provided for the system design and performance evaluation of UAV-based air- to-ground (A2G) THz-MIMO wireless communications.

This paper proposes a spatial correlation channel modeling of wireless leaky coaxial cables (LCXs) multiple-input multiple-output (LCX-MIMO) system with the impact of mutual coupling (MC), and tries to explain the reason why the channel capacity of LCX-MIMO system is not highly dependent on the LCX spacing. The expressions of the channel correlation function (CF) of LCX-MIMO system without/with the impact of MC are derived. In addition, the mutual impedance between LCXs of LCX-MIMO system is simulated by high frequency structure simulator (HFSS) software, and the impact of MC between LCXs on channel correlation is analyzed. The numerical results show that when the LCX spacing is very small, the channel CF of LCX-MIMO system with MC is much smaller than that of LCX-MIMO system with MC, and the channel CF of LCX-MIMO system continues to decrease slowly with the increase of LCX spacing, which indicates that the channel capacity of LCX-MIMO system is not highly dependent on LCX spacing.

Network function virtualization (NFV) offers a flexible and effective means to utilize heterogeneous resources for space-air-ground integrated networks (SAGINs), enabling seamless connectivity for data transmission over large spans. Converging the cloud and edge computing capabilities, SAGINs have the potential to further provision service function chains (SFCs) with various Internet applications. This is driving the need for efficient schemes of the cloud- or edge-based services in SAGINs. In this article, we propose a novel SAGIN architecture based on the cloud-serving and edge-processing collaboration. The cloud-based services are provisioned by the selected ground data centers (DCs), in which the traffic is processed by the virtual network function (VNF) hosted in the edge nodes and DCs. In such an architecture, the edge nodes enable flexible SFC provisioning solutions while DCs offer a variety of cloud-oriented network services. In addition, we apply anycast to further improve the agility of SFC provisioning. From these perspectives, we investigate the cloud-serving and edge-processing SFC provisioning problem leveraging anycast, concerning DC assignment, edge and VNF placement, SFC mapping, and delay constraints simultaneously. The joint problem is formulated by a mixed integer linear program (MILP) model to jointly minimize the communication and computation costs subject to their tradeoff. A decomposition approach is further developed for the sake of scalability. Results from numerical simulations show that the proposed approach can reduce overall costs by up to 32.99%.

By decoupling the software function on hardware devices, Network Function Virtualization(NFV) provides a new service architecture named Service Function Chain(SFC), which combines multiple Virtual Network Functions(VNFs) in a specific order. In order to reduce network resources consumption and improve the resource utilization, VNF sharing provides an effective solution for this requirement. However, traditional sharing methods lack a dynamic processing mechanism to select the deployment and shared node location according to the network state dynamically. Moreover, how to further optimize the utilization of network resources is challenging. This paper proposed a VNF sharing evaluation mechanism to evaluate and decide whether to share a VNF, then a node priority calculation mechanism was designed and mapped on node selection probability, which can select appropriate VNF to deploy or share VNF according to network state and resource requirements of SFC, finally, a reinforcement learning approach was utilized to update the selection probability of nodes and complete the VNF sharing process in air-ground network. The experimental results indicate that compared with other five benchmark algorithms, the proposed algorithm can reduce the transmission delay effectively, at the same time, it can improve node and link load resource utilization and acceptance rate of SFC after the VNF sharing.

In this paper, a space-time correlation model is proposed for unmanned aerial vehicle-based (UAV-based) THz multiple-input multiple-output (MIMO) wireless channels. This model considers not only the scattering and reflection fading on rough surface, but also the atmospheric absorption attenuation, the random deployment and arbitrary trajectory of UAV, antenna arrays of transmitter (Tx) and receiver (Rx). Furthermore, the statistical properties of this model, i.e. time auto-correlation function (T-ACF) and space cross-correlation function (S-CCF), are derived and thoroughly analyzed with several important UAV-related parameters and different carrier frequencies. Finally, the simulated results and corresponding theoretical ones match well, which shows the correctness of the proposed model.

With the widespread application of unmanned cluster technology, broadband, low latency, high flexibility, and reliable fifth-generation (5G) UAV communication networks are increasingly becoming a key issue. The traditional network technology faces several challenges, including irregular distribution of spectrum resources, real-time changes of network topology, and a variety of unforeseen services. The elastic optical networks (EONs) is integrated into UAV networks to effectively address resource fragmentation and optimize spectrum allocation with its high bandwidth, low latency and dynamic tunability in this article. A heterogeneous UAV-EON system is proposed to realize the collaborative management of network access and resource allocation. To obtain the approximate optimal solution of network access and routing and spectrum allocation (RSA) in UAV-EON system, this article presents a hybrid two-stage optimized algorithm which combines the global search function of whale optimization algorithm (WOA) with the local optimization function of genetic algorithm (GA), ensuring the consistency and effectiveness of the UAV network. Therefore, the algorithm can address the challenges of network selection, routing, and spectrum allocation in UAV-EON. Finally, we test the number of successful assignments and resource utilization under different workloads and network scale. Considering the high dynamic characteristics of UAV nodes and the fading characteristics of atmospheric laser channels, a real network scenario is constructed and a cross-layer optimal algorithm from physical layer to network is proposed. The research results show that compared with the traditional intelligent optimization algorithm, the proposed algorithm can improve by more than 10% in the success rate of task allocation and resource occupation.

Time-triggered (TT) flows are usually periodic in time-sensitive networks. However, nondeterministic end systems can generate TT flow frames with significant jitter (i.e., jittery TT flows). Jitter can cause frames to miss the TT windows scheduled for the current period, resulting in excessive access delays, which in turn affect the end-to-end deterministic transmission of the TT flows. In our previous study, we proposed the use of a dynamic multiwindow approach to achieve deterministic access to jittery TT flows; however, its window schedule computation is too slow, and this method is only suitable for small networks with a few TT flows. We therefore propose a group-based, fast scheduling method for accessing and transmitting the windows of jittery TT flows based on multiple windows. A combination of heuristic algorithms and solvers, including the establishment of TT window groups, division of the solution region, and integrated parallel and serial incremental coarse-and fine-grained computations, significantly improves the efficiency of TT window scheduling. For coarse-grained scheduling, by establishing large window clusters and central alignment, the complexity of scheduling is considerably reduced while keeping success rates high. Furthermore, the integer linear programming constraints and objective functions for this method are provided. Compared with the conventional dynamic multiwindow approach, the proposed approach reduces the scheduling time for TT windows by two orders of magnitude for a small star network with a small number of jittery TT flows. Moreover, the reduction in scheduling time becomes more pronounced as the network topology complexity and number of jittery TT flows increase. Finally, the scheduling time performance of the proposed method is verified in commonly used star, tree, and bus networks. Evaluations demonstrate that the access and transmission windows for 500 jittery TT flows can be scheduled in these networks, enabling deterministic access and significantly improving scheduling efficiency.

A microwave photonics Doppler frequency shift (DFS) and angle of arrival (AOA) measurement system with a large spurious-free dynamic range (SFDR) is proposed in this article. The system integrates the linearization of microwave photonic systems with DFS and AOA measurement, achieving a high-precision, wideband measurement capabilities while enabling long-distance, high-quality signal transmission. By effectively suppressing the third-order intermodulation distortion (IMD3), second-order intermodulation distortion (IMD2), and DC components, the SFDR is significantly enhanced, and the micro-DFS measurement capability is expanded. The system enables long distance transmission and measurement through the suppression of periodic power fading, facilitates DFS measurement and direction discrimination via the implementation of I/Q down-conversion, and wide-angle AOA measurement is achieved through phase analysis. Compared to traditional down-conversion system based on dual-parallel Mach-Zehnder modulator (DPMZM), the proposed system demonstrates a 10 dB improvement in SFDR3. Furthermore, experimental verification shows a DFS measurement error of +/- 0.2 Hz, while the AOA measurement error is +/- 1.5 degrees within a range close to 150 degrees.

Unmanned aerial vehicle (UAV) as an aerial base station or relay device is a promising technology to rapidly provide wireless connectivity to ground device. Given UAV’s agility and mobility, ground user’s mobility, a key question is how to analyze and value the performance of UAV-based wireless channel in the terahertz (THz) band. In this paper, a three-dimensional (3D) time-varying channel model is proposed for UAV-based dual-mobility wireless channels based on geometric channel model theory in THz band. In this proposed channel model, the small-scale fading (e.g., scattering fading and reflection fading) on rough surfaces of communication environment and the atmospheric molecule absorption attenuations are considered in THz band. Moreover, the statistical properties of the proposed channel model, including path loss, time autocorrelation function (T-ACF) and Doppler power spectrum density (DPSD), have been derived and the impact of several important UAV-related and vehicle-related parameters have been investigated and compared to millimeter wave (mm-wave) band. Furthermore, the correctness of the proposed channel model has been verified via simulation, and some useful observations are provided for the system design of THz UAV-based dual-mobility wireless communication systems.

In this paper, a three-dimensional (3D) time-varying channel model is proposed for unmanned aerial vehicles (UAVs) air-to-air (A2A) wireless channels based on geometric channel model theory in terahertz (THz) band. In this proposed channel model, the scattering fading and reflection fading on rough surfaces of propagation environments, and the atmospheric molecules absorption attenuations are considered in THz band. Moreover, the statistical properties of the proposed channel model, including path loss, time autocorrelation function (T-ACF) and Doppler power spectrum density (PSD), have been derived and analyzed with the several important UAV-related parameters and different carrier frequencies (i.e. millimeter wave (mm-wave) and THz bands). Finally, the correctness of the proposed channel model has been verified via simulation, and some useful observations are provided for the system design of THz UAV-based A2A wireless communication systems.

In this paper, we propose a novel Space-Air-Ground Integrated Networks (SAGINs) architecture based on cloud-edge collaboration leveraging network function virtualization (NFV). The cloud-based services are provisioned by the selected ground datacenters (DCs) and the traffic is processed by the virtual network function (VNF) hosted in edge nodes and DCs. DCs provide various cloud-oriented network services, and edge nodes enable flexible solutions for SFC provisioning. We apply anycast to further improve the agility of SFC provisioning. From these perspectives, we investigate the cloud-serving and edge-processing SFC provisioning problem leveraging anycast, concerning DC assignment, edge and VNF placement, and SFC mapping simultaneously. A mixed integer linear program (MILP) model is formulated to jointly minimize the communication and computation cost subject to their trade-off. A decomposition approach is further developed for the sake of scalability. Numerical simulation results demonstrate that the proposed scheme provides savings of up to 12.44% overall cost.

The relationship between wavelength demand and network physical connectivity is studied to solve the wavelength resource shortage problems in dynamic optical satellite network, and a method based on a time-space conflict map is proposed to analyze the characteristics of wavelength resources. The method for randomly generating network topology connections is utilized to discretize the dynamic topology of an optical satellite network into a space conflict map and a time conflict map, which express the physical connectivity and effective service window of the optical satellite network, respectively. Based on the time-space conflict map theory, the path selection probability of space conflict avoidance and the effective window service probability of time storage are established and multiplied to obtain the conflict probability in time-space and calculate the number of wavelengths required in the network. The results show that wavelength requirements are closely related to the network physical connectivity, maximum number of link hops, and number of transponders. More wavelength resources should be allocated to an optical satellite network with a larger business overlap factor.

In view of the problem faced in Aviation Information Network (AIN) that the unbalanced load of platforms caused by the dynamically arrived service function chains (SFCs), we formulate the SFC migration problem into a multi-objective optimization model and propose a coalitional game based migration algorithm (CGM). In this paper, we take the aviation platform as the game player, the virtual network function (VNF) as the game commodity, and perform the migration of VNFs through the comparation and swap between different platforms to realize the efficient management of network resources. Experimental results show that the proposed algorithm has the advantages of low computational complexity and fast convergence rate, which can effectively reduce the network energy consumption and migration overhead.

Offloading traffic from terrestrial areas to low-orbit satellite networks, the ultra-long-distance and ultra-large-range connectivity can be achieved by on-board routing and transmission. This paper investigates the relay-assisted NOMA-enabled uplink traffic offloading in the integrated satellite-terrestrial network. The joint energy efficiency maximum problem is formulated and then decomposed into two subproblems, i.e., the decoding sequence and power allocation (DS-PA) problem, and the users-satellites-subchannels association and transmit power allocation (USSA-TPA) problem. For DS-PA problem, the decoding sequence constraint is removed, and then the successive convex approximation method is utilized to solve it. To tackle the USSA-TPA issue, we reformulate it into two subproblems and resort to the alternate iteration method to deal with them iteratively. The numerical results show that the proposed joint optimization of energy efficiency for uplink traffic offloading cooperated with NOMA scheme outperforms other methods and their combinations. The performance of proposed USSA scheme and power allocation method is also discussed.

Network virtualization has become a promising paradigm for supporting diverse vertical services in Software Defined Networks (SDNs). Each vertical service is carried by a virtual network (VN), which normally has a chaining structure. In this way, a Service Function Chain (SFC) is composed by an ordered set of virtual network functions (VNFs) to provide tailored network services. Such new programmable flexibilities for future networks also bring new network management challenges: how to collect and analyze network measurement data, and further predict and diagnose the performance of SFCs? This is a fundamental problem for the management of SFCs, because the VNFs could be migrated in case of SFC performance degradation to avoid Service Level Agreement (SLA) violation. Despite the importance of the problem, SFC performance analysis has not attracted much research attention in the literature. In this current paper, enabled by a novel detailed network debugging technology, In-band Network Telemetry (INT), we propose a learning based framework for early SFC fault prediction and diagnosis. Based on the SFC traffic flow measurement data provided by INT, the framework firstly extracts SFC performance features. Then, Long Short-Term Memory (LSTM) networks are utilized to predict the upcoming values for these features in the next time slot. Finally, Support Vector Machine (SVM) is utilized as network fault classifier to predict possible SFC faults. We also discuss the practical utilization relevance of the proposed framework, and conduct a set of network emulations to validate the performance of the proposed framework.

Distributed Denial-of-Services (DDoS) are serious network threats hardly eliminated. Current network entropy-based DDoS detection methods suffer from distinguishing DDoS attack traffic among normal traffic through a fixed empirical detection threshold, i.e., most of such thresholds are case-sensitive ones. With the Rényi entropy of a network, the paper devised a Generalized Network Temperature (GNT) based approach for DDoS attack detection, where GNT is a novel and fine-granular-scale statistical indicator that describes the network entropy changes in the light of both network traffic and network topology changes. Within a series of predefined time windows, our proposed approach first collects the selected network traffic features and then calculates the GNT for each time window. Second, the DDoS attacks are then acknowledged or denied by comparing each GNT to a dynamically adjustable thresh-old generated by the Exponentially Weighted Moving Average (EWMA) model. Furthermore, the publicly available CIC DoS 2017 dataset is utilized to test the proposed approach in the paper. The experimental results show that our proposed approach outperforms the known Shannon entropy-based DDoS attack detection methods with respect to both efficacy and efficiency.

The Aviation Information Network (AIN) scenario has the characteristics of services bursting, diverse types and delay sensitivity. To solve the service function chain (SFC) mapping problem, this paper proposes a SFC mapping algorithm based on network function virtualization (NFV). Firstly, the SFCs are deployed in the platforms with high relativity, and then the network function instances are integrated and migrated considering the impact of service delay and business traffic, to reduce the network energy consumption and improve the resource utilization. The simulation results show that the proposed approach can effectively optimize the network performance in terms of the running platform numbers, SFCR acceptance rate, and the network resource consumption while guaranteeing the service delay requirements, which is suitable for solving the SFC mapping problem in the AIN scenarios.

Leveraging on Network Function Virtualization (NFV) and Software Defined Networking (SDN), network slicing (NS) is recognized as a key technology that enables the 5G Infrastructure Provider (InP) to support diversified vertical services over a shared common physical infrastructure. 5G end-to-end (E2E) NS is a logical virtual network that spans across the 5G network. Existing works on improving the reliability of the 5G mainly focus on reliable wireless communications, on the other hand, the reliability of an NS also refers to the ability of the NS system to provide continued service. Hence, in this work, we focus on enhancing the reliability of the NS to cope with physical network node failures, and we investigate the NS deployment problem to improve the reliability of the system represented by the NS. The reliability of an NS is enhanced by two means: firstly, by considering the topology information of an NS, critical virtual nodes are backed up to allow failure recovery; secondly, the embedding of the augmented NS virtual network is optimized for failure avoidance. We formulate the embedding of the augmented virtual network (AVN) to maximize the survivability of the NS system as the survivable AVN embedding (S-AVNE) problem through an Integer Linear Program (ILP) formulation. Due to the complexity of the problem, a heuristic algorithm is introduced. Finally, we conduct intensive simulations to evaluate the performance of our algorithm with regard to improving the reliability of the NS system.