Digital Communications and Networks最新文献

In this paper, we analyze a hybrid Heterogeneous Cellular Network (HCNet) framework by deploying millimeter Wave (mmWave) small cells with coexisting traditional sub-6GHz macro cells to achieve improved coverage and high data rate. We consider randomly-deployed macro base stations throughout the network whereas mmWave Small Base Stations (SBSs) are deployed in the areas with high User Equipment (UE) density. Such user centric deployment of mmWave SBSs inevitably incurs correlation between UE and SBSs. For a realistic scenario where the UEs are distributed according to Poisson cluster process and directional beamforming with line-of-sight and non-line-of-sight transmissions is adopted for mmWave communication. By using tools from stochastic geometry, we develop an analytical framework to analyze various performance metrics in the downlink hybrid HCNets under biased received power association. For UE clustering we considered Thomas cluster process and derive expressions for the association probability, coverage probability, area spectral efficiency, and energy efficiency. We also provide Monte Carlo simulation results to validate the accuracy of the derived expressions. Furthermore, we analyze the impact of mmWave operating frequency, antenna gain, small cell biasing, and BSs density to get useful engineering insights into the performance of hybrid mmWave HCNets. Our results show that network performance is significantly improved by deploying millimeter wave SBS instead of microwave BS in hot spots.

Hybrid precoding is considered as a promising low-cost technique for millimeter wave (mm-wave) massive Multi-Input Multi-Output (MIMO) systems. In this work, referring to the time-varying propagation circumstances, with semi-supervised Incremental Learning (IL), we propose an online hybrid beamforming scheme. Firstly, given the constraint of constant modulus on analog beamformer and combiner, we propose a new broad-network-based structure for the design model of hybrid beamforming. Compared with the existing network structure, the proposed network structure can achieve better transmission performance and lower complexity. Moreover, to enhance the efficiency of IL further, by combining the semi-supervised graph with IL, we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning, where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk. Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update, all transmissions, even the ones during the model update, would make contributions to model update in the proposed method. During the model update, the amount of unlabelled transmissions is very large and they also carry some information, the prediction performance can be enhanced to some extent by these unlabelled channel data. Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach. Besides, we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.

The advent of Network Function Virtualization (NFV) and Service Function Chains (SFCs) unleashes the power of dynamic creation of network services using Virtual Network Functions (VNFs). This is of great interest to network operators since poor service quality and resource wastage can potentially hurt their revenue in the long term. However, the study shows with a set of test-bed experiments that packet loss at certain positions (i.e., different VNFs) in an SFC can cause various degrees of resource wastage and performance degradation because of repeated upstream processing and transmission of retransmitted packets.

To overcome this challenge, this study focuses on resource scheduling and deployment of SFCs while considering packet loss positions. This study developed a novel SFC packet dropping cost model and formulated an SFC scheduling problem that aims to minimize overall packet dropping cost as a Mixed-Integer Linear Programming (MILP) and proved that it is NP-hard. In this study, Palos is proposed as an efficient scheme in exploiting the functional characteristics of VNFs and their positions in SFCs for scheduling resources and deployment to optimize packet dropping cost. Extensive experiment results show that Palos can achieve up to 42.73% improvement on packet dropping cost and up to 33.03% reduction on average SFC latency when compared with two other state-of-the-art schemes.

Nowadays, Internet of Things (IoT) is widely deployed and brings great opportunities to change people's daily life. To realize more effective human-computer interaction in the IoT applications, the Question Answering (QA) systems implanted in the IoT services are supposed to improve the ability to understand natural language. Therefore, the distributed representation of words, which contains more semantic or syntactic information, has been playing a more and more important role in the QA systems. However, learning high-quality distributed word vectors requires lots of storage and computing resources, hence it cannot be deployed on the resource-constrained IoT devices. It is a good choice to outsource the data and computation to the cloud servers. Nevertheless, it could cause privacy risks to directly upload private data to the untrusted cloud. Therefore, realizing the word vector learning process over untrusted cloud servers without privacy leakage is an urgent and challenging task. In this paper, we present a novel efficient word vector learning scheme over encrypted data. We first design a series of arithmetic computation protocols. Then we use two non-colluding cloud servers to implement high-quality word vectors learning over encrypted data. The proposed scheme allows us to perform training word vectors on the remote cloud servers while protecting privacy. Security analysis and experiments over real data sets demonstrate that our scheme is more secure and efficient than existing privacy-preserving word vector learning schemes.

When the ground communication base stations in the target area are severely destroyed, the deployment of Unmanned Aerial Vehicle (UAV) ad hoc networks can provide people with temporary communication services. Therefore, it is necessary to design a multi-UAVs cooperative control strategy to achieve better communication coverage and lower energy consumption. In this paper, we propose a multi-UAVs coverage model based on Adaptive Virtual Force-directed Particle Swarm Optimization (AVF-PSO) strategy. In particular, we first introduce the gravity model into the traditional Particle Swarm Optimization (PSO) algorithm so as to increase the probability of full coverage. Then, the energy consumption is included in the calculation of the fitness function so that maximum coverage and energy consumption can be balanced. Finally, in order to reduce the communication interference between UAVs, we design an adaptive lift control strategy based on the repulsion model to reduce the repeated coverage of multi-UAVs. Experimental results show that the proposed coverage strategy based on gravity model outperforms the existing state-of-the-art approaches. For example, in the target area of any size, the coverage rate and the repeated coverage rate of the proposed multi-UAVs scheduling are improved by 6.9–29.1%, and 2.0–56.1%, respectively. Moreover, the proposed scheduling algorithm is high adaptable to diverse execution environments.© 2022 Published by Elsevier Ltd.

The Internet of Things (IoT) has taken the interconnected world by storm. Due to their immense applicability, IoT devices are being scaled at exponential proportions worldwide. But, very little focus has been given to securing such devices. As these devices are constrained in numerous aspects, it leaves network designers and administrators with no choice but to deploy them with minimal or no security at all. We have seen distributed denial-of-service attacks being raised using such devices during the infamous Mirai botnet attack in 2016. Therefore we propose a lightweight authentication protocol to provide proper access to such devices. We have considered several aspects while designing our authentication protocol, such as scalability, movement, user registration, device registration, etc. To define the architecture we used a three-layered model consisting of cloud, fog, and edge devices. We have also proposed several pre-existing cipher suites based on post-quantum cryptography for evaluation and usage. We also provide a fail-safe mechanism for a situation where an authenticating server might fail, and the deployed IoT devices can self-organize to keep providing services with no human intervention. We find that our protocol works the fastest when using ring learning with errors. We prove the safety of our authentication protocol using the automated validation of Internet security protocols and applications tool. In conclusion, we propose a safe, hybrid, and fast authentication protocol for authenticating IoT devices in a fog computing environment.

Green and low-carbon is a new development model that seeks balance between environmental sustainability and high economic growth. If explainable and available carbon emission data can be accurately obtained, it will help policy regulators and enterprise managers to more accurately implement this development strategy. A lot of research has been carried out, but it is still a difficult problem that how to accommodate and adapt the complex carbon emission data computing models and factor libraries developed by different regions, different industries and different enterprises. Meanwhile, with the rapid development of the Industrial Internet, it has not only been used for the supply chain optimization and intelligent scheduling of the manufacturing industry, but also been used by more and more industries as an important way of digital transformation. Especially in China, the Industrial Internet identification and resolution system is becoming an important digital infrastructure to uniquely identify objects and share data. Hence, a compatible carbon efficiency information service framework based on the Industrial Internet Identification is proposed in this paper to address the problem of computing and querying multi-source heterogeneous carbon emission data. We have defined a multi cooperation carbon emission data interaction model consisting of three roles and three basic operations. Further, the implementation of the framework includes carbon emission data identification, modeling, calculation, query and sharing. The practice results show that its capability and effectiveness in improving the responsiveness, accuracy, and credibility of compatible carbon efficiency data query and sharing services.

How to use a few defect samples to complete the defect classification is a key challenge in the production of mobile phone screens. An attention-relation network for the mobile phone screen defect classification is proposed in this paper. The architecture of the attention-relation network contains two modules: a feature extract module and a feature metric module. Different from other few-shot models, an attention mechanism is applied to metric learning in our model to measure the distance between features, so as to pay attention to the correlation between features and suppress unwanted information. Besides, we combine dilated convolution and skip connection to extract more feature information for follow-up processing. We validate attention-relation network on the mobile phone screen defect dataset. The experimental results show that the classification accuracy of the attention-relation network is 0.9486 under the 5-way 1-shot training strategy and 0.9039 under the 5-way 5-shot setting. It achieves the excellent effect of classification for mobile phone screen defects and outperforms with dominant advantages.

The development of the Internet of Things (IoT) has brought great convenience to people. However, some information security problems such as privacy leakage are caused by communicating with risky users. It is a challenge to choose reliable users with which to interact in the IoT. Therefore, trust plays a crucial role in the IoT because trust may avoid some risks. Agents usually choose reliable users with high trust to maximize their own interests based on reinforcement learning. However, trust propagation is time-consuming, and trust changes with the interaction process in social networks. To track the dynamic changes in trust values, a dynamic trust inference algorithm named Dynamic Double DQN Trust (Dy-DDQNTrust) is proposed to predict the indirect trust values of two users without direct contact with each other. The proposed algorithm simulates the interactions among users by double DQN. Firstly, CurrentNet and TargetNet networks are used to select users for interaction. The users with high trust are chosen to interact in future iterations. Secondly, the trust value is updated dynamically until a reliable trust path is found according to the result of the interaction. Finally, the trust value between indirect users is inferred by aggregating the opinions from multiple users through a Modified Collaborative Filtering Average-based Similarity (SMCFAvg) aggregation strategy. Experiments are carried out on the FilmTrust and the Epinions datasets. Compared with TidalTrust, MoleTrust, DDQNTrust, DyTrust and Dynamic Weighted Heuristic trust path Search algorithm (DWHS), our dynamic trust inference algorithm has higher prediction accuracy and better scalability.

In order to improve the Physical Layer Security (PLS) perspective, this paper aims to empower function of PLS by considering a backhaul Non-Orthogonal Multiple Access (NOMA) system in two practical situations. In the proposed schemes, the untrusted user intercepts information transmitted to the far user, or the external eavesdropper overhears confidential information sent to the far user in the context of NOMA technique. Unlike the conventional NOMA systems, this paper emphasizes the actual situations of the existence of actual illegal users and legitimate users, especially the reasonable use of relay selection architecture to improve the confidentiality performance. To evaluate the security properties of the proposed scheme, a comprehensive analysis of the Security Outage Probability (SOP) performance is first performed, and then the corresponding SOP asymptotic expressions are derived for real scenarios related to eavesdroppers and untrusted users. Numerical results are performed to verify the analysis in terms of the secure performance metric.