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With the growing demand for communications in remote and dispersed areas, Internet-of-Remote Things (IoRT) networks with joint unmanned aerial vehicle (UAV) access and geostationary orbit (GEO) satellite backhaul hold great promise to provide sufficient access services to Internet-of-Things (IoT) users and devices. As the fundamental of the performance optimization of IoRT networks, the performance analysis sheds light on the relationship between the network performance (i.e., backlog, delay, and throughput) and access scale (i.e., the numbers of UAVs and UAV users). Aiming at the challenges brought by the complex network structure (i.e., two-level queuing network along with the converged traffic), we introduce the stochastic network calculus-based min-plus convolution and the leftover service to mathematically describe the complex structure. For the analytical challenges of the continuous-time arrival process and heterogeneous two-level link capacities, we innovatively prove their supermartingale features and further derive the closed-form expressions of the network backlog and delay bounds based on the martingale theory. To pursue higher throughput while guaranteeing delay performance, we formulate a mixed-integer optimization problem of the access scale that contains a nondifferentiable variable derived from a transcendental equation. For the tractability, we propose a three-directional iterative (TDI) algorithm to search the optimal solution of the optimization problem. Simulation results verify the tightness of our performance bounds in contrast to the standard bound and the effectiveness of the proposed algorithm.

Space-air-ground networks play important roles in both fifth generation (5G) and sixth generation (6G) techniques. Low earth orbit (LEO) satellites and high altitude platforms (HAPs) are key components in space-air-ground networks to provide access services for the massive mobile and Internet of Things (IoT) users, especially in remote areas short of ground base station coverage. LEO satellite networks provide global coverage, while HAPs provide terrestrial users with closer, stable massive access service. In this work, we consider the cooperation of LEO satellites and HAPs for the massive access and data backhaul of remote area users. The problem is formulated to maximize the revenue in LEO satellites, which is in the form of mixed integer nonlinear programming. Since finding the optimal solution by exhaustive search is extremely complicated with a large scale of network, we propose a satellite-oriented restricted three-sided matching algorithm to deal with the matching among users, HAPs, and satellites. Furthermore, to tackle the dynamic connections between satellites and HAPs caused by the periodic motion of satellites, we present a two-tier matching algorithm, composed of the Gale-Shapley-based matching algorithm between users and HAPs, and the random path to pairwise-stable matching algorithm between HAPs and satellites. Numerical results show the effectiveness of the proposed algorithms.

Content caching has limitations on achieving cache gains (e.g., cache hit ratio) in small cell networks, due to limited storages of small base stations (SBSs) and inherent user demand patterns (i.e., initial content preferences). Two effective approaches have been proposed to exploit the potential of content caching: SBS cooperation to utilize cache storage, and proactive content recommendation to shape user demand. In this paper, we investigate cooperative content caching and recommendation to maximize cache gains, while guaranteeing users’ satisfaction by recommending appealing content items. We propose a generic framework for cooperative content caching and recommendation, based on which we propose an online and distributed scheme by designing a continuous-time Markov chain (CTMC). In particular, online content caching (a.k.a., content replacement) is implemented by hopping from one cache state to another in the CTMC, while content recommendation is performed heuristically through sequential fixing at each cache state. Besides, we characterize the performance gap between our proposed scheme and the theoretical optimum in terms of cache hit ratio. Simulation results demonstrate that the proposed scheme achieves better cache hit ratios than other schemes in single-BS scenarios, and provides a competitive solution in multiple-BS scenarios.

Spherical wavefront and array nonstationarity due to physically large antenna array are two typical characteristics specific to massive multiple-input–multiple-output (M-MIMO) propagation. Unfortunately, traditional MIMO channel models need to be enhanced to better characterize these two new propagation features. In light of this limitation, this article proposes an enhanced two-dimensional geometry-based stochastic channel model for 5G M-MIMO communications in real propagation environments. Specifically, a coordinate-based channel model framework is first introduced. Based on this, the impact of spherical wavefront can be mathematically described with the aid of some spatial geometric manipulations. Moreover, a general Markov process is developed to model the array nonstationarity characteristic due to cluster appearance and disappearance along the array. Meanwhile, the associated approach to derive the state transition probabilities of the proposed Markov process is detailed. Finally, capabilities of the proposed channel model in characterizing spherical wavefront and array nonstationarity of M-MIMO channels are shown by the numerical simulations. Moreover, validity of our proposal is verified by the measurement data.

The satellite data relay networks (SDRNs) hold great promise in 6G communications for the timely offloading of the global traffic. Since the delay performance is regarded as one of the most important metrics reflecting the offloading efficiency, studying its relationship with network parameters becomes really essential to the development and application of the SDRN. However, the complex data offloading process and heterogeneity of traffic arrivals and transmission links pose many challenges to the stochastic delay analysis. To accurately model the data offloading process in SDRNs, we build a series-parallel queuing model with through and cross traffic while considering the propagation delay. On this basis, we respectively propose a propagation delay embedded min-plus convolution method based on stochastic network calculus and a Markov chain method based on Monte Carlo to depict the leftover services of the heterogeneous links received by the per-flow traffic in an aggregate. To eliminate the impacts of the heterogeneity, we uniformly characterize the arrivals and leftover services by their moment generating functions (MGFs) which contain the full moment information, and shield the heterogeneity by deriving the envelopes of the arrivals and leftover services with the help of MGFs, Chernoff bound and union bound. Then, in the light of the geometric relationship between the envelopes of the arrivals and leftover services, we analyze the upper bounds of the stochastic delay, which provides the guidance to the network configuration. Eventually, simulation results verify the effectiveness of the theoretical analysis and further reveal maximum four times the delay difference between the heterogeneous links influenced by traffic type, burstiness, and access number.

Exploiting the time-varying feature of wireless channels, retransmission could enable reliable data transmission. However, the growing deployment of small cell base stations (BSs) would induce significant temporal interference correlation. In consequence, once the current transmission fails, the subsequent ones are likely to fail as well. In this light, we investigate the retransmission performance in dense networks with temporally correlated interference in terms of network spatial throughput (ST). Our results reveal that the impact of temporal interference correlation on the retransmission performance critically depends on the BS density. Specifically, temporal interference correlation would cause a greater network ST attenuation when the BS density is closer to the critical density, under which network ST is maximized. Moreover, the impact of temporal interference correlation is shown to be cumulative as the number of retransmission attempts increases. Furthermore, towards efficient retransmission, we adopt and optimize a P-Activation strategy (PAS). It is shown that the optimized PAS is able to effectively mitigate the overwhelming strength and temporal correlation of interference. As a result, the retransmission performance in improving network ST is significantly enhanced in dense networks, while the variation of network ST with BS density exhibits sigmoid trend instead of the previous near-bell shape.

In the fifth-generation (5G) mobile communication system, base stations become denser, and user equipment becomes more powerful. Multiple-association technique promises diversity gains for 5G. However, existing multiple-association schemes are centralized, which have high signal overhead and depend on the reference signal receiving power, and cannot adapt to the fluctuating traffic of users. Therefore, it calls for a new distributed multiple-association scheme. We model the multiple-association problem as a state-based potential game (SPG). With the aid of SPG, the association policy that we designed could drive the ultradense networks to evolve toward the global optimization in the flow level performance. Finally, the performance of our proposed traffic-aware multiple association (TAMA) algorithm is investigated through a practical and discrete simulation method.

Finite element analysis method is used to study the wrinkling of the inner wall of the “日”-shaped profile during the rotary draw bending process in this paper. Based on Abaqus/explicit platform, we establish finite element analysis (FEA) models of rotary draw bending process with flexible core device and rigid core device and discuss the improvement methods of two types of core and the causes of defects in profile forming. Comparing with the forming effect of two kinds of core devices in the rotary draw bending process through FEA, it is proved that adopting a rigid core device can reduce wrinkling obviously. It provides a theoretical basis for the reasonable design of the mold for the “日”-shaped section and has certain reference value for the actual production.

Predicting traffic by exploiting its regularity in spatio- temporal variation enables intelligent resource provisioning and management in wireless networks. Traditional prediction techniques are largely based on exploiting traffic spatio-temporal correlation, that is, the statistical relationship between the current traffic and the historical data in a single cell (temporal correlation) or neighboring cells (spatial correlation). Such an approach is effective in predicting the regular components in traffic. However, as traffic types are becoming increasingly diversified, the random components in traffic are becoming dominant. This results in decreasing accuracy in correlation-based prediction techniques. In view of the limitation of existing techniques, we propose a new approach of traffic prediction by exploiting the predictable causality in wireless traffic, which arises from the causal relationship between the occurrences of special events and the triggered traffic variations. Building on the approach, we propose a novel framework of correlation- and causality-based prediction (Coca-Predict) that integrates the two types of prediction to exploit their complementary strengths to maximize prediction accuracy. Specifically, traffic information in spatio-temporal and other dimensions are fed into the correlation and causality sub-predictors to predict the regular component and variation tendency of the traffic, respectively. Experimental results based on realistic traffic data at a specific airport demonstrate that Coca-Predict outperforms the state-of-the-art prediction techniques by exploiting traffic causality. Finally, open challenges and opportunities for wireless traffic prediction are highlighted to shed light on this important direction in designing intelligent wireless networks.

Satellite Data Relay Networks (SDRNs) play an important role in the data relay from User Satellites (USs) to ground stations by Tracking Data Relay Satellites (TDRSs). For better exploitation of SDRNs, the development of the systematic model and accurate system analysis is essential. To describe the end-to-end data transmission in SDRNs, we construct an MMOO/MMSP/1/K-G/G/1 tandem queuing model where the two parts depict the traffic arrival and transmission service of USs and TDRSs, respectively. Because the active and inactive periods of the data transmission are determined by the visibility between two satellites, classical buffer state based vacation policies become imprecise. Moreover, these two kinds of periods appear alternatively and their duration varies over time so that it is hard to model such intermittent transmission by existing service models. To overcome these difficulties, we propose a Markov Chain Monte Carlo based Markov Modulated Service Process (MMSP) which can tightly match the distributions of the active and inactive periods. In this process, we propose two algorithms to calculate the service state transition probability and the number of the sub-states in each service state, respectively, which guarantees the alternative transition between the active and inactive states as well as the sojourn time spent in each state. For the quality of service analysis, we find the different features of the queue variation under different arrival and service rate conditions. By separately calculating the related mean queue lengths and emergence probabilities, we first derive the expressions of the system loss probability, mean queue length, and mean delay. Finally, we conduct numerous simulations to verify the accuracy of our system model and performance evaluation, which provides the guidance to the buffer design and transmission resource allocation.

SIN is constructed to support real-time data acquisition, massive data transmission and processing, and systematized information services. Different from static networks, both the network topologies and the available network resources are dynamic in SIN. However, the existing networking technologies treat SIN as segmented static networks, and ignore the relationship among different segmented subnetworks, which results in low resource utilization and poor transmission QoS. This article exploits the time-varying graph theory to characterize and allocate the dynamic resources of SIN. We first introduce the overall SIN architecture, and discuss its key technologies in terms of network slicing and dynamic routing. Then, we construct a novel STAG to precisely depict the multi-dimensional time-varying resources and reveal their connection relationships in SIN. With STAG, we propose the dynamic network slicing strategy to build the dedicated network slices for different services, and design the intra-slice routing scheme to guarantee the service transmission QoS. Simulation results demonstrate our methods can complete more space missions with higher resource utilization. Finally, we discuss the promising future of the time-varying graph theory.

A method of the scattering Phase Function Scaling (PFS) for determination of the direct normal irradiance (DNI) involving the circumsolar normal irradiance (CSNI) is developed. A fraction of the normalized phase function integrated over the field of view for a pyrheliometer is introduced in the radiative transfer equation so the scaled optical depth using this phase function is derived. The PFS method is tested in a radiative transfer scheme and a simple fast parametric scheme SUNFLUX using the observational datasets at 14 Baseline Surface Radiation Network (BSRN) stations. The evaluation results show that the modelled DNI in terms of the PFS method in the radiative transfer scheme is significantly improved compared with that using the original delta-Eddington scaling method and compared well with that determined using other two radiative transfer models of DISORT and SMARTS. The modelled DNI with the PFS method in the SUNFLUX scheme is also improved compared with that without using the PFS method and compared well with that from the SMARTS model. The CSNI is also evaluated against the results from the other three models. The results are generally poor in all cases. The poor results are mainly due to the small magnitude of CSNI and uncertainties in the input data. Uncertainty in the aerosol scattering phase function could be the main reason responsible for the poor comparisons.

In this paper, finite-time consensus control method is presented for a robot nonlinear multi-agent system. To achieve the goal of finite-time consensus control in the presence of input saturation as well as external disturbance, a new adaptive-terminal sliding mode control method has been proposed. The adaptive method is used to handle an external disturbance with unknown bound, while the terminal sliding mode makes it possible to achieve finite-time consensus and reduce settling and reaching times. In the proposed method, the resulting control signals are always below the saturation range, and of course the external disturbance is estimated in a limited time. The simulation results on nonlinear multi-agent robot system show the optimal performance of the proposed method.

Orbital angular momentum (OAM) has aroused a widespread interest in many fields, especially in telecommunications due to its potential for unleashing new capacity in the severely congested spectrum of commercial communication systems. Beams carrying OAM have a helical phase front and a field strength with a singularity along the axial center, which can be used for information transmission, imaging and particle manipulation. The number of orthogonal OAM modes in a single beam is theoretically infinite and each mode is an element of a complete orthogonal basis that can be employed for multiplexing different signals, thus greatly improving the spectrum efficiency. In this paper, we comprehensively summarize and compare the methods for generation and detection of optical OAM, radio OAM and acoustic OAM. Then, we represent the applications and technical challenges of OAM in communications, including free-space optical communications, optical fiber communications, radio communications and acoustic communications. To complete our survey, we also discuss the state of art of particle manipulation and target imaging with OAM beams.

This paper aims at shedding light on the impact of unsaturated traffic on the performance of uplink non-orthogonal multiple access (NOMA) transmissions. Nevertheless, the unsaturated traffic gives rise to the discontinuous interference and the inherent interaction of queues, which in turn highly complicates the performance evaluation. By utilizing tools from queuing theory, we first explicitly characterize the stable throughput region, which represents the region of traffic arrival rates on the condition that the queuing delay converges in distribution to a bounded random variable. In light of this, the critical condition under which NOMA can extend the stable throughput region of orthogonal multiple access (OMA) is derived. Then, we propose an algorithmic solution to evaluate the average delay incurred from both queuing and transmission. It is interestingly found that the superiority of NOMA over OMA in terms of average delay heavily hinges on the temporal traffic dynamics of each user. In particular, NOMA enjoys a clear advantage when the traffic arrival rate of the user with stronger channel condition considerably exceeds the traffic arrival rate of the user with weaker channel condition. The derived results can provide helpful guidance to fully leverage the comparative advantages of NOMA under various traffic conditions.

The mobile telecom carriers have deployed massive infrastructures that support or carry the data signal transmission. Most of them are passive devices lacking the ability to actively monitor and automatically report information, which are called “dumb devices.” At present, the dumb device management has problems, such as information incomplete or inaccurate, and lack of dynamic update mechanism. For catering to the construction of smart cities, we design an information management system considering the full lifecycle management for dumb devices to realize real-time or periodical context awareness and information transmission based on narrow band Internet of Things (NB-IoT). Compared to the existing radio frequency identification (RFID)-based solutions, which require RFID readers and electronic tags and have a limited sensing distance, the NB-IoT-based solution for dumb device management has advantages in transmission distance and communication stability. The NB-IoT terminal is attached to the dumb device, and a global positioning system module is installed on it to obtain the positioning information. The NB-IoT terminal is controlled by an real-time clock (RTC) alarm to periodically enter the low power mode and then wake up to automatically collect the location and battery information and upload it to the server. The application objects of this information management system can be extended to dumb devices in other industries.

In this paper, we analyze the feasibility of interference alignment (IA) schemes with $T$ finite channel extensions for multi-input–multi-output (MIMO) interference broadcast channel (IBC) with common messages, where each base station transmits one common single data stream to all users within its cell. We investigate necessary conditions of feasibility of the IA schemes for this network in three different scenarios, i.e., with no channel extensions ( $T=1$ ), with $T$ ( $T\ge 2$ ) channel extensions for a positive measure of channel coefficients, and with $T$ ( $T \ge 2$ ) dependent channel extensions. These necessary conditions impose constraints on system configurations, e.g., the number of users, the number of antennas, the number of cells, and the number of channel extensions, which must be satisfied when the IA schemes are feasible, and also implicitly provide constraints on the upper bound of degrees of freedom (DoF) with finite channel extensions.

As the typical delay tolerant networks, the satellite networks possess the intermittent connections, the large-scale time delays and the time-varying topologies. Obviously, such features seriously affect the delivery of mission data with certain requirements on the traffic or latency. To decrease the delivery time, existing relay-based contact graph routing (CGR) method selects the earliest reachable path to forward mission data. However, this method cannot guarantee the transmission of a large amount data and wastes the rare contact opportunities. Therefore, in this paper, we focus on the transmission QoS problem of multiple missions over the satellite networks, and design a QoS support routing strategy to achieve multiple flow-maximizing paths with acceptable delivery delays. Especially, with the storage time aggregated graph (STAG), we construct an on-demand mission model to depict both the network dynamic characteristics and the different mission requirements, and then reduce the QoS support problem as a graph-based maximum flow problem. To solve this problem, one STAG-based multiple flow-maximizing routing scheme is proposed to ensure the mission QoS and match the rare network resources, which has low computation complexity. Finally, compared with CGR, simulation results demonstrate the proposed scheme can achieve a higher mission completion rate and resource utilization rate.

Both complicated interference and frequent handoffs exist in 5G ultra-dense small cell networks (UDNs), which significantly degrade the system performance. Therefore, a novel power control and simple traffic offloading scheme is proposed to handle the interference and handoffs. Owing to the severe interference coupling, this problem is formulated as a dynamic stochastic game (DSG) between small cells, which can characterize the stochastic property of channel states. Nevertheless, it is hard to solve the DSG system when the number of players exceeds 2. Therefore, to solve the DSG system in UDNs, we use the mean field approximation theory and convert the DSG into a mean field game (MFG) in which the game equilibrium is analyzed by solving two tractable partial differential equations, from the global and individual perspectives. In contrast to the conventional MFG works, there usually exists a dominating player, which introduces higher interference due to the location or larger transmit power in UDNs, for assumed generic player. Consequently, we formulate the framework of MFG with a dominating player and lots of generic players. Within the formulated framework, first we design the combined cost function considering both the aggregate interference and signal-to-interference-plus-noise ratio performances for both the generic player and its dominator. Then, we derive the corresponding Fokker–Planck–Kolmogorov and Hamilton–Jacobi–Bellman equations. Next, we propose a novel interference-aware traffic offloading and power control policy. Finally, we present numerical results showing the energy and spectrum efficiency performances of the proposed policy compared with the policy without traffic offloading.

The emerging distributed satellite cluster network (DSCN) holds great promise in various practical fields, including earth observation, disaster rescue, and tracking of forest fires. In the DSCN environment, it is essential to achieve the best data delivery performance by coordinating multi-dimensional heterogeneous and dynamic resources. However, in real-world applications, the distribution of long-term data arrival is not often fully known. Motivated by this fact, we propose a distributionally robust two-stage stochastic optimization framework with considering the dynamic network resources and the partially known distribution information of long-term data arrival. Aiming at maximizing the total network reward, we formulate a two-stage stochastic flow optimization problem based on the extended time expanded graph. Then, we introduce an ambiguity set for the uncertain distribution of the long-term random data arrival inspired by the idea from the distributionally robust optimization. On the basis of the proposed ambiguity set, we further propose a data arrival distribution robust two-stage recourse (DADR-TR) algorithm by converting the original stochastic optimization problem into a deterministic cone optimization problem, which is computationally tractable. The extensive simulations have been conducted to evaluate the impact of various network parameters on the algorithm performance and further validate that the proposed DADR-TR algorithm can achieve high data delivery performance without full distribution information of the long-term data arrival.

In this paper, the sum rate of opportunistic interference alignment (OIA) is analyzed in multiple-input-multiple-output interfering broadcast channels. The alignment metric upon which users are scheduled is based on the chordal distance between certain interfering subspaces at each receiver, and the closed-form expressions for the rates of the scheduled users are derived. Furthermore, we show that for a system in which each user has $N$ receive antennas and the $j$ th base station transmits $d_{j}$ data streams, where $\sum _{j=1}^{I}d_{j}={N}+1$ and ${N}\ge 2$ , the rate for each user can be approximated by the mean of a Gumbel random variable. Further analysis reveals that if the number of users in cell $i$ scales as $\rho ^{\alpha }$ , where $\rho $ is the normalized transmit power and $\alpha \in [{0,1}]$ , then cell $i$ can achieve $\alpha d_{i}$ degrees of freedom. The simulation results confirm the validity of the theoretical analysis and the accuracy of the approximation. Thus, the sum rate analysis provided herein is an effective performance evaluation method for multi-cell OIA.

This study analyses and compares the downlink energy efficiency (EE) of user association and resource scheduling algorithms considering sleep mode of small cell base stations (SBSs) in two-tier heterogeneous cellular networks. Owing to the NP-hardness of joint user association and resource scheduling algorithms, the authors successively perform user association and resource scheduling algorithms. Firstly, in the user association algorithms, sleep mode for the SBSs without associated users is considered to improve EE, where they associate users with the macrocell base station (MBS) or SBSs according to three signal strength strategies, namely, unbiased user association, biased user association and signal to interference plus noise ratio-based user association. Then, in the resource scheduling algorithms, they schedule the MBS and SBSs resource blocks to users based on three scheduling criteria, i.e. maximum received signal strength, round robin and proportional fairness, respectively. Fairness among users is also analysed. In the simulation, numerical results for different successive user association and resource scheduling algorithms are compared. The optimum algorithms are found in the different network environments. These results are significant for the heterogeneous network operation to get high EE.

In this paper we consider a single-cell massive multiple-input-multiple-output scenario, where the number of base station (BS) antennas is larger than the number of single antenna user terminals (UTs). We study and investigate common impairment named as channel aging caused by the relative movement between BS antenna and UTs. To acquire channel state information we apply minimum-mean-square-error detector. After that we study some commonly used channel predictors. Then we apply Wiener predictor and ARMA predictor to mitigate the effects of channel aging. We provide signal-to-interference-plus-noise ratios for each channel predictor and demonstrate their overall performance. We apply matched filtering for precoding. We perform thorough analysis with both predictors and observe that Wiener predictor performs better than ARMA predictor in all aspects such as achievable rates, cumulative distribution function and with higher Doppler spreads. The calculated results are deterministic and simulations show that they are even more accurate for system dimensions practically.

Small satellite networks (SSNs) have attracted intensive research interest recently and have been regarded as an emerging architecture to accommodate the ever-increasing space data transmission demand. However, the limited number of on-board transceivers restricts the number of feasible contacts (i.e., an opportunity to transmit data over a communication link), which can be established concurrently by a satellite for data scheduling. Furthermore, limited battery space, storage space, and stochastic data arrivals can further exacerbate the difficulty of the efficient data scheduling design to well match the limited network resources and random data demands, so as to the long-term payoff. Based on the above motivation and specific characteristics of SSNs, in this paper, we extend the traditional dynamic programming algorithms and propose a finite-embedded-infinite two-level dynamic programming framework for optimal data scheduling under a stochastic data arrival SSN environment with joint consideration of contact selection, battery management, and buffer management while taking into account the impact of current decisions on the infinite future. We further formulate this stochastic data scheduling optimization problem as an infinite-horizon discrete Markov decision process (MDP) and propose a joint forward and backward induction algorithm framework to achieve the optimal solution of the infinite MDP. Simulations have been conducted to demonstrate the significant gains of the proposed algorithms in the amount of downloaded data and to evaluate the impact of various network parameters on the algorithm performance.

Through the allocation of multi-antenna time blocks to spacecrafts, the data relay satellite network (DRSN) is capable of providing data relay within their visible intervals (i.e., time windows). During the relay process, the generated hybrid tasks incorporate common tasks, emergency tasks, and temporary tasks. However, higher priority unpredicted tasks (i.e., emergency tasks and temporary tasks) unpredictably preempt antenna resources, thereby resulting in more common tasks unsuccessful to relay. It is, therefore, nontrivial to investigate the dynamic hybrid task scheduling problem with time windows for the multi-antenna DRSN to efficiently and real-timely allocate multi-antenna time blocks aiming at accommodating more unpredicted tasks and reducing the number of unsuccessful common tasks. To this end, we propose a stochastic optimization framework to maximize the time average number of hybrid tasks by jointly optimizing the scheduling periods and the antenna time block allocation. For the tractability purpose, by leveraging its unique structure, we first equivalently transform it to a scheduling period adjustment (SPA) problem, embedded with a sequence of antenna time block allocation (ATBA) problems. Then, two efficient algorithms are developed to solve the SPA and ATBA problem, respectively. Finally, simulation results demonstrate that the proposed algorithm can significantly increase the time average number of hybrid tasks.

Preload has a significant effect on the precision of machine tool spindle, and it changes transiently in different operating conditions. In this paper, the transient feature of spindle preload under multi-factor coupled effect was discussed theoretically and experimentally. By adopting the transient network method and the time-discrete approach, the integrated thermo-mechanical model of a spindle was established. The axial and radial deformations of spindle system, induced by centrifugal force, thermal, and assembly stresses, were all discussed. The effects of the various factors on the transient preload were analyzed, and results show that speed and temperature contribute the most to the transient preload, followed by the bearing deflection. Finally, experimental results indicate the correctness of the improved model.

Unmanned aerial vehicles (UAVs) have been extensively applied as aerial access points to assist the terrestrial wireless network. Despite the inherent potential, nevertheless, it still remains to explore whether the gain of UAV access points (UAPs) could be fully harvested, which is critically dependent on the factors including the flight altitude and deployment density of UAPs. In this light, we investigate the performance of a downlink UAV integrated terrestrial cellular network (UTCN) and analytically study the influence of varying UAP altitude and density on the spatial throughput (ST) of UTCN. In particular, we obtain the UAP altitude upper bound, below which more line-of-sight (LOS) connections could be provided to improve network ST. Otherwise, cross-layer interference over the LOS paths becomes dominant, which results in significant degradation of network ST. More importantly, we reveal the limitation of the application of UAPs by showing that there exists a critical UAP density, beyond which network ST would encounter a rapid decrease. To fully exploit the potential of UAPs, we further tailor a probabilistic interference avoidance scheme and study the optimization of the UAP activated probability. Remarkably, network ST could be substantially improved using the optimized activated probability, i.e., network ST could increase with the growing UAP density and converge to a positive constant instead of zero in the dense UAP regime. Therefore, the results of this paper could provide insight on the deployment and optimization of UTCN.

For angle-of-arrival (AoA)-based localization methods, the AoA estimation accuracy would notably influence the localization performance. In this letter, we evaluate the estimation accuracy of different AoAs in a multipath environment and propose a weighted AoA-based localization approach. Specifically, the AoA estimation accuracy is quantified by deriving a closed form expression of asymptotic variance of the AoA estimation error. However, in practice, the accessible information for receivers is insufficient to retrieve the variance. Accordingly, we still find an approximate but effective substitution to evaluate the AoA estimation accuracy on basis of the limited information. Aided by the above analysis, we design the weighted localization method, where each estimated AoA is weighted with corresponding factor related to the AoA estimation accuracy. Simulations show that our proposed localization method decreases the median localization error by 20% compared with the unweighted method.

The orbital angular momentum (OAM) is considered as a new degree of freedom (DOF) that could be utilized to enhance the capacity of wireless communication systems. However, it is known that uniform circular array (UCA)-based OAM in line-of-sight channels is a special case of the multiple-input multiple-output (MIMO) system. In this letter, we investigate the performance of UCA-based OAM communications in a “spatial keyhole” channel and reveal that in contrast to traditional MIMO, UCA-based OAM may provide additional DOF. In particular, the phenomenon that diffraction at the “spatial keyhole” does not change the helical phase structure of OAM modes provides a promising way to overcome the keyhole effect for traditional multi-antenna wireless communications.

Space information networks (SINs) have been designed to achieve agile acquisition, transmission, and processing of space information. Compared to traditional terrestrial communication networks comprised of mobile users and fixed resources, SINs are characterized by resource mobility, specifically in terms of resource migration, interchange, and aggregation. The resource mobility of SIN provides opportunities to deal with the resource scarcity problem. However, due to complex multi-dimensional resource mobility management, making full use of the opportunities brought by resource mobility is a daunting challenge for network performance improvement. This article provides an overview of open issues with regard to resource mobility in SINs. We first propose the concept of resource mobility and further elaborate on the opportunities it provides. Then we introduce the key research challenges of resource mobility management from three aspects. Furthermore, an optimal resource mobility utilization strategy is devised to achieve high network performance. Overall, resource mobility opens new niches for the exploitation of efficient resource utilization in SINs.

Ultra-dense heterogeneous networks, as a novel network architecture in the fifth-generation mobile communication system (5G), promise ubiquitous connectivity and smooth experience, which take advantage of multiple radio access technologies (RATs), such as WiFi, UMTS, LTE, and WiMAX. However, the dense environment of multi-RATs challenges the network selection because of the more frequent and complex decision process along with increased complexity. Introducing artificial intelligence to ultra-dense heterogeneous networks can improve the way we address network selection today, and can execute efficient and intelligent network selection. Whereas, there still exist difficulties to be noted. On one hand, the contradiction between real-time communications and time-consuming learning is exacerbated, which can result in slow convergence. On the other hand, the black-box learning mode suffers from oscillation due to the diversity of multi-RATs, which can result in arbitrary convergence. In this paper, we propose a model-driven framework with a joint off-line and on-line way, which is able to achieve fast and optimal network selection through an alliance of machine learning and game theory. Further, we implement a distributed algorithm at the user side based on the proposed framework, which can reduce the number of frequent switching, increase the possibility of gainful switching, and provide the individual service. The simulation results confirm the performance of the algorithm in accelerating convergence rate, boosting user experience, and improving resource utilization.

Signal processing on antenna arrays has recently received extensive attention in the area of angle-of-arrival (AoA)-based indoor localization. Although sufficient array elements can improve the resolution in the AoA estimation, the array orientation has not been well exploited in research into the localization performance. In this paper, we investigate the effect of array orientations on the performance of AoA-based indoor localization systems. Appropriate array orientation can efficiently reduce the uncertainty in AoA estimation, thereby improving the localization accuracy. Accordingly, we present OpArray, an accurate indoor localization system based on flexible array deployment. First, OpArray designs an array deployment scheme, which establishes the foundation for accurate AoA estimates. The deployment scheme can be easily implemented through array rotations so as to optimize array orientations at receivers. Second, OpArray incorporates two refined phase preprocessing algorithms to mitigate the impact of negative factors, which exist in the practical implementation. In addition, aided by an improved AoA estimation algorithm, OpArray can localize a target on commercial off-the-shelf Wi-Fi platforms. Our experiments in a multipath-rich indoor environment show that OpArray achieves a median localization error of 0.5 m and the 80th percentile error is 1.0 m, which outperforms the state-of-the-art localization systems.

The network function virtualization (NFV) enables the virtual network functions (VNFs) to run as software components upon a virtualization system hosted in a cloud. Recently, due to its high flexibility and elasticity in terms of network services and functions deploying, NFV has been considered as one of the key enabling techniques of the fifth-generation (5G) wireless systems. Moreover, the creation of the core network functions is the key to the implementation of 5G. Currently, there are extensive studies researching into how to deploy cooperatively VNFs over a federated cloud for multiple cloud providers (CPs) to create the 5G Core. These studies have a critical assumption that each CP participating in the cooperation is fully informed of all the information about other CPs. However, the assumption may not hold in practice. Since CPs adopt diverse virtualization technologies (e.g., XEN, KVM, or containers), the cost to create an instance of one VNF for each CP may be different. Moreover, the cost information is private and will not be shared with each other. Consequently, it is difficult for each CP to determine whether to cooperate with others and the way of cooperation. Therefore, in this paper, we utilize the Bayesian coalition formation game (BCFG) to tackle this challenging situation with the unknown information about CPs and formulate the optimal coalition that can deploy cooperatively each VNFs of 5G Core. Specifically, we address the optimal number of VNF instances to instantiate, the number of virtual resources for each instance, and the placement decision in a specific CP over a federated cloud. We propose a BCFG-based non-myopic and full negotiation approach for the creation of the virtual 5G Core network functions. Simulation results demonstrate the comprehensive performance of the proposed method in terms of convergence properties and the profits that each participating CP can obtain.

Unmanned aerial vehicles (UAVs) can be deployed as wireless relays or aerial base stations to improve network connectivity and coverage in cellular networks. UAVs can also be used to significantly enhance the performance of mobile ad-hoc networks and wireless sensor networks. In the future, UAVs are expected to become an integral part of the fifth generation wireless networks as well as key enablers of the coming massive Internet of Things. However, there are still many challenging issues in designing architectures and deployment of UAV-based networks. To address the issues, game theory has recently been adopted as an effective tool for modeling and analyzing problems in UAV-aided networks. In this paper, we survey the applications of game theory in solving various UAV-assisted networks challenges. We first provide a brief introduction to wireless communications with UAVs and then introduce basic game theory concepts and their relation to wireless networks. We further present the classification and brief introduction to the games applied to solve problems in UAV-aided networks. We then provide a comprehensive literature review on game-theoretic techniques utilized in dealing with challenges in the UAV-based wireless networks. Finally, we introduce advanced distributed schemes for interference management in large UAV-assisted communication networks. This paper aims to provide readers with an understanding of UAV-aided networks in terms of their architecture, benefits, challenges, and various game theoretical solutions applied to these communications networks.

The multi-commodity flow problem plays an important role in network optimization, routing, and service scheduling. With the network partitioning and the intermittent connectivity, the commodity flows in delay tolerant networks (DTNs) are time-dependent, which is very different from that over the static networks. As an NP-hard problem, existing works can only obtain sub-optimal results on maximizing the multi-commodity flow of dynamic networks. To overcome these bottlenecks, in this paper, we propose a graph-based algorithm to solve the maximum two-commodity flow problem over the DTNs. Through analyzing the relationship between the two commodities, we propose a maximum two-commodity flow theorem to simplify the coupling two-commodity flow problem as the two single-commodity flow ones. Then, with the help of the storage time aggregated graph (STAG) (a DTN model with less memory), we construct a pair of flow graphs to describe the reduced two single-commodity flows (addition flow and subtraction flow), and design the corresponding flow calculation methods. Moreover, we design a STAG-based dynamic combined flow algorithm to maximize the two-commodity flow. Finally, the computational complexity of the proposed algorithm is analyzed, and its efficacy has also been demonstrated through an illustrative example and numerical simulations.

In this paper, we investigate the transmitter cooperation based topological interference management (TIM) scheme for MIMO Interference Channels (IC), where there is no channel state information beyond only the topology information at transmitters and receivers to cancel the interference. Specifically, for two different scenarios, i.e., there exists a Hamiltonian cycle or a perfect matching in the alignment-feasible graph, we propose different transmitter cooperation based TIM schemes by jointly designing the precoding and decoding matrices and present one theorem to characterize the achievable degrees of freedom (DoF) of the proposed TIM schemes. Moreover, the numeric results show that the DoF of the proposed scheme can be significantly higher than that by the TIM scheme without transmitter cooperation.

System modeling and performance analysis are nontrivial issues in satellite networks. Mainstream methods model the acquire-store-forward process of traffic as a queuing process, where the transmitter and buffer work as the server and queue, respectively. However, the intermittent connectivity of satellite links hampers the direct employment of traditional vacation policies, because the inactive period of link is independent of the buffer state. Hence, we propose a time-limited M/G/1 model for the intermittent transmission among satellites. Furthermore, we derive the steady-state expressions and lower bound of performance parameters by transient analysis. Finally, simulation results verify the effectiveness of our method.

Network densification is proved to be the most efficient method to expand network capacity over the past few decades. Nevertheless, can network densification always boost network performance? To shed light on this, we explore the pros and cons of network densification in $k$ -dimension downlink small cell networks by analyzing transmission success probability (SP) and network spatial throughput (ST). Remarkably, different from long-distance propagation in sparse networks, short-distance propagation of distinctive features dominates as well under dense deployment of small cells. On this account, we apply a multislope bounded pathloss model (MBPM) to characterize signal attenuation within long and short propagation distance. Under MBPM, it is surprisingly found that network densification would enhance SP and ST only when small cell base station density $\lambda \left(\mathrm{SBS}/\mathrm{m}^{k}\right)$ is smaller than some critical densities. Beyond the critical densities, both SP and ST are proved to exponentially diminish with $\lambda$ and even approach zero when $\lambda$ is sufficiently large. In other words, ultradense deployment of small cells indeed deteriorates network performance. Besides, the numerically derived critical densities are helpful for the practical deployment of small cell networks.

In satellite networks, information can be transmitted either directly by inter-satellite links or the movement of satellites carrying. Consequently, how to quantify network capacity, considering both the carrying and transmission capability of satellites is crucial to the deployment of satellite networks. In this paper, we define the capacity of satellite networks consisting of both, and propose a strategy to exploit the mobile carrying of satellites under the constraint of service requirements. Then, we reveal the theoretical relationship between satellite carrying and the network capacity. The theoretical analysis and simulated results show that 1) satellite carrying can improve the network capacity when the service delay constraints could be released; 2) The capacity gain from satellite carrying is influenced by network parameters, such as orbital altitude, number of satellites, and storage capacity.

A large amount of protein impurity severely restricts the application of polysaccharides of Ruditapes philippinarum (PRP) in food and medicine. Moreover, the traditional Sevag deproteinization method always involves organic reagents. The purpose of this paper was to develop an effective, green and mild deproteinization method from PRP by attapulgite-based silk fibroin composite aerogel (ASA). Firstly, ASA was synthesized and applied to remove protein from PRP. Secondly, the deproteinization parameters were optimized with selectivity coefficient as index as follows: dose of ASA 1% and pH 7.0. Under these conditions, deproteinization ratio (Dr%), polysaccharide recovery ratio (Rr%) and selectivity coefficient (Kc) reached 79.44 ± 1.87%, 95.81 ± 2.95% and 18.95 ± 1.55, respectively. Next, the feasibility of ASA method was evaluated. As a result, ASA method not only achieved higher deproteinization efficiency in less time compared with Sevag method, but also retained structure and antioxidant activity of polysaccharides. ASA was also proven with recycling ability and could be reused more than five times. Furthermore, it was found that protein adsorption on ASA was better fitted by pseudo second-order kinetic and Freundlich model. Taking together, the data implied that ASA method would be promising of deproteinization from PRP suitable for polysaccharides processing.

In cache-enabled wireless networks (CWN), the unbalanced traffic distribution due to the node's sociality may lead to local congestion, which significantly degrades system throughput. Especially, nodes prefer to share content with those that have social relationships with them, which may result in heavy traffic load in the nodes with great social relationships. Therefore, it is crucial to capture the interplay among sociality, content caching and traffic distribution. In this paper, we design a caching strategy through jointly considering sociality and load balance to maximize the throughput capacity. To this end, efficient betweenness (EB) is adopted to quantify the traffic distribution, where EB is the number of content delivery paths through a node. Aided by EB, the impacts of key system parameters including sociality and caching strategy on throughput capacity are elaborated. According to the critical condition of the steady state in CWN, we formulate an optimization problem aiming to maximize throughput capacity. Due to the non-convexity of the initial problem, we propose an effective heuristic algorithm to solve it, which can balance traffic load according to the node's sociality and transmission capacity. Simulation results show that the proposed algorithm can increase the throughput capacity by 35.7% against benchmark approaches.

In this work, we investigate the location error of a fingerprint-based indoor system with the application of hybrid received signal strength (RSS) and channel state information (CSI) fingerprints. It manifests that exploiting correlation between RSS and CSI could effectively reduce location error. On this basis, we propose a hybrid RSS/CSI localization algorithm (HRCL), which is designed based on the deep learning. The HRCL fully exploits quick construction of fingerprint database with the coarse-grained RSS and rich multipath information of the fine-grained CSI. The RSS and CSI with high correlation are selected to construct fingerprint database, aiming to improve localization accuracy. Moreover, the deep neural network is trained for location estimation. Especially, experimental results validate that the location error of HRCL can be reduced by 64.4%, compared with the existing localization method. Moreover, the location error of HRCL can be reduced by 29.1 %, compared with HRCL without RSS/CSI selection by correlation coefficient.

Memory is the foundation of intelligence. KID model covers multiple human cognitive processes such as learning and memory. This paper refines its memory process, especially focusing on associative memory of long-term memory. An associative memory framework with novel neural network storage architectures is presented to simulate human-like associative memory ability for machine intelligence. The presented framework involves an associative memory repository and two abstract functions, Assimilation() for knowledge encoding and storage, and Instantiation() for knowledge recall and application. The proposed novel storage architecture has two storage structures which both includes three kinds of layers: input layer, competitive layer and associative memory layer. Its design integrates multiple associative memory related neuroscience theories. It is characterized by chaotic feature, self-organization, self-adjustment, self-growth and associative recall. With the encapsulation of presented associative memory framework and novel storage architecture, the KID model can incorporate associative memory and be applied to various fields like intelligent information and knowledge management systems, personized products development and robotic intelligence.

Fixed-wing unmanned aerial vehicles (UAVs) are of great potential to serve as aerial access points (APs) owing to better aerodynamic performance and longer flight endurance. However, the inherent hovering feature of fixed-wing UAVs may result in discontinuity of connections and frequent handover of ground users (GUs). In this work, we model and evaluate the performance of a fixed-wing UAV network, where UAV APs provide coverage to GUs with millimeter wave backhaul. Firstly, it reveals that network spatial throughput (ST) is independent of the hover radius under real-time closest-UAV association, while linearly decreases with the hover radius if GUs are associated with the UAVs, whose hover center is the closest. Secondly, network ST is shown to be greatly degraded with the over-deployment of UAV APs due to the growing air-to-ground interference under excessive overlap of UAV cells. Finally, aiming to alleviate the interference, a projection area equivalence (PAE) rule is designed to tune the UAV beamwidth. Especially, network ST can be sustainably increased with growing UAV density and independent of UAV flight altitude if UAV beamwidth inversely grows with the square of UAV density under PAE.

As the sixth generation (6G) network is under research, and one important issue is the aerial access network and terrestrial-space integration. The Internet of Remote Things (IoRT) sensors can access the unmanned aerial vehicles (UAVs) in the air, and low Earth orbit (LEO) satellite networks in the space help to provide lower transmission delay for delay-sensitive IoRT data. Therefore, in this article, we consider the LEO satellite-assisted UAV data collection for the IoRT sensors. Specifically, a UAV collects the data from the IoRT sensors, then two transmission modes for the collected data back to Earth: 1) the delay-tolerant data leveraging the carry-store mode of UAVs to Earth and 2) the delay-sensitive data utilizing the UAV-satellite network transmission to Earth. Considering the limited payloads of UAVs, we focus on minimizing the total energy cost (trajectory and transmission) of UAVs while satisfying the IoRT demands. Due to the intractability of direct solution, we deal with the problem using the Dantzig-Wolfe decomposition and design the column generation-based algorithms to efficiently solve the problem. Moreover, we present a heuristic algorithm for the subproblem to further reduce the complexity of large-scale networks. Finally, numerical results verify the efficiency of the proposed algorithms and the advantage of LEO satellite-assisted UAV trajectory design combined with the data transmission is also analyzed. This work was supported in part by the Natural Science Foundation of China under Grant U19B2025, Grant 61725103, Grant 61701363, and Grant 61931005; and in part by the Doctoral Students' Short-Term Study Abroad Scholarship Fund of Xidian University.

We investigate power optimization for throughput maximization in massive multiple-input multiple-output (MIMO) downlink networks with hybrid energy harvesting transmitter. In particular, a finite-capacity energy harvester is employed at the base station in conjunction with the fixed power grid for system energy supply. With availability of the noncausal knowledge about the channel state information (CSI) and the energy harvesting process, the throughput maximization is first formulated as a typical convex optimization problem, subject to a set of battery storage and power constraints. Especifically, this optimization is also featured by taking into account the circuit energy consumption that can not be neglected in massive MIMO systems with a large quality of radio frequency chains. Mathematically, Lagrange dual decomposition technique can be used to derive the optimal power variables. However, due to its offline calculation characteristics, the proposed approach is hard to implement in practice and can only serve as a performance upper bound. To facilitate real world application, we then heuristically present an online scheme that merely utilizes the causal information of the CSI and the statistics of the energy harvesting process, without significantly degrading the performance. Finally, simulation results are shown to verify the effectiveness of the proposed algorithms.

In this paper, we investigate the limitation of multi-user multiple-input multiple-output (MIMO) in ultra-dense networks (UDNs) and investigate how to overcome the limitation by designing efficient interference management strategies. Specifically, it is shown that the area spectral efficiency (ASE), an indicator to network capacity, would approach zero with over-deployed base stations (BSs) even when multi-user MIMO is applied. Worse still, it manifests that the multi-user gain of MIMO cannot be harvested in UDN due to the overwhelming interference. In particular, the maximal ASE is shown to be degraded by increasing the number of served users in each cell. To alleviate the bottleneck brought by interference, we have designed and optimized two simple but efficient BS activation policies. It is shown that the application of the optimized BS activation policy could improve network capacity scaling law and boost the potential of multi-user MIMO in UDN. Remarkably, the ASE is shown to increase with BS density $\lambda $ even when $\lambda $ and user density are sufficiently large. Moreover, the maximal ASE in the $\lambda \rightarrow \infty $ regime is shown to increase with the number of served users in each cell, which contradicts with the all-BS-on case.

Orbital angular momentum (OAM) multiplexing is considered to have the potential of further improving the spectral efficiency of communication systems. However, a common uniform circular array (UCA)-based multi-mode OAM communication system using discrete Fourier transform (DFT) and inverse DFT (IDFT) has high peak-to-average power ratio (PAPR), which reduces the radio frequency power amplifier (PA) efficiency and degrades system performance. In this paper, a constant envelope OAM (CE-OAM) system is proposed to achieve the minimum signal PAPR to maximize the PA efficiency. The transmitter using a phase modulator converts the high PAPR signal into constant envelope signal with $0$dB PAPR, and the receiver performs inverse transform after OAM symbol detection, followed by the conventional OAM demodulation. Simulation results validate the feasibility of the proposed CE-OAM system and shows that CE-OAM system with appropriate modulation coefficient outperforms conventional OAM system in both PAPR and bit error rate (BER) performances.

Constructing energy-efficient data center networks (DCNs) is becoming increasingly significant. In the hybrid DCNs with both wired and wireless links, reconfigurable wireless links can effectively reduce the routing path length and the usage of the switch, thereby greatly reduce the energy consumption DCNs. In this paper, we propose an energy-efficient hybrid flow routing and antenna scheduling scheme for tree-based hybrid DCNs. Firstly, the original problem of hybrid routing and scheduling is decomposed into two subproblems by taking advantage of the wireless energy-saving features. Then, a novel weight-relaxing-rounding algorithm is developed to solve the first subproblem. Specifically, the topology characteristics of the tree-based DCNs is used to perform weight transformation and link remapping, and then to find energy-efficient wired subnet. After that, the relax-and-rounding technology is adopted to obtain energy-efficient wireless links scheduling solution. Finally, the numerical results show that the proposed scheme can achieve a near optimal solution when the network scale is small. As the network scale increases, the proposed scheme is still able to save more energy than the existing algorithm.

With the growing deployment of small cell base stations (BSs), the impact of backhaul congestion on the performance of small cell networks becomes dominant. To capture this effect, we present a conjoint framework integrating backhaul architecture with the access network. In particular, we evaluate the performance of ultra-dense networks (UDNs) in terms of network spatial throughput (ST), supposing that backhaul is conveyed to BSs through millimeter wave (mmWave) links by gateways. Notably, in contrast to diminishing to zero in previous work, network ST is shown to converge into a saturation under the given gateway density since the limited backhaul capability of gateways would stabilize the interference distribution of access network. Moreover, despite the benefit of enhancing backhaul capacity, over-deployed gateways would result in the potential intercell interference, which degrades network ST of UDN. On this account, we further study the optimization of gateway density to balance the tradeoff between enhancing backhaul capacity and mitigating intercell interference. It indicates that the application of mmWave beamforming at gateways leads to an increase in optimal gateway density, under which network ST could be further improved.