The interplay between artificial intelligence (AI) and fog radio access networks (F-RANs) is investigated in this work from two perspectives: how F-RANs enable hierarchical AI to be deployed in wireless networks and how AI makes F-RANs smarter to better serve mobile devices. Due to the heterogeneity of processing capability, the cloud, fog, and device layers in F-RANs provide hierarchical intelligence via centralized, distributed, and federated learning. In addition, cross-layer learning is also introduced to further reduce the demand for the memory size of the mobile devices. On the other hand, AI provides F-RANs with technologies and methods to deal with massive data and make smarter decisions. Specifically, machine learning tools such as deep neural networks are introduced for data processing, while reinforcement learning (RL) algorithms are adopted for network optimization and decisions. Then, two examples of AI-based applications in F-RANs, i.e., health monitoring and intelligent transportation systems, are presented, followed by a case study of an RL-based caching application in the presence of spatio-temporal unknown content popularity to showcase the potential of applying AI to F-RANs.

Network slicing based fog radio access network (F-RAN) has emerged as a promising architecture to support various novel applications in 5G-and-beyond wireless networks. However, the co-existence of multiple network slices in F-RANs may lead to significant performance degradation due to the resource competitions among different network slices. In this paper, the downlink F-RANs with a hotspot slice and an Internet of Things (IoT) slice are considered, in which the user equipments (UEs) of different slices share the same spectrum. A novel joint resource allocation and admission control scheme is developed to maximize the number of UEs in the hotspot slice that can be supported with desired quality-of-service, while satisfying the interference constraint of the UEs in the IoT slice. Specifically, the admission control and beamforming vector optimization are performed in the hotspot slice to maximize the number of admitted UEs, while the joint sub-channel and power allocation is performed in the IoT slice to maximize the capability of the UEs in the IoT slice tolerating the interference from the hotspot slice. Numerical results show that our proposed scheme can effectively boost the number of UEs in the hotspot slice compared to the existing baselines.

Intelligent edge computing carries out edge devices of the Internet of things (IoT) for data collection, calculation and intelligent analysis, so as to proceed data analysis nearby and make feedback timely. Because of the mobility of mobile equipments (MEs), if MEs move among the reach of the small cell networks (SCNs), the offloaded tasks cannot be returned to MEs successfully. As a result, migration incurs additional costs. In this paper, joint task offloading and migration schemes in mobility-aware Mobile Edge Computing (MEC) network based on Reinforcement Learning (RL) are proposed to obtain the maximum system revenue. Firstly, the joint optimization problems of maximizing the total revenue of MEs are put forward, in view of the mobility-aware MEs. Secondly, considering time-varying computation tasks and resource conditions, the mixed integer non-linear programming (MINLP) problem is described as a Markov Decision Process (MDP). Then we propose a novel reinforcement learning-based optimization framework to work out the problem, instead traditional methods. Finally, it is shown that the proposed schemes can obviously raise the total revenue of MEs by giving simulation results.

In this manuscript, a cooperative non-orthogonal multiple access based intelligent mobile edge computing (NOMA-MEC) communication system is constructed in detail. The nearby user is viewed as a decoding and forwarding relay, which can assist a distant user in offloading tasks to the intelligent MEC server. Then, the closed-form expressions of offloading outage probability for a pair of users are derived in detail to evaluate the performance of the cooperative NOMA-MEC system. Furthermore, the approximate expressions of offloading outage probability are provided in the high signal-to-noise ratio region. Based on the asymptotic analyses, the diversity order of distant user and nearby user is n+m+1 and n+1, respectively. The system throughput and energy efficiency of cooperative NOMA-MEC are analyzed in delay-limited transmission mode. Numerical results show that 1) Cooperative NOMA-MEC is better than orthogonal multiple access (OMA) in terms of offload performance; 2) The offload performance of cooperative NOMA-MEC system improves as the number of transmission task decreases; and 3) Cooperative NOMA-MEC performs better than OMA in energy efficiency.

As an emergent-architecture, mobile edge computing shifts cloud service to the edge of networks. It can satisfy several desirable characteristics for IoT systems. To reduce communication pressure from IoT devices, data aggregation is a good candidate. However, data processing in MEC may suffer from many challenges, such as unverifiability of aggregated data, privacy-violation and fault-tolerance. To address these challenges, we propose PVF-DA: privacy-preserving, verifiable and fault-tolerant data aggregation in MEC based on aggregator-oblivious encryption and zero-knowledge-proof. The proposed scheme can not only provide privacy protection of the reported data, but also resist the collusion between MEC server and corrupted IoT devices. Furthermore, the proposed scheme has two outstanding features: verifiability and strong fault-tolerance. Verifiability can make IoT device to verify whether the reported sensing data is correctly aggregated. Strong fault-tolerance makes the aggregator to compute an aggregate even if one or several IoTs fail to report their data. Finally, the detailed security proofs are shown that the proposed scheme can achieve security and privacy-preservation properties in MEC.

The Poisson point process (PPP) has been widely used in wireless network modeling and performance analysis due to the independence between its nodes. Therefore, it may not be a suitable model for many of the exclusive networks between the nodes. This paper analyzes the energy efficiency (EE) and optimizes the two-tier heterogeneous cellular networks (HetNets). Considering the mutual exclusion between macro base stations (MBSs) distribution, the deployment of MBSs is modeled by the Matérn hard-core point process (MHCPP), and the deployment of pico base stations (PBSs) is modeled by the PPP. We adopt a simple approximation method to study the signal to interference ratio (SIR) distribution in two-tier MHCPP-PPP networks and then derive the coverage probabilities, the average data rates and the energy efficiency of HetNets. Finally, an optimization algorithm is proposed to improve the EE of HetNets by controlling the transmit power of PBSs. The simulation results show that the EE of a system can be effectively improved by selecting the appropriate transmit power for the PBSs. In addition, two-tier MHCPP-PPP HetNets have higher energy efficiency than two-tier PPP-PPP HetNets.

In this paper, we investigate the downlink performance of cell-free massive multi-input multi-output non-orthogonal multiple access (CF-mMIMO-NOMA) system with conjugate beamforming precoder and compare against the orthogonal multiple access (OMA) counterpart. A novel achievable closed-form spectral efficiency (SE) expression is derived, which characterizes the effects of the channel estimation error, pilot contamination, imperfect successive interference cancellation (SIC) operation, and power optimization technique. Then, motivated by the closed-form result, a sum-SE maximization algorithm with the sequential convex approximation (SCA) is proposed, subject to each AP power constraint and SIC power constraint. Numerical experiments indicate that the proposed sum-SE maximization algorithms have a fast converge rate, within about five iterations. In addition, compared with the full power control (FPC) scheme, our algorithms can significantly improve the achievable sum-SE. Moreover, NOMA outperforms OMA in many respects in the presence of the proposed algorithms.

This paper analyses of the outage probability and the achievable rate of massive multi-input-multi-output (MIMO) systems, in which the base station (BS) is equipped with digital-to-analog-converters (DACs) of mixed-level resolution. And the matched-filter (MF) precoding is used on the BS. Closed-form expressions are derived by the distribution of user-interference power and other statistical properties in the signal-to-interference-plus-noise-ratio. Then, the combination of mixed-DACs resolution profile is chosen about outage probability and achievable rate with the BS energy consumption. And the resolution configurations between the outage probability and the achievable rate and the BS energy consumption are given. Meanwhile, Effects of related parameters and channel errors are analysed about outage probability and achievable rate. The numerical results show that the correctness of the formula derivations. As the number of users increases the system's achievable rate increases and the outage probability decreases. The selected resolution configuration system has better comprehensive performance.

Named Data Network (NDN) has caused wide concerns in VANET community because NDN uses a content-centric mechanism that naming content rather than the host. However, integrating NDN into VANET (NDN-VANET) also faces several challenges including consumer/provider mobile, broadcast storm problem and so on. In this paper, we propose a Bayesian-based Receiver Forwarding Decision (BRFD) scheme to mitigate the broadcast storm problem incurred by interest packets in NDN-VANET. In the BRFD, vehicles received an interest packet are required to make forwarding decisions based on Bayesian decision theory according to current network conditions obtained by neighbor interaction. However, the receiver-forwarding decision in BRFD can also cause a conflict issue because multiple vehicles forward copies of the same packet at the same time. So a back-off mechanism is introduced in BRFD. Experimental results show that the BRFD algorithm has better performance in several aspects in contrast to probability-based forwarding scheme and “bread crumb” routing.

Nowadays, most positioning systems carry out locational calculation based on the accurate location information of some devices in the network. However there is a deviation in the locational information of the part of the device, we need to reduce it in order to obtain higher positioning accuracy. In this paper, we proposed a new centralized D2D (Device-to-Device) co-location algorithm. This algorithm uses DBSACN (Density-Based Spatial Clustering of Applications with Noise) clustering to reduce the deviation of device location information. Numerical results show that the positioning accuracy of the centralized D2D co-localization algorithm is improved by 62.7% compared with the SPAWN algorithm, which positioning performance superior to the traditional co-localization algorithm.

In this paper, a novel nonlinear companding transform (NCT) is proposed to reduce the Peak-to-Average Power Ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals. The companding function is designed based on continuously differentiable reshaping of the probability density function (PDF) of signal amplitudes. The original PDF is cut off for PAPR reduction, and lower and medium segments of original PDF are scaled and linearized respectively, for maintaining power and cumulative distribution constraints. The linearized segment is set to be the tangent of the scaled version at the inflexion point, so as to reduce the out-of-band (OOB) radiation as much as possible. Parameters of the proposed scheme are solved under joint constraints of constant power and unity cumulative distribution. A new receiving method is also proposed to improve the bit error rate (BER) performance of OFDM systems. Simulation results indicate the proposed scheme can achieve better OOB radiation and BER performance at same PAPR levels, compared with existing similar companding algorithms.

A joint beamforming algorithm is proposed for intelligent reflecting surface (IRS) aided wireless multiple-input multiple-output (MIMO) communication using statistical channel state information (CSI). The beamforming is done by alternatively optimizing the IRS reflecting coefficients and the covariance matrix of the transmit symbol vector, such that the ergodic rate of the system is maximized. The algorithm utilizes only the second order momentum of the random channel matrices and does assume any specific channel distribution, leading to a general framework for ergodic rate evaluation. A practical channel correlation model is configured to validate the performance gain. It is found that the rate can be enlarged by the joint optimization algorithm, however, the gain over that of randomly deployed reflecting coefficients depends highly on the relative correlation distance of the IRS elements and the spatial position of the IRS. In particular, the results suggest that IRS should be placed in the vicinity of either the transmitter or the receiver. Placing IRS far away from those positions is non-beneficial.

The global Internet is composed of tens of thousands of autonomous system networks that are interconnected through a border gateway protocol. The analysis of changes in the interconnected relationships of the global Internet network is essential for studying the evolutionary trend of the global Internet. In addition, this analysis can also contribute toward the research on China's Internet development gaps. This article draws on the global Internet network status map for 21 years, starting from 1998 to 2019, based on inter-domain routing data sets of international third parties and China Academy of Information and Communications Technology. Moreover, the article also conducts a big data analysis on the relationship between global Internet network changes. The research results show that the global Internet network interconnection has increased, by nearly 60 times in the past 21 years. Peer-to-peer interconnection has gradually surpassed transit interconnection as the main mode of global interconnection. Furthermore, large Internet companies are playing an increasingly important role in global interconnection. Simultaneously, the results indicate the gradual movement of China's Internet base toward the global core and the continuous improvements of the global network's status.

Fresh status updates are vital to the efficient operation of network monitoring and real-time control applications. In this paper, we consider a mobile edge computing (MEC)-assisted status update system, where smart devices extract valuable status updates from sensed data to achieve timely awareness of the surroundings by exploiting computational resources at the device and edge server. To quantify the freshness of status updates obtained by executing computation tasks, we employ the concept of age of information (AoI) to characterize the timeliness of status updates. To cope with the limited energy at devices, we investigate a joint task generation and computation offloading scheme under a given energy budget for minimizing the age of obtained status updates. The age minimization problem is modeled as a constrained Markov decision process (CMDP). To obtain the optimal policy, we derive the structural properties of the optimal deterministic policy and propose a light-weight structure-based status update algorithm in the case of known channel statistics. Moreover, we consider a more realistic scenario without prior knowledge of channel statistics, and propose a Q-learning-based status update algorithm to make online decisions. Simulation results show that the performance of our proposed algorithms is competitive when compared with existing schemes.

To overcome the limitations of conventional speech enhancement methods, such as inaccurate voice activity detector (VAD) and noise estimation, a novel speech enhancement algorithm based on the approximate message passing (AMP) is adopted. AMP exploits the difference between speech and noise sparsity to remove or mute the noise from the corrupted speech. The AMP algorithm is adopted to reconstruct the clean speech efficiently for speech enhancement. More specifically, the prior probability distribution of speech sparsity coefficient is characterized by Gaussian-model, and the hyper-parameters of the prior model are excellently learned by expectation maximization (EM) algorithm. We utilize the k-nearest neighbor (k-NN) algorithm to learn the sparsity with the fact that the speech coefficients between adjacent frames are correlated. In addition, computational simulations are used to validate the proposed algorithm, which achieves better speech enhancement performance than other four baseline methods-Wiener filtering, subspace pursuit (SP), distributed sparsity adaptive matching pursuit (DSAMP), and expectation-maximization Gaussian-model approximate message passing (EM-GAMP) under different compression ratios and a wide range of signal to noise ratios (SNRs).

Integrating Multi-access Edge Computing (MEC) in Low Earth Orbit (LEO) network is an important way to provide globally seamless low-delay service. In this paper, we consider the scenario that MEC platforms with computation and storage resource are deployed on LEO satellites, which is called “LEO-MEC”. Service request dispatching decision is very important for resource utilization of the whole LEO-MEC system and QoE of MEC users. Another important problem is service placement that is closely coupled with request dispatching. This paper models the joint service request dispatching and service placement problem as an optimization problem, which is a Mixed Integer Linear Programming (MILP). Our proposed mechanism solves this problem and uses the solved decision variables to dispatch requests and place services. Simulation results show that our proposed mechanism can achieve better performance in terms of ratio of served users and average hop count compared with baseline mechanism.

Recently, due to the deployment flexibility of unmanned aerial vehicles (UAVs), UAV-assisted mobile relay communication system has been widely used in the maritime communication. However, the performance of UAV-assisted mobile relay communication system is limited by the capacity of wireless backhaul link between base station and UAV. In this paper, we consider a caching UAV-assisted decode-and-forward relay communication system in a downlink maritime communication. For the general case with multiple users, the optimal placement of UAV is obtained by solving the average achievable rate maximization problem through the one-dimensional linear search. For a special case with single user, we derive a semi closed-form expression of the optimal placement of UAV. Simulation results confirm the accuracy of analytical results and show that the optimal placement of UAV and the average achievable rate significantly depend on the cache capacity at UAV. We also show the difference between the performances of the air-to-ground model and the air-to-sea model.

In order to solve the problems of data sharing security and policy conflict in multicloud storage systems (MCSS), this work designs an attribute mapping mechanism that extends ciphertext policy attribute-based encryption (CP-ABE), and proposes a multi-authority CP-ABE access control model that satisfies the need for multicloud storage access control. The mapping mechanism mainly involves the tree structure of CP-ABE and provides support for the types of attribute values. The framework and workflow of the model are described in detail. The effectiveness of the model is verified by building a simple prototype system, and the performance of the prototype system is analyzed. The results suggest that the proposed model is of theoretical and practical significance for access control research in MCSS. The CP-ABE has better performance in terms of computation time overhead than other models.

NIST (National Institute of Standards and Technology) statistical test recognized as the most authoritative is widely used in verifying the randomness of binary sequences. The Non-overlapping Template Matching Test as the 7^th test of the NIST Test Suit is remarkably time consuming and the slow performance is one of the major hurdles in the testing process. In this paper, we present an efficient bit-parallel matching algorithm and segmented scan-based strategy for execution on Graphics Processing Unit (GPU) using NVIDIA Compute Unified Device Architecture (CUDA). Experimental results show the significant performance improvement of the parallelized Non-overlapping Template Matching Test, the running speed is 483 times faster than the original NIST implementation without attenuating the test result accuracy.

Objectdetection could be recognized as an essential part of the research to scenarios such as automatic driving and pedestrian detection, etc. Among multiple types of target objects, the identification of small-scale objects faces significant challenges. We would introduce a new feature pyramid framework called Dual Attention based Feature Pyramid Network (DAFPN), which is designed to avoid predicament about multi-scale object recognition. In DAFPN, the attention mechanism is introduced by calculating the top-down pathway and lateral pathway, where the spatial attention, as well as channel attention, would participate, respectively, such that the pyramidal feature maps can be generated with enhanced spatial and channel interdependencies, which bring more semantical information for the feature pyramid. Using the COCO data set, which consists of a considerable quantity of small-scale objects, the experiments are implemented. The analysis results verify the optimized performance of DAFPN compared with the original Feature Pyramid Network (FPN) specifically for the identification on a small scale. The proposed DAFPN is promising for object detection in an era full of intelligent machines that need to detect multi-scale objects.