Existing fuzzy neural networks (FNNs) are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep fuzzy neural network, namely deep evolving fuzzy neural networks (DEVFNN). Fuzzy rules can be automatically extracted from data streams or removed if they play little role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely Generic Classifier (gClass), drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of input space dimension due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using six datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four state-of the art data stream methods and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy.

Transaction Network Services (TNS) has been around for decades, and as one of the largest independent providers of inter-carrier call signaling and routing, it's an established player in telecom. But it's that long-time positioning that's helping it compete in the wild, wild west of the robocall detection business. This week, the company announced that its Neighbor Spoofing feature is enabling wireless carriers to protect their subscribers from the robocall tactic that uses local area codes or other means to make the consumer think the call is originating in their local area. The thinking is, if a call matches or closely matches their area code, they're more likely to trust the call is real and pick up. Carriers will use messages like "Potential spam" or "Likely spam" to let their customers know when a call is coming from a bad number so they don't pick up.

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that an edge-based deployment can also be used as an enabler of advanced Machine Learning (ML) applications in cellular networks, thanks to the balance it strikes between a completely distributed and a centralized approach. First, we will present an edge-controller-based architecture for cellular networks. Second, by using real data from hundreds of base stations of a major U.S. national operator, we will provide insights on how to dynamically cluster the base stations under the domain of each controller. Third, we will describe how these controllers can be used to run ML algorithms to predict the number of users, and a use case in which these predictions are used by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that prediction accuracy improves when based on machine learning algorithms that exploit the controllers' view with respect to when it is based only on the local data of each single base station. The next generation of cellular networks (5G) is being designed to satisfy the massive growth in capacity demand, number of connections and the evolving use cases of a connected society for 2020 and beyond [1]. Michele Polese and Michele Zorzi are with the Department of Information Engineering (DEI), University of Padova, Italy. In order to meet these requirements, a new approach in the design of the network is required, and new paradigms have recently emerged [3]. First, the densification of the network will increase the spatial reuse and, combined with the usage of mmWave frequencies, the available throughput. On the other hand, this will introduce new challenges related to mobility management [4].

Telecom service providers will use AI to manage and operate networks or many of their businesses won--t survive. That--s one of the key findings of our recent report, and it's based on the simple economics of price, cost and profitability. Telecom is a capital-intensive business with high fixed costs, which puts pressure on service providers to control variable costs, especially human capital. This has always been an issue, but recently it--s getting worse. In 2017, Tom Nolle of CIMI Corporation estimated that many CSPs crossed the point where revenue per bit was lower than cost per bit in 2017. Threatened by fast and highly efficient web-scale companies, service providers are straining under the challenge posed by digital transformation.

Curtin University, in Western Australia, will be working with Optus Business as they form a research group that will focus on the impact of artificial intelligence (AI) on regional telecommunications, higher education and the urban environment. According to the report made by the University, an artificial intelligence research group will be formed from the five-year alliance. The group will be embedded in the School of Electrical Engineering, Computing and Mathematical Sciences at the University, having strong links to the Curtin Institute for Computation. The excellent research, teaching and learning capabilities of the University will be synergised with the market-leading technology and infrastructure capabilities of the telco company and will be fully leveraged by the alliance of both. The research group will involve the appointment of an Optus Chair in Artificial Intelligence and three Optus Research Fellows focusing on applying artificial intelligence technologies in areas such as regional telecommunications, improving higher education student outcomes and the urban environment.

Abstract--We propose channel charting (CC), a novel framework in which a multi-antenna network element learns a chart of the radio geometry in its surrounding area. The channel chart captures the local spatial geometry of the area so that points that are close in space will also be close in the channel chart and vice versa. CC works in a fully unsupervised manner, i.e., learning is only based on channel state information (CSI) that is passively collected at a single point in space, but from multiple transmit locations in the area over time. The method then extracts channel features that characterize large-scale fading properties of the wireless channel. Finally, the channel charts are generated with tools from dimensionality reduction, manifold learning, and deep neural networks. The network element performing CC may be, for example, a multi-antenna base-station in a cellular system and the charted area in the served cell. Logical relationships related to the position and movement of a transmitter, e.g., a user equipment (UE), in the cell can then be directly deduced from comparing measured radio channel characteristics to the channel chart. The unsupervised nature of CC enables a range of new applications in UE localization, network planning, user scheduling, multipoint connectivity, handover, cell search, user grouping, and other cognitive tasks that rely on CSI and UE movement relative to the base-station, without the need of information from global navigation satellite systems. UTURE wireless communication systems must sustain a massive increase in traffic volumes, number of terminals, and reliability/latency requirements [2], [3]. C. Studer, S. Medjkouh, and E. Gönültaş are with the School of Electrical and Computer Engineering, Cornell University, Ithaca, NY; email: studer@cornell.edu, T. Goldstein is with the Department of Computer Science, University of Maryland, College Park, MD; email: tomg@cs.umd.edu O. Tirkkonen was a visiting professor at the School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, and is now at the School of Electrical Engineering, Aalto University, Finland; email: olav.tirkkonen@aalto.fi The work of CS, SM, and EG was supported in part by Xilinx Inc., and by the US NSF under grants ECCS-1408006, CCF-1535897, CAREER CCF-1652065, and CNS-1717559.

It's been the bane of any cable TV subscriber. They get frustrated with their provider and want to switch but have few alternatives available. For residents of Los Angeles, Sacramento, Houston and Indianapolis this year, 5G could be change that. These are the four markets Verizon will be testing 5G later this year. The 5G networks have been touted as the next big thing for wireless consumers, a way for them to get faster service, with videos that will open immediately and downloads that will take seconds instead of minutes.

"She was like, 'You're not your normal, cheery, bubbly self,' " Mr. Witkowski said. " 'You're not using exclamation points.' " She told him she felt his emails came off as more demanding than usual. "I didn't really know how to react," he said. Exclamation points are stressing people out. Years of rampant use have both diluted the punctuation mark's meaning and inflated its significance.

CELCOM Axiata Bhd inked an agreement with Huawei Technologies (Malaysia) Sdn Bhd to apply the Cloud-based Digitised Operation Platform, Software as a Service (SaaS) solution. Celcom will be the first in the country to adopt full suite Cloud-based Operation Support Service (OSS) system to accelerate agility in their automation and intelligence of network management, and pave the way for their journey towards becoming a digital company. The Digitised Operation Platform brings together Artificial Intelligence (AI) and Machine Learning technology powered by Huawei's Operation Web Services (OWS) suite, to enhance Celcom's capabilities in managing increasingly complex networks and services. It also enables Celcom to transform their daily operations from reactive to proactive and predictive, and further solidify their drive to deliver an awesome customer experience. The agreement to acquire the platform for Celcom's network operation was signed by Celcom Axiata chief technology officer Amandeep Singh and Huawei Technologies (Malaysia) chief executive officer Baker Zhouxin. Through this partnership, Huawei aims to leverage on its Digitised Operation AUTomation & INtelligence Services Solution (AUTIN), and share global experiences with Celcom to achieve a visualised, automated and intelligent network operation.

Tuning cellular network performance against always occurring wireless impairments can dramatically improve reliability to end users. In this paper, we formulate cellular network performance tuning as a reinforcement learning (RL) problem and provide a solution to improve the signal to interferenceplus-noise ratio (SINR) for indoor and outdoor environments. By leveraging the ability of Q-learning to estimate future SINR improvement rewards, we propose two algorithms: (1) voice over LTE (VoLTE) downlink closed loop power control (PC) and (2) self-organizing network (SON) fault management. The VoLTE PC algorithm uses RL to adjust the indoor base station transmit power so that the effective SINR meets the target SINR. The SON fault management algorithm uses RL to improve the performance of an outdoor cluster by resolving faults in the network through configuration management. Both algorithms exploit measurements from the connected users, wireless impairments, and relevant configuration parameters to solve a non-convex SINR optimization problem using RL. Simulation results show that our proposed RL based algorithms outperform the industry standards today in realistic cellular communication environments. The tuning of network performance aims at providing the end user with excellent quality of experience (QoE). With over 1.5 billion smartphones used globally, demand patterns have The authors are with the Wireless Networking and Communications Group, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA email: {faris.mismar, This paper is an expanded journal version of [1] and [2]. 2 Demands have shifted towards reliable packetized voice and applications with higher data rates and lower latencies [4].