Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Block Error Performance of Short-Packet Non-Orthogonal Multiple Access Systems

We develop a new framework to analyze the average block error rate (BLER) of using short-packet communications in a non-orthogonal multiple access (NOMA) system. In this system, the N-antenna base station (BS) designs its transmission weight as per the instantaneous channel knowledge from the BS to either the near user or the far user. Incorporating the unique relationship of the finite blocklength and the achievable rate from the BS to two users, we derive new closed-form expressions for the average BLER for arbitrary signal-to-noise ratios (SNRs). We also derive simple expressions for the asymptotic average BLER at high SNRs, based on which we determine the optimal power allocation and minimum blocklength for satisfying the average BLER targets at two users. We demonstrate that the user, whose channel knowledge is used for weight design, achieves a lower BLER than the other. We further show that NOMA leads to a lower latency than orthogonal multiple access given the same average BLER targets. Notably, this latency advantage is more profound when the average BLER target ratio between the near user and the far user increases.
Non-Coherent Massive MIMO Systems: A Constellation Design Approach

In this paper, a joint multi-user constellation is proposed for energy detection-based non-coherent massive multiple-input multiple-output system. This is motivated by the simple design and high energy efficiency it entails for both the transmitter and receiver. First, the orthogonal codes is employed to suppress the multi-user interference. However, this comes at the price of consuming more communications resources. In this study, the key to reduce code redundancy is the design of a joint constellation since it makes energy detection applicable when multiple users employ the same orthogonal codes. Although it is unsolvable initially, our analysis indicates that through minimizing the symbol-error rate (SER), the joint constellation design becomes feasible. Concretely, two analytical expressions of SER based on Gamma and Gaussian distributions are derived. Via minimizing the error probability, an important result that the joint constellation should satisfy is obtained. Accordingly, an isometric constellation design is proposed to find constellations that enable non-coherent reception with multiple users, and reduce SER simultaneously. In addition, decoding regions of symbol decision are optimized to further improve the error performance. In the end, numerical simulations are carried out to highlight the effectiveness of our proposed scheme.
Reasoning on objects and grasping using description logics

In the growing interest about robot ontologies, the role of description logics (DL) used to query the ontology is often neglected. After discussing the representation problems of using ontologies for robotics problems, we develop two case studies relevant for dexterous manipulation, grasping unknown objects and understanding objects as the sum of their parts. We develop knowledge bases for solving those problems, and examine the possible queries that could be answered. We observe that the creation of such relationships in ontologies can follow an embodied approach, letting the robot to create new relationships from experiments, so making knowledge acquisition an embodied and iterative process. From those two cases we conclude that the creation of robot ontologies able to improve reasoning in manipulation is still a challenge itself. However ontologies are expected to be a necessary tool to share knowledge between humans and robots, and their role in integrating available symbolic knowledge and experimental data is still to be exploited. GRAPHICAL ABSTRACT
Analyzing the Browser Security Warnings on HTTPS Errors

HTTPS provides authentication, data confidentiality, and integrity for secure web applications in the Internet. In order to establish secure connections with the target website but not a man-in-the-middle or impersonation attacker, a browser shows security warnings to users, when different HTTPS errors happen (e.g., it fails to build a valid certificate chain, or the certificate subject does not match the domain visited). Each browser implements its own design of warnings on HTTPS errors, to balance security and usability. This paper presents a list of common HTTPS errors, and we investigate the browser behaviors on each error. Our study discloses browser defects on handling HTTPS errors in terms of cryptographic algorithm, certificate verification, name validation, HPKP, and HSTS.
Mobility Data Assessment for Vehicular Networks

Understanding mobility is a fundamental task in the design of mobile networking solutions. The adoption of mobility traces is extremely relevant both to obtain a meaningful understanding of mobility and to create realistic simulation scenarios. However, those traces may have different features that lead to conclusions inconsistent with reality and, consequently, impact the performance of the proposed solutions. In this work, we propose a methodology to evaluate mobility traces considering their spatial and temporal aspects. Furthermore, we review real, publicly available, and widely adopted mobility traces and discuss the application them to vehicular networks. The results show that the use of mobility data for vehicular networks is extremely timely, but that they must undergo a process of quality improvement.
Big Data Analytics for User Association Characterization in Large-Scale WiFi System

Large-scale WiFi systems have been widely deployed in an increasing number of corporate places such as universities, big malls and companies, to provide fast Internet experience to users. However, user association patterns in such large-scale systems have not been well investigated, which is crucial for performance enhancement and intelligent system management. In this paper, we provide the analytics of a large-scale campus WiFi dataset, which includes more than 8,000 access points (APs) and 40,000 active users in the area of 3.0925 km<sup>2</sup>. By conducting extensive analysis on association patterns, we achieve several key insights as follows. First, user associations are highly dynamic as short association durations and frequent AP transitions prevail throughout the whole trace. Second, even though users may associate to many APs, they generally have a small preferable AP set in which they spend most of their WiFi connection time for data traffic; in addition, each user has distinct yet relatively fixed AP transition route, indicating that given its current associated AP, its next association AP is highly predictable. Third, diurnal association patterns are observed not only at single AP level, but also at the building and the system level, where the number of associated users and the data traffic vary periodically on a daily basis. These insights can provide valuable guidelines to numerous intelligent service provisions such as proactive service migration, edge content distribution, efficient network management.
An SDN-Based Framework for Routing Multi-Streams Transport Traffic Over Multipath Networks

Multi-stream transport protocols, such as SCTP and QUIC, tackle challenges such as Head of Line (HoL) faced by TCP. They are getting more and more deployed and currently carry more than 7% of the global Internet traffic. However, since these protocols have limited control over the actual routes the streams will take in the network, the streams are generally forwarded through a single and same path, even though there are generally multiple paths available in the network. Consequently, the traffic streams do not fully benefit from the available bandwidth and performance improvement remains limited. Software Defined Networks (SDN) separate control planes and data planes. It offers a complete view of the network to applications and enables network programmability through flexible rules. These rules may be used to ensure that the different streams generated by multi-stream transport protocols follow multiple paths in the network. In this paper, we propose an SDN-based framework for multi-stream transport protocols in multipath networks. The proposed framework provides an interface for applications to specify multi-stream rules. Based on these rules, the framework uses the services offered by the SDN Controller to ensure that the multiple streams go over multiple paths in the network. Experiments performed show that our proposal improves the QoS offered to the end users.
Energy Harvesting-Based D2D Relaying Achieving Energy Cooperation Underlaying

Energy Harvesting (EH)-based cellular communication has emerged for the merit of simple deployment and continuous energy supply recently. However, the amounts of harvested energy are not always enough to meet the communication requirements of cellular devices. In this paper, we propose an energy cooperation scheme taking advantage of Device-to-Device (D2D) relaying technology, in which those devices with insufficient energy are aided by others to accomplish data transmission. In this scheme, we study the energy efficiency optimization problem, which involves the D2D relay selection, spectrum reusing and power allocation issues. Mathematically, it is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem, which turns out to be NP-hard. Thus we make a detailed mathematical analysis to the problem and introduce a two-layer optimization algorithm, and based on which, a suboptimal solution with lower computational complexity is then proposed. The simulation results show that the schemes are valid and can achieve significant improvement in system transmission rate and the satisfaction rate of users' communication requirements.
Modified Bi-Directional LSTM Neural Networks for Rolling Bearing Fault Diagnosis

The rolling bearing fault diagnosis with vibration data is critical to the reliability and the safety of rotating machinery. According to the non-stationary characteristics and the simple logical structure characteristics of rolling bearing vibration data, a rolling bearing fault diagnosis method based on modified bidirectional long short-term memory (Bi-LSTM) neural network is put forward in this paper. Firstly, original vibration data are decomposed into time-frequency feature with the combination of Daubechies 10 wavelet packet transform and Symlets 8 wavelet packet transform. Then, we design bidirectional long-term memory (Bi-LTM) neural network, the Bi-LTM neural network only uses long-term memory to process rolling bearing feature data and get the result of fault diagnosis. In order to enhance functionality of the Bi-LTM internal activation function, the Bi-LTM internal function uses softsign. We evaluate our models on a standard dataset. Moreover, given the analytical results, compared to Bi-LSTM, the proposed Bi-LTM method further reduces the rolling bearing fault diagnosis error rate by 6 times. Numerical and simulation results verify that the rolling bearing fault diagnosis method based on the proposed method is justified.
Channel Acquisition for Hybrid Analog-Digital mMIMO System by Exploiting the Clustered Sparsity

This paper considers the channel estimation for massive multiple-input multiple-output (mMIMO) systems, where the base station (BS) employs hybrid analog-digital processing to reduce the hardware complexity. With hybrid analog-digital processing, the BS cannot obtain enough training samples since the number of radio frequency (RF) chains is limited. This results in significant difficulty in the design of channel estimation scheme. This paper aims to address this problem using the emerging machine learning techniques. By exploiting the sparsity property of angular-domain channel, a novel clustered sparse Bayesian learning (SBL) approach is proposed to estimate the channel reliably. The scheme does not require the knowledge of channel statistics and angular information of users. The numerical simulations show that the proposed scheme based on clustered SBL outperform the reference schemes significantly in term of normalized mean square error (NMSE).
Joint Content Placement, RRH Clustering and Beamforming for Cache-Enabled Cloud-RAN

This work studies the joint problem of optimal content placement, RRH clustering and beamformer design, in a cache-enabled cloud-radio access network (C-RAN). In the considered system, multiple remote radio heads (RRHs) connected to a centralized baseband unit (BBU) pool via fronthaul links, cooperatively serve the downlink users by grouping them into potentially overlapping clusters. Each RRH is equipped with a local cache from which it can directly acquire the requested user contents, without the need to occupy the fronthaul links. We aim to jointly optimize the caching placement, user association and downlink beamforming vector at each RRH, in order to strike a balance between fronthaul traffic reduction and transmission power minimization. To this end, we propose to employ the ratio between these two important system utilities as the objective function, referred to as caching efficiency. A penalty dual decomposition (PDD) based algorithm is presented to address the resulting nonconvex optimization problem, which features coupling constraints and mixed-integer variables. Simulation results validate the efficiency of the proposed algorithm.
Research on Data Mining Methods

Data mining technology has a strong technical foundation, which is the result of a long-term research and development of database technology. At first, all kinds of business data is stored in the database of computers, and then the database can be queried and accessed, then the database can be real-time traversed. Data mining makes the database technology get into a more advanced stage where the database cannot only query and traverse the past data, but also can identify the potential links between the data in the past, so as to promote the transfer of information. This paper expounds the contradiction between the rapid expansion and high efficiency of the data and people’s not getting the effective information and knowledge which are needed in scientific decision-making, puts forward the development and evolution of data mining, points out the prospect of data mining and finally describes all the work done in this paper.
Asymptotic BER Analysis of MMSE Receivers in Multicell MU-MIMO Systems

For large-scale antenna array multiple-input multiple-output (MIMO) systems, it has become a common assumption to use low-complexity linear receivers such as zero-forcing and minimum mean-square error (MMSE) combiners. Typical formulations for such combiners imply that only the desired signals within the cell of interest are explicitly taken into account for interference-rejection combining. In the context of a multi-cell multiuser MIMO system, we study the performance of uplink MMSE detection with the presence of the interfering signals from outside the cell. An asymptotic signal-to-interference-plus-noise ratio (SINR) lower bound is proposed, which becomes exact as the input signal-to-noise ratio (SNR) tends to infinity. The SINR lower bound is in simple form and helps to understand the impact of the interfering signals. From this, a closed-form bit-error rate (BER) upper bound is derived, which can be applied for users with arbitrary power and an arbitrary number of antennas at the base station. The BER bound is validated through simulations, and is found to be very tight for the whole range of the input SNR.
Location Prediction with Communities in User Ego-Net in Social Media

Social media embed rich but noisy signals of physical locations of their users. Accurately inferring a user's location can significantly improve the user's experience on the social media and enable the development of new location-based applications. This paper proposes a novel community-based approach for predicting the location of a user by using communities in the egonet of the user. We further propose both geographical proximity and structural proximity metrics to profile communities in the ego-net of a user, and then evaluate the effectiveness of each individual metric on real social media data. We discover that geographical proximity metrics, such as average/median haversine distance and community closeness, are strong indicators of a good community for geotagging. In addition, structural proximity metric conductance performs comparable to geographical proximity metrics while triangle participation ratio and internal density are weak location indicators. To the best of our knowledge, this is the first effort to infer the physical location of a user from the perspective of latent communities in the user's ego-net.
Multi-Layer Coding and Map-Assisted Partial Group Decoding for Multi-Color Multi-User Visible Light Communication

In this paper, interference cancellation with multilayer coding and constrained partial group decoder (CPGD) is adopted for visible light communication (VLC) with a light emitting diode (LED) array. To reduce the online computational complexity, a decoding map on the decoding orders and the associated rate allocations is constructed via solving the max-min fairness problem based on the derived achievable rate of a VLC multiple access channel. A classification-based algorithm is proposed to reduce the decoding map size. Finally, the system throughput is further enhanced via solving the transmitter-user association problem followed by an iterative update of rate allocation and decoding order. From the numerical results, symmetric decoding map is observed for the squared transmitting LED array.
Group-Sparse Beamforming for Sum-Spectral Efficiency Maximization in Cloud-RAN

A cloud radio access network (cloud-RAN) is a promising cellular architecture to increase both network spectral efficiency and energy efficiency. In the downlink transmission of cloud-RAN, a fundamental trade-off exists between the sum-spectral efficiency and the network power consumption induced by the fronthual links. To optimize this trade-off, it is essential to jointly identify a set of active remote radio heads (RRHs) and the beamforming vectors used at the active RRHs. To resolve this problem, this paper presents a novel group-sparse beamforming algorithm inspired by sparse principal component analysis (sparse-PCA). The key idea of the proposed method is to reformulate the sum-spectral efficiency maximization problem under a group-sparsity constraint into a generalized sparse-PCA problem, which is a tractable non-convex optimization problem. Using this reformulated optimization problem, a computationally efficient algorithm is proposed, which finds the solution that guarantees the first-order necessary optimality condition of the non-convex optimization problem. Simulation results demonstrate significant advantage of the proposed group-sparse beamfroming method.
Optimal Design of Multiple Panel Arrays in LoS MIMO System

This paper investigates the optimal design of multiple panel arrays (MPAs) for line-of-sight (LoS) multiple-input multiple-output (MIMO) communication systems. We use the spherical wave channel model and give a geometric model to model the LoS channel, which allows the receive antenna arrays to have azimuth rotation, elevation angle rotation, up-down offset and left-right offset distance. Based on the geometric model, we derive the optimal antenna design conditions for achieving the maximum channel capacity and spatial multiplexing gain according to the effective degrees of freedom. The results show that the proposed antenna design can achieve high channel space freedom when the receive antennas have angle rotation and offset, and is suitable for the case where the receive antennas have a large left-right offset distance.
Latency-Aware Traffic Grooming for Dynamic Service Chaining in Metro Networks

Optical metro networks are currently evolving in response to the new requirements of emerging 5G services. Network Function Virtualization (NFV) is being leveraged as a platform to dynamically provision these services on top of Virtual Network Functions (VNFs), and central offices in metro areas are being upgraded to host processing units that can host the needed to provision services with stringent-latency and high-bandwidth requirements closer to users (i.e., edge computing). By concatenating these VNFs in a specific order and route traffic among them, operators generate a so-called “Service Chain“(SC). Considering the fact that, new 5G services have bandwidth requirements typically with sub-wavelength granularity, traffic grooming is required to achieve efficient network resources utilization. Since grooming affects the end-to-end latency of provisioned services, we investigate how to perform latency-aware traffic grooming, and we propose an algorithm for dynamic SC provisioning, that considers the latency requirements of each SC to decide about if grooming shall be allowed at intermediate network nodes. Our proposed algorithm tries to minimize the blocked bandwidth as well as number of nodes to host VNFs in the network (NFV-nodes) considering the nodes computational capacity, links bandwidth and end-to-end latency constraints. Results obtained from numerical evaluation show that, our algorithm is able to reduce the number of NFV-nodes up to 50%, while keeping amount of blocked bandwidth below a specific threshold.
Secure Downlink Massive MIMO NOMA Network in the Presence of a Multiple-Antenna Eavesdropper

In this paper, the secrecy performance of a massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) network is studied in the presence of a multiple-antenna eavesdropper. The ergodic secrecy rates for the downlink transmission in the considered system are derived to provide important insights. Then, by using these results, a joint power allocation scheme is proposed for both uplink training and downlink data transmission phases to maximize the sum ergodic secrecy rates. Because the utility function of interest is non-concave and the involved constraints are non-convex, a new iterative algorithm is proposed, which can find at least a local optimum. The obtained results reveal that the secrecy performance of NOMA networks benefits from deploying massive MIMO techniques. They also indicate that the proposed optimization algorithm enhances the secrecy performance of the considered system.
Doppler Characteristics of 60 GHz mmWave I2I Channels

This paper analyzes the data obtained from a 60 GHz infrastructure-to-infrastructure millimetre wave channel sounding campaign, carried out in an urban-highway environment in Brno, Czech Republic. The transmitter and the receiver are placed at different heights to emulate a link between a typical base station and a roadside unit while the passing vehicles acted as scatterers. The measured data is processed to determine the Doppler spread which is then analyzed to study the effect of mobile scatterers on the channel. The proposed processing method successfully overcomes the limitations of the measurement setup. A detailed discussion on the latency versus accuracy trade-off of the proposed method is also presented.
Machine-Learning Assisted Outdoor Localization via Sector-Based Fog Massive MIMO

Gains in area spectral efficiency have been recently demonstrated in networks of large-antenna arrays by means of fog massive MIMO operation and virtual sector-based processing. In this paper, we adopt such sector-based processing and operation to localize users in the network. We investigate the viability of some widely used supervised-learning methods in estimating user locations by observing across the fog massive MIMO network signals transmitted by the users. In particular, we evaluate linear regression (LR), weighted K-nearest neighbors (WKNN), and neural networks (NN) in the context of a network of massive-antenna remote-radio heads (RRHs) using simulations based on a spatially consistent channel model. As our simulations reveal, NN-based location estimators trained with user-sector channel gains could be a viable approach for providing user location information at the network side.