Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature-Selection Based Data Prioritization in Mobile Traffic Prediction Using Machine Learning

Recently, the demand for realtime and accurate prediction of mobile traffic has been growing in traffic engineering and dynamic resource allocation that work to handle increased mobile data traffic. However, most conventional prediction techniques assumed that traffic logs at every unit time at every base station are perfectly available. This assumption is critical in realtime mobile traffic prediction because the volume of traffic log data collected at base stations is huge and they compete bandwidth with normal user application traffic when they are sent from base stations to the server that performs prediction. Therefore, in realtime mobile traffic prediction, we should consider the condition in which the bandwidth ensured for forwarding traffic log data is limited. In this paper, we propose a method that prioritizes traffic log data in the basis of the contribution to prediction accuracy; each base station sends more important traffic log data to the server with higher priority. The importance of each data entry of traffic log data means how much prediction accuracy would degrade if the entry is missing. The proposed method enables us to reduce the volume of traffic log data sent from base stations to the server while maintaining prediction accuracy at the sufficient level. Our simulation study using a real dataset of mobile-traffic measurement validates our method in terms of prediction accuracy under the limitation of available traffic log data.
Nonlinearity Mitigation for VLC with an Artificial Neural Network Based Equalizer

Light emitting diode (LED) is a major source of nonlinearity in visible light communication (VLC). In this work, we introduce a Wiener-Hammerstein model considering the LED nonlinearity and memory effect of an indoor channel. The VLC nonlinearity belongs to dynamic-range-limited nonlinearities. Considering the impact of nonlinearity strength in different dynamic regions, we design three types of signals for both weak and strong nonlinearity regions. Moreover, in order to well compensate the nonlinearity, an artificial neural network (ANN) based equalizer is compared with the conventional Volterra series-based equalizer and the memory orthogonal polynomial-based equalizer. The results show that the proposed equalizer significantly outperforms conventional nonlinear equalizers, up to two orders of magnitude in high SNR region.
Indoor Wired Communication for Connected Devices in Impulsive Transients

We consider an Internet of things (IoT) environment for a smart home where all the electronic appliances can communicate to each other and to the outer world as well. Unlike ongoing emphasis on wireless connectivity in an indoor environment, we extend the concept of power line communication (PLC) to facilitate the communication among connected devices. In this paper, we assume that the communication among the smart appliances takes place through electrical wires feeding the power supply. The effect of sudden turn-on or turn-off of other devices on the communicating device pair is incorporated through probabilistic models. Specifically, the sudden transients are characterized using Poisson distributed random variable. We statistically analyse the performance of such system in terms of capacity. Precisely, the expression for average capacity is derived in Poisson transients.
Patient-Aware EEG-Based Feature and Classifier Selection for e-Health Epileptic Seizure Prediction

With the advent of wireless EEG headsets and the improved computational capabilities of mobile devices, the ubiquitous monitoring of patients suffering from epilepsy has recently gained notable interest from research and industry under the umbrella of emerging e-health systems. Among others, the problem of seizure prediction plays a central role in reducing the adverse effects of epileptic seizures by trying to foresee the occurrence of an epileptic seizure before its onset. This paper presents a patient-aware seizure prediction approach which combines an optimized spatio-temporal EEG feature extraction algorithm and classifier selection to maximize the prediction accuracy and reduce the false alarm rate. Experimental results demonstrate that the proposed approach leads to promising prediction results when tested on real clinical data from the Freiburg database, with accuracy exceeding 90% for most subjects. The key differentiating aspect of the proposed approach is its flexibility to be efficiently fine-tuned and optimized per subject in order to enhance sensitivity and minimize the false alarm rate.
Tomography of Large Adaptive Networks under the Dense Latent Regime : Invited Paper

This work examines the problem of graph learning over a diffusion network when measurements can only be gathered from a limited fraction of agents (latent regime). Under this setting, most works in the literature rely on a degree of sparsity to provide guarantees of consistent graph recovery. This work moves away from this condition and shows that, even under dense connectivity, the Granger estimator ensures an identifiability gap that enables the discrimination between connected and disconnected nodes within the observable subnetwork.
Towards Green Fog-LR-PON Integration for Wireless Backhauls

Integrating optical access networks with fog computing combines the high capacity of optical fiber with closer-to-the-edge computing and storage capabilities. Such integration is believed to form a highly capable backhaul that will alleviate network congestions, serve local demands with less energy consumption, and live up to the requirements of tomorrow's access networks and application requirements. This integration however requires reexamining the bandwidth allocation in addition to reconsidering the network architecture itself, which was not designed to support direct edge-to-edge communications. Moreover, the growing demands for energy-efficient access networks also add the requirement for sleep-aware bandwidth allocation and a power-conserving framework. In this paper, we study the offloading performance in a long-reach passive optical network (LR-PON) when the underlying bandwidth allocation is either centralized or decentralized. Moreover, we investigate how offloading can be supported in each paradigm when optical network units (ONUs) go through a cyclic sleep-mode to conserve energy. We consider this paper to be one of the first to look into integrating fog with LR-PONs under a power-conserving framework and examine which allocation paradigm would be fit to carry offloaded traffic with better network performance and energy-efficiency within this new setting.
Multi-Pair Two-Way Massive MIMO Relaying with Hardware Impairments over Rician Fading Channels

We consider a multi-pair two-way massive multiple-input multiple-out (MIMO) relaying system over Rician fading channels, where multi-pair users exchange their information via the amplify-and-forward (AF) relaying equipped with large number of antenna arrays. Hardware impairments at the relay and imperfect channel state information (CSI) are taken into account. More specifically, we derive a new linear minimum mean- square error (MMSE) channel estimator for the proposed system. It is demonstrated that normalized mean square error (NMSE) is a constant when the pilot power grows to infinity. Moreover, the asymptotic spectral efficiency with maximum ratio processing is presented in closed-form and the power scaling laws are analyzed. Simulation results indicates that massive MIMO is capable of compensating the loss caused by hardware impairments, estimation error and Rician fading.
How Many Operational Design Domains, Objects, and Events?

A first step toward validating an autonomous vehicle is deciding what aspects of the system need to be validated. This paper lists factors we have found to be relevant in the areas of operational design domain, object and event detection and response, vehicle maneuvers, and fault management. While any such list is unlikely to be complete, our contribution can form a starting point for a publicly available master list of considerations to ensure that autonomous vehicle validation efforts do not contain crucial gaps due to missing known issues.
Cloud Offloading with QoS Provisioning Using Cross-Layer Network Coding

In this paper, we consider the use of cross- layer network coding and fog radio access networks (F- RANs) as a means to jointly optimize users quality-of-service and offload the cloud servers and cellular macro base- stations. Multiple edge nodes called enhanced remote radio heads (eRRHs) are connected to a central unit known as cloud base station (CBS). The transmit frame of each eRRH consists of multiple resources blocks called power zones (PZs), each fixed at a pre-assigned power level. The various ergodic capacities of different users at different PZs&#x002F;eRRHs and the CBS bring a new trade-off between the number of multiplexed users and the transmission rates of each PZ and each CBS allocated channel. The proposed framework incorporates such information in the network coding decisions. As such, the multiplexed users, encoded files, and transmission rates of each PZ&#x002F;eRRH and each CBS allocated channel maximizes the throughput which is defined as the number of correctly received bits and actual CBS physical-resource offloading, respectively. The problem is first formulated using graph theory techniques, and its intractability is shown. Given the difficulty of the problem, the paper proposes a heuristic approach by dividing it into two sequential subproblems and solving each subproblem efficiently. Presented simulation results reveal that the proposed solution achieves small offloading performance degradation compared to the cross-layer QoS unaware scheme but largely maximizes the received throughput compared to the state-of- art algorithms.
Joint Wired and Wireless Traffic Minimization for Energy-Efficient Content Delivery Networks

Pushing contents from content providers (CPs) directly to user equipments (UEs) during off-peak hours can significantly reduce the incurring traffic during peak hours. Both wired and wireless transmission costs are considerable in this scenario, especially in current cellular networks where the backhaul is regarded as a bottleneck of transmission. In order to alleviate the traffic pressure over the network without loss of users' quality of experience, this paper presents a joint wired and wireless transmission scheme which considers grouping, subchannel allocation, wired routing, and wired bandwidth allocation. An iterative algorithm is proposed to achieve the tradeoff between those two kinds of traffic. The users in the same group are served by a single multicast transmission from a base station (BS), while the BSs which multicast the same content form a multicast tree in backbone wired network. Compared with traditional unicast scheme, our approach can reduce the wired, wireless, and total traffic by 23%, 46%, and 34% at most according to the simulation results.
Kernel K-Mace Clustering

We propose a kernel k-means based unsupervised clustering algorithm. The kernel k-means approaches require finding not only the Correct Number of Clusters (CNC) but also the optimum kernel function parameters in the clustering procedure. Existing index validation approaches use a criterion different from the k-means criterion to find the optimum CNC and also choose kernel parameter by trial and error. The proposed algorithm denoted by kernel k-Minimum Average Central Error (Kernel k-MACE), estimates the CNC while simultaneously providing the optimum value of the Gaussian kernel parameter. The advantage of the method in theory is in its consistency in using only one criterion for all the three steps of clustering, CNC estimation, and kernel function parameter estimation. A novel cluster initialization technique enables Kernel k-MACE to converge in less iterations compared to the existing approaches. Simulation results illustrate superiority of Kernel K-MACE over multiple state-of-the-art unsupervised clustering methods for both real data sets and self-generated data sets having 10%-50% overlap. The method outperforms the existing methods by not only providing more accurate CNC estimates, but also by providing better clustering results evaluated by Adjusted Random Index (ARI) and Normalized Variation Index (NVI).
FASOR Retransmission Timeout and Congestion Control Mechanism for CoAP

The Constrained Application Protocol (CoAP) has been designed to be used on constrained devices such as Internet of Things (IoT) devices. The existing congestion control algorithms for CoAP have known shortcomings in addressing congestion and retaining a good level of performance when link errors occur. In this paper, we propose Fast-Slow RTO (FASOR) mechanism that takes into account special needs in wireless environments while still properly addressing congestion. We run a series of experiments to confirm that FASOR is able to successfully cope with challenging network conditions such as bufferbloat, high level of congestion, and high link-error rates unlike the default and CoCoA congestion control that have severe problems with bufferbloated congestion.
Mobility Prediction Scheme for Optimized Load Balance in Heterogeneous Networks

With the rapid growing of global mobile data traffic, communication network capacity encounters big challenges. Especially in some hot areas, such as shopping malls, teaching buildings and gymnasiums, there are massive mobile users in the overloaded cells, while the neighbor cells may have few users. Therefore, the user mobility may result in load imbalance and further impact the network performance. In this paper, we introduce the mobility prediction scheme to optimize the network load balance. The mobility prediction model is established with decision tree. To find the beneficial input training attributes, inspired by the idea of order-2 Markov model, we study the effect of user's previous several movement states on the prediction to improve the prediction accuracy. Then, the time series analysis method is adopted to find the load status variation regularity rather than monitoring every potential user's impact to improve efficiency. Eventually, we present the resource allocation scheme to optimize the network load balance based on the users mobility prediction result and target cell load status analysis. Simulation results show that the proposed scheme could optimize the system's load balance effectively in the heterogeneous networks.
Development of Natural Language Processing Tools for Cook Islands Māori

This paper presents three ongoing projects for NLP in Cook Islands Maori: Untrained Forced Alignment (approx. 9% error when detecting the center of words), speech-to-text (37% WER in the best trained models) and POS tagging (92% accuracy for the best performing model). Included as part of these projects are new resources filling in a gap in Australasian languages, including gold standard POS-tagged written corpora, transcribed speech corpora, time-aligned corpora down to the level of phonemes. These are part of efforts to accelerate the documentation of Cook Islands Maori and to increase its vitality amongst its users.
Network Slice Selection in Softwarization Based Mobile Networks

Recently network slicing has been introduced as a key enabler to accommodate diversified services in NFV- enabled software-defined mobile networks. Although there has been some research work on network slice deployment and configuration, how user equipments (UEs) select the most appropriate network slice is still an essential yet challenging issue, as slice selection may substantially affect the resource utilization and user quality of service. In this paper, we investigate the optimal selection of end-to-end slices with aim of improving network resources utilization while guaranteeing the quality of service (QoS) of users. We formulate the optimal slice selection problem as maximizing the users' satisfaction degree, and prove it is NP-hard. We thus resort to genetic algorithm (GA) to find a sub-optimal solution, and develop a heuristic algorithm based on GA algorithm. The effectiveness of our proposed NS selection algorithm is validated via simulation experiments.
Early Video Content Popularity Detection with Change Point Analysis

Video content is responsible for more than 70% of the global IP traffic. Consequently, it is important for content delivery infrastructures to rapidly detect and respond to changes in content popularity dynamics. For flexible and highly adaptive solutions, the capability for a quick response should be driven from early (real-time) and low-complexity content popularity detection schemes. In this paper, we focus on the early and low-complexity detection of video content popularity, which we address as a statistical change point (CP) detection problem. Our proposed methodology estimates in real-time the existence, the number, the magnitude and the direction of changes in the average number of video visits by combining: (i) off-line and on-line CP schemes; (ii) an improved measurements window segmentation heuristic for the detection of multiple CPs; and (iii) a variation of the moving average convergence divergence (MACD) indicator to detect the direction of changes. We evaluated the proposed framework using a large database of real youtube video visits. The proposed algorithm is shown to accurately identify CPs and the direction of change in the off-line phase. Finally, a few illustrative examples of two variations of the on-line algorithm are also included.
Performance Analysis of User Plane Connectivity in the 5G Non-Standalone Deployment

The Fifth Generation (5G) radio networks are expected to appear first according to so called non-standalone deployment option, which was created as a set of early specifications by the Third Generation Partnership Project (3GPP). The non- standalone option has its basis in dual- connectivity technique, which allows the use of legacy network and which aims at reliability of connection, coverage and yet very high throughput. This article conducts a performance analysis of the 5G non-standalone option for a split bearer in the user plane by means of simulations. The analysis focuses on the factors that influence the throughput and latency, as well as the impacted receiver window. The study addresses dual- connectivity options, where a slower connection is serving concurrently with the high performing 5G. Further analysis follows for the impact of improving the packet delay observed over the slower transmission path, the impact of packet data arrival rate, as well as the tolerated amount of data in-flight dependent on the size of the protocol unit Sequence Number.
Shrinkage Estimation of Semiparametric Multiplicative Error Models

Within models for nonnegative time series, it is common to encounter deterministic components (trends, seasonalities) which can be specified in a flexible form. This work proposes the use of shrinkage type estimation for the parameters of such components. The amount of smoothing to be imposed on the estimates can be chosen using different methodologies: Cross-Validation for dependent data or the recently proposed Focused Information Criterion. We illustrate such a methodology using a semiparametric autoregressive conditional duration model that decomposes the conditional expectations of durations into their dynamic (parametric) and diurnal (flexible) components. We use a shrinkage estimator that jointly estimates the parameters of the two components and controls the smoothness of the estimated flexible component. The results show that, from the forecasting perspective, an appropriate shrinkage strategy can significantly improve on the baseline maximum likelihood estimation.
3D Cellular Network Architecture with Drones for beyond 5G

In this paper, a novel concept of three-dimensional (3D) cellular networks, that integrate drone base stations (drone-BS) and drone users (drone-UEs), is introduced. For this new 3D cellular network architecture, a novel framework for the deployment of drone-BSs and latency-minimal cell association for drone-UEs is proposed. For drone-BSs' deployment, a tractable method based on the notion of truncated octahedron shapes is proposed that ensures full coverage for a given space with minimum number of drone-BSs. Then, an optimal 3D cell association scheme is determined such that the drone-UEs' latency, considering transmission, computation, and backhaul latencies, is minimized. In particular, using optimal transport theory, the optimal 3D cell partitions are derived according to the spatial distribution of drone-UEs and the drone-BSs' locations. Simulation results show that the proposed approach reduces the latency of drone-UEs compared to the classical cell association approach that uses a signal-to-interference-plus-noise ratio (SINR) criterion. In particular, the proposed approach yields a reduction of up to 46% in average latency compared to the SINR-based association. Also, it is shown that the proposed latency-optimal cell association improves the spectral efficiency of a 3D wireless cellular network of drones.
When Blockchain Makes Ephemeral Keys Authentic: A Novel Key Agreement Mechanism in the IoT World

Conventional Key Management Protocols establish secure communication channels by using asymmetric cryptography based on “fixed” public keys. In the Internet of Things context, it is preferable to have a good level of freshness of the cryptographic material. But efficient solutions leveraging “ephemeral” public keys are not available yet. The work presented herein solves this problem by conceiving a novel key agreement methodology integrating the Blockchain technology. Specifically, Blockchain is used to store X.509 certificates related to the initial fixed public keys of devices, to publish new ephemeral public keys, and to help the verification of the authenticity of ephemeral public keys without sending signatures and additional X.509 certificates. A preliminary comparison against Transport Layer Security protocol and other customized key agreement schema presented in the literature demonstrates that the proposed approach registers low communication overhead, limited energy consumptions, and acceptable communication latencies while ensuring the lowest memory footprint.
Ping Pong Beam Training for Multi Stream MIMO Communications with Hybrid Antenna Arrays

We propose an iterative training procedure that approximates multi-stream MIMO eigenmode transmission between two transceivers equipped with hybrid digital analog antenna arrays. The procedure is based on a series of alternate (ping pong) transmissions between the two devices in order to exploit the reciprocity of the wireless channel. During the ping pong iterations, the update of the devices' digital precoders/combiners is performed based on a QR decomposition of the received signal matrix. Concurrently, their analog precoders/combiners are progressively updated by a novel “multi-beam split and drop with backtracking” mechanism that tracks the channel's main spatial components. As shown throughout the paper, the proposed algorithm converges with only few iterations, has minimal computational complexity, and performs very closely to optimal singular value decomposition based precoding with sufficiently large signal-to-noise ratio.