When a failure occurs in a network, network operators need to recognize service impact, since service impact is essential information for handling failures. In this paper, we propose Deep learning based Service Impact Prediction (DeepSIP), a system to predict the time to recovery from the failure and the loss of traffic volume due to the failure in a network element using a temporal multimodal convolutional neural network (CNN). Since the time to recovery is useful information for a service level agreement (SLA) and the loss of traffic volume is directly related to the severity of the failures, we regard these as the service impact. The service impact is challenging to predict, since a network element does not explicitly contain any information about the service impact. Thus, we aim to predict the service impact from syslog messages and traffic volume by extracting hidden information about failures. To extract useful features for prediction from syslog messages and traffic volume which are multimodal and strongly correlated, and have temporal dependencies, we use temporal multimodal CNN. We experimentally evaluated DeepSIP and DeepSIP reduced prediction error by approximately 50% in comparison with other NN-based methods with a synthetic dataset.

Since Korea has a limited ICT R&D fund compared to other IT global countries, its strategy was essential to achieve its global competence in each generation of mobile communication. Just after the rollout of the world's first 5G service, the government took the next step by announcing the 5G strategy to promote the 5G application to a wide-ranging industry and create a sustainable 5G ecosystem leading to new growth engines. In this article, we focus on the government-industry 5G collaborations, including the R&D roadmap and promotion to the 5G commercialization, the global collaboration, the first 5G experience, and 5G vertical trials to make the 5G-enabled industrial transformation take place in Korea. The development of an electronic digital switching system called TDX in the 1980s, the world's first CDMA mobile service in the 1990s, and the nationwide wired and mobile broad Internet networks in the 2000s are the key advances that made it possible for Korean consumers to easily adopt new technologies such as LTE and 5G. In 2018, the handset penetration rate of South Korea was similar to western Europe, where LTE adaption was 84% with 99.95% coverage and 65Mbps downlink capacity.4

Ericsson Overview Ericsson is a world-leading provider of telecommunications equipment & services to mobile & fixed network operators. Over 1,000 networks in more than 180 countries use Ericsson equipment, & more than 40 percent of the world's mobile traffic passes through Ericsson networks. Using innovation to empower people, business & society, we are working towards the Networked Society, in which everything that can benefit from a connection will have one. At Ericsson, we apply our innovation to market-based solutions that empower people & society to help shape a more sustainable world. We are truly a global company, working across borders in 175 countries, offering a diverse, performance-driven culture & an innovative & engaging environment where employees enhance their potential every day.

Predictive power allocation is conceived for power-efficient video streaming over mobile networks using deep reinforcement learning. The goal is to minimize the accumulated energy consumption over a complete video streaming session for a mobile user under the quality of service constraint that avoids video playback interruptions. To handle the continuous state and action spaces, we resort to deep deterministic policy gradient (DDPG) algorithm for solving the formulated problem. In contrast to previous predictive resource policies that first predict future information with historical data and then optimize the policy based on the predicted information, the proposed policy operates in an online and end-to-end manner. By judiciously designing the action and state that only depend on slowly-varying average channel gains, the signaling overhead between the edge server and the base stations can be reduced, and the dynamics of the system can be learned effortlessly. To improve the robustness of streaming and accelerate learning, we further exploit the partially known dynamics of the system by integrating the concepts of safer layer, post-decision state, and virtual experience into the basic DDPG algorithm. Our simulation results show that the proposed polices converge to the optimal policy derived based on perfect prediction of the future large-scale channel gains and outperforms the first-predictthen-optimize policy in the presence of prediction errors. By harnessing the partially known model of the system dynamics, the convergence speed can be dramatically improved. I. INTRODUCTION Mobile video traffic is expected to account for more than 75% of the global mobile data by 2021, and video-on-demand (VoD) services represent the main contributor [2]. This paper was presented in part at IEEE Globecom 2019 [1]. To avoid video stalling for a user experiencing hostile channel conditions, a base station (BS) can increase its transmit power for ensuring that the video segment is downloaded before being played.

"Delivering our commitment to customers means creating innovative solutions, powered by the latest technology that helps our customers move their business forward," said Derek Bryan, vice president EMEA, Verizon Connect. "We're delivering next-level solutions, powered by advanced AI and machine learning to help our customers be safe, productive and efficient all over the world.

In this paper, the problem of minimizing energy and time consumption for task computation and transmission is studied in a mobile edge computing (MEC)-enabled balloon network. In the considered network, each user needs to process a computational task in each time instant, where high-altitude balloons (HABs), acting as flying wireless base stations, can use their powerful computational abilities to process the tasks offloaded from their associated users. Since the data size of each user's computational task varies over time, the HABs must dynamically adjust the user association, service sequence, and task partition scheme to meet the users' needs. This problem is posed as an optimization problem whose goal is to minimize the energy and time consumption for task computing and transmission by adjusting the user association, service sequence, and task allocation scheme. To solve this problem, a support vector machine (SVM)-based federated learning (FL) algorithm is proposed to determine the user association proactively. The proposed SVM-based FL method enables each HAB to cooperatively build an SVM model that can determine all user associations without any transmissions of either user historical associations or computational tasks to other HABs. Given the prediction of the optimal user association, the service sequence and task allocation of each user can be optimized so as to minimize the weighted sum of the energy and time consumption. Simulations with real data of city cellular traffic from the OMNILab at Shanghai Jiao Tong University show that the proposed algorithm can reduce the weighted sum of the energy and time consumption of all users by up to 16.1% compared to a conventional centralized method.

Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few. Giving applications access to personal texts can easily lead to (un)intentional privacy violations. We propose the first privacy-preserving solution for text classification that is provably secure. Our method, which is based on Secure Multiparty Computation (SMC), encompasses both feature extraction from texts, and subsequent classification with logistic regression and tree ensembles. We prove that when using our secure text classification method, the application does not learn anything about the text, and the author of the text does not learn anything about the text classification model used by the application beyond what is given by the classification result itself.

Artificial intelligence (AI) is one of the most exciting technological growth areas in recent years, with some investors like technologically-focused entrepreneur Tej Kohli predicting the sector will be worth $150 trillion (£125tn) by 2025, but why do we need the technology in our phones? Flagship devices today all come equipped with specialised AI processing chips, known and neural engines or neural processing units, from Apple's A12 Bionic CPU to Huawei's Kirin 980 or Qualcomm's Snapdragon 845, and more and more tasks are using their advanced processing capabilities. The most obvious artificial intelligence in our phones are the voice assistants that learn to understand our voice commands and then act appropriately from telling us the weather to playing our favourite song or adding an appointment to our calendar. Google, Apple, and Amazon have steered clear of labelling their services as AI so as not to scare away users fearful of a robot takeover, but these services rely on machine learning to function – understanding what you are telling them to do and then performing the right action. Possibly the most advanced implementation of any digital assistant is Google's Duplex service that will make calls and interact with other people and businesses on your behalf.

Routing is one of the key functions for stable operation of network infrastructure. Nowadays, the rapid growth of network traffic volume and changing of service requirements call for more intelligent routing methods than before. Towards this end, we propose a definition of cognitive routing and an implementation approach based on Deep Reinforcement Learning (DRL). To facilitate the research of DRL-based cognitive routing, we introduce a simulator named RL4Net for DRL-based routing algorithm development and simulation. Then, we design and implement a DDPG-based routing algorithm. The simulation results on an example network topology show that the DDPG-based routing algorithm achieves better performance than OSPF and random weight algorithms. It demonstrate the preliminary feasibility and potential advantage of cognitive routing for future network.

This paper proposes a convolutional neural network (CNN) model which utilizes the spectral correlation function (SCF) for wireless radio access technology identification without any prior information about bandwidth and/or the center frequency. The sensing and classification methods are applied to the baseband equivalent signals. Two different approaches are elaborated. The proposed method is implemented in two different settings; in the first setting, signals are jointly sensed and classified. Sensing and classification are conducted in a sequential manner in the second setting. The performance of both approaches is discussed in detail. The proposed method eliminates the threshold estimation processes of classical estimators. It also eliminates the need to know the distinct features of signals beforehand. Over-the-air real-world measurements are used to show the robustness and the validity of the proposed method and various wireless signals are successfully distinguished from each other without any a priori knowledge. The over-the-air real-world measurements are also shared in the format of SCF. The performance of SCF-based identification is compared with the cases when fast Fourier transform and amplitude-phase representation are used as the training inputs for CNN. The comparative performance of the proposed method is quantified by precision, recall, and F1-score metrics. Moreover, a setup to compare the performance of the proposed approach with classical cyclostationary features detection (CFD) is prepared. Measurement results indicate the superiority of the proposed method against CFD, especially at the low signal-to-noise ratio regime.