KEY FINDINGS The automated chatbots, personalized offers, and efficiently streamlined customer service processes can be managed to provide enhanced customer service by the telecommunication services if the Artificial Intelligence gets integrated with the former. By assimilating advanced technologies like Artificial Intelligence, machine learning, etc. and 5G system, the telecommunication operators, can enhance and implement realization of high levels of self-organization, intelligent management and fault-free networks that are much more reliable as compared to the earlier networks. Advantages like detection of flaws in the network, network security, network optimization & offer virtual assistance are influencing the global market for Artificial Intelligence in telecommunication to propel vigorously at a CAGR of 42.16% from 2019-2027, as estimated by Inkwood Research. Furthermore, the incorporation of artificial intelligence technologies with upcoming wireless networks is appraised to increase the demand & adoption of such artificial intelligence tools & services in the telecommunication sector. MARKET INSIGHTS The upsurge in mobile data traffic & smartphone users across the world and the integration of AI with newer wireless networks will necessarily drive the global AI in the telecom market.Concerns related to incompatibility, the unreliability of artificial intelligence algorithms, lack of skilled personnel & difficulties in the protection of confidential & private data are the primary challenges faced by the market players.

The cloud radio access network (CRAN) is a promising paradigm to meet the stringent requirements of the fifth generation (5G) wireless systems. Meanwhile, wireless traffic prediction is a key enabler for C-RANs to improve both the spectrum efficiency and energy efficiency through load-aware network managements. This paper proposes a scalable Gaussian process (GP) framework as a promising solution to achieve large-scale wireless traffic prediction in a cost-efficient manner. First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale executions. Second, in the prediction phase, we fuse local predictions obtained from the BBUs via a cross-validation based optimal strategy, which demonstrates itself to be reliable and robust for general regression tasks. Moreover, such a cross-validation based optimal fusion strategy is built upon a well acknowledged probabilistic model to retain the valuable closed-form GP inference properties. Third, we propose a CRAN based scalable wireless prediction architecture, where the prediction accuracy and the time consumption can be balanced by tuning the number of the BBUs according to the real-time system demands. Experimental results show that our proposed scalable GP model can outperform the state-of-the-art approaches considerably, in terms of wireless traffic prediction performance. I. INTRODUCTION The fifth generation (5G) system is expected to provide approximately 1000 times higher wireless capacity and reduce up to 90 percent of energy consumption compared with the current 4G system [1]. A CRAN is composed of two parts: the distributed remote radio heads (RRHs) with basic radio functionalities to provide coverage over a large area, and the centralized baseband units (BBUs) pool with parallel BBUs to support joint processing and cooperative network management. The BBUs can perform dynamic resource allocation in accordance with realtime networkdemands based on the virtualized resources in cloud computing. One major feature for the C-RANs to enable high energy-efficient services is the fast adaptability to nonuniform traffic variations [1]-[4], e.g., the tidal effects. Consequently, wireless traffic prediction techniques stand out as the key enabler to realize such loadaware managementand proactive control in C-RANs, e.g., the load-aware RRH on/off operation [4].

Abstract--In this paper we consider the task of detecting abnormal communication volume occurring at node-level in communication networks. The signal of the communication activity is modeled by means of a clique stream: each occurring communication event is instantaneous and activates an undirected subgraph spanning over a set of equally participating nodes. We present a probabilistic framework to model and assess the communication volume observed at any single node. Specifically, we employ nonparametric regression to learn the probability that a node takes part in a certain event knowing the set of other nodes that are involved. On the top of that, we present a concentration inequality around the estimated volume of events in which a node could participate, which in turn allows us to build an efficient and interpretable anomaly scoring function. Finally, the superior performance of the proposed approach is empirically demonstrated in real-world sensor network data, as well as using synthetic communication activity that is in accordance with that latter setting. I. INTRODUCTION Monitoring the activity in communication networks has become a popular area of research and particular attention has been paid to detection tasks such as spotting events or anomalies. Aneffective way to represent the communication activity is via a dynamic graph where the entities are considered to be nodes, and each communication event (or more simply event) to be represented by a set of connecting edges that appear at a specific time interval.

Real-life mobile phone data may contain noisy instances, which is a fundamental issue for building a prediction model with many potential negative consequences. The complexity of the inferred model may increase, may arise overfitting problem, and thereby the overall prediction accuracy of the model may decrease. In this paper, we address these issues and present a robust prediction model for real-life mobile phone data of individual users, in order to improve the prediction accuracy of the model. In our robust model, we first effectively identify and eliminate the noisy instances from the training dataset by determining a dynamic noise threshold using naive Bayes classifier and laplace estimator, which may differ from user-to-user according to their unique behavioral patterns. After that, we employ the most popular rule-based machine learning classification technique, i.e., decision tree, on the noise-free quality dataset to build the prediction model. Experimental results on the real-life mobile phone datasets (e.g., phone call log) of individual mobile phone users, show the effectiveness of our robust model in terms of precision, recall and f-measure.

Franz Schubert composed his Symphony No.8 in 1822, but never completed it, making only two movements along with an outline of a third. Nearly 200 years later, Huawei, Emmy-awarding composer Lucas Cantor, and artificial intelligence (A.I.) inside the Mate 20 Pro smartphone have done what the renowned composer didn't. They've finished the unfinished symphony. The project is a continued illustration of not only the power of Huawei's Kirin 980 processor and Dual-Neural Processing Unit (NPU) artificial intelligence accelerator, but also the potential for using A.I. in varied creative projects. We're familiar with A.I. modes on smartphone cameras, and Huawei has previously demonstrated the power and speed of its A.I. in a self-driving car, where a phone was used to identify and help avoid obstacles.

Intelligent connectivity is the combination of high-speed, low-latency 5G networks, cutting-edge artificial intelligence (AI) and the linking of billions of devices through the Internet of Things (IoT). As these three revolutionary technologies combine they will enable transformational new capabilities in transport, entertainment, industry and public services, and much more beyond. As operators expand beyond provision largely of network access to facilitation of holistic services, they are rapidly bringing into view a world of technological ease and sophistication which not long ago still seemed a long way off. The GSMA estimates that, by 2025, there will be 25 billion connected devices. This hyperconnectivity will be enabled by undisturbed mobile broadband, which will make the number of connected devices communicating with one other will be virtually limitless.

While the objective in traditional multi-armed bandit problems is to find the arm with the highest mean, in many settings, finding an arm that best captures information about other arms is of interest. This objective, however, requires learning the underlying correlation structure and not just the means. Sensors placement for industrial surveillance and cellular network monitoring are a few applications, where the underlying correlation structure plays an important role. Motivated by such applications, we formulate the correlated bandit problem, where the objective is to find the arm with the lowest mean-squared error (MSE) in estimating all the arms. To this end, we derive first an MSE estimator based on sample variances/covariances and show that our estimator exponentially concentrates around the true MSE. Under a best-arm identification framework, we propose a successive rejects type algorithm and provide bounds on the probability of error in identifying the best arm. Using minimax theory, we also derive fundamental performance limits for the correlated bandit problem.

Tech giant Huawei's president has denied the firm has any links to Chinese spying operations. In a letter to the House of Commons Science and Technology Committee, the firm's president Ryan Ding insisted the firm was not involved with such practices. But a 2012 US House Intelligence Committee report outlined Huawei's links to the Chinese state, has since been picked up by other western governments, including Australia, Germany and the UK. FBI Director Christopher Wray has also suggested that the company's smartphones could be used to "maliciously modify or steal information." But Mr Ding insisted that Huawei had never and would never assist any country in gathering intelligence on other countries.

Erbium-doped fiber amplifier (EDFA) is an optical amplifier/repeater device used to boost the intensity of optical signals being carried through a fiber optic communication system. A highly accurate EDFA model is important because of its crucial role in optical network management and optimization. The input channels of an EDFA device are treated as either on or off, hence the input features are binary. Labeled training data is very expensive to collect for EDFA devices, therefore we devise an active learning strategy suitable for binary variables to overcome this issue. We propose to take advantage of sparse linear models to simplify the predictive model. This approach simultaneously improves prediction and accelerates active learning query generation. We show the performance of our proposed active learning strategies on simulated data and real EDFA data.

Dr. Graham Allison -- a specialist in national security at Harvard, where he has taught for five decades -- tells me: "The story beneath the story is the Great Rivalry between a meteorically rising China and a ruling U.S." The lead story of last Sunday's New York Times ("In 5G Race With China, U.S. Pushes Allies to Fight Huawei") trumpets the contest over 5G cellular networks, which exponentially accelerate online speed and ubiquity. Be smart, from Axios chief tech correspondent Ina Fried: Often forgotten is how much China and the U.S. still need one another.