Digital technology adoption appeared to be optional for decades in many industries, but no more. In fact, if your job does not require a substantial amount of creativity, deriving complex conclusions or physically moving things, you have every reason to check your back. So, what is different today? A number of factors come together to accelerate the current situation from slower evolutionary speed to faster revolutionary velocity. While in the first technical transition from analogue signal transmission to digital, the digitization, distance or reproduction did not mean a degradation in quality any longer. Digital telephone lines, music CDs or slower data transfers allowed the dissemination of information over long distances or without a decrease in quality.

We read with interest this week that Vodafone has just announced that they have developed the world's first Radio Positioning System (RPS) for drones. This new control system uses a 4G modem and SIM embedded within each drone to enable real-time tracking of the drone with up to 50 metre accuracy. The system is intended for use by drone operators helping them to identify and stay clear of unauthorized airspace zones. Authorized bodies such as air traffic control will be able to use the technology to repel unauthorized intrusions into controlled airspace. In addition to position monitoring the system can help drone pilots with beyond line of sight control.

Wi-Fi) and the second/third/fourth generation (2/3/4G) mobile network, the number of mobile phones, which is 7.74 billion, 103.5 per 100 inhabitants all over the world in 2017, is rising dramatically [1]. Nowadays, mobile phone can not only send voice and text messages, but also easily and conveniently access the Internet which has been recognized as the most revolutionary development of Mobile Internet (M-Internet). Meanwhile, worldwide active mobile-broadband subscriptions in 2017 have increased to 4.22 billion, which is 9.21% higher than that in 2016 [1]. Figure 1 shows the numbers of mobile-cellular telephone and active mobile-broadband subscriptions of the world and main districts from 2010 to 2017. The numbers which are up to the bars are the mobile-cellular telephone or active mobile-broadband subscriptions (million) in the world of the year which increase each year. Under the M-Internet, various kinds of content (image, voice, video, etc.) can be sent and received everywhere and the related applications emerge to satisfy people's requirements, including working, study, daily life, entertainment, education, healthcare, etc. In China, mobile applications giants, i.e., Baidu, Alibaba and Tencent, held 78% of M-Internet online time per day in App which was about 2,412 minutes in 2017 [2]. This figure indicates that M-Internet has entered a rapidly growth stage.

You may have read recently that Vodafone, Audi and Nokia are going to set up a 4G mobile network on the moon next year. It's happening, and it forms part of a private lunar mission to explore the surface of the moon. What you might not know is that the decision to set up the network with 4G technology was a deliberate choice over 5G, which is gathering momentum, if not yet in the roll out phase, here on Earth. Evidently, for space, 5G is not yet up to the task.1 Yet down here for us Earthlings, 5G is very much the talk of the telecommunications and technology worlds, with some communications specialists arguing it could be one of the most important developments in human history.2 But what exactly is different about this new wave of wireless technology?

This work demonstrates the potential of deep reinforcement learning techniques for transmit power control in emerging and future wireless networks. Various techniques have been proposed in the literature to find near-optimal power allocations, often by solving a challenging optimization problem. Most of these algorithms are not scalable to large networks in real-world scenarios because of their computational complexity and instantaneous cross-cell channel state information (CSI) requirement. In this paper, a model-free distributed dynamic power allocation scheme is developed based on deep reinforcement learning. Each transmitter collects CSI and quality of service (QoS) information from several neighbors and adapts its own transmit power accordingly. The objective is to maximize a weighted sum-rate utility function, which can be particularized to achieve maximum sum-rate or proportionally fair scheduling (with weights that are changing over time). Both random variations and delays in the CSI are inherently addressed using deep Q-learning. For a typical network architecture, the proposed algorithm is shown to achieve near-optimal power allocation in real time based on delayed CSI measurements available to the agents. This work indicates that deep reinforcement learning based radio resource management can be very fast and deliver highly competitive performance, especially in practical scenarios where the system model is inaccurate and CSI delay is non-negligible.

If there is one industry that should be leveraging data in every way possible, it's telecommunications. The telecommunications industry services billions of people each day, generating massive amounts of data. Though not many telecom companies are leveraging this data, the introduction of data science, machine learning, and artificial intelligence in this industry are inevitable. A study by McKinsey, Telcos: The Untapped Promise of Big Data, based on a survey of leaders from 273 telecom organizations, found that most companies had not yet seriously leveraged the data at their disposal to increase profits. And only 30 percent say they have already made investments in big data.

Mobile US named Nokia to supply it with $3.5 billion in next-generation 5G network gear, the firms said on Monday, marking the world's largest 5G deal so far and concrete evidence of a new wireless upgrade cycle taking root. No.3 U.S. mobile carrier T-Mobile - which in April agreed to a merger with Sprint to create a more formidable rival to U.S. telecom giants Verizon and AT&T - said the multiyear supply deal with Nokia will deliver the first nationwide 5G services. The T-Mobile award is critical to Finland's Nokia, whose results have been battered by years of slowing demand for existing 4G networks and mounting investor doubts over whether 5G contracts can begin to boost profitability later this year. But cash-strapped telecom operators around the world have been gun-shy over committing to commercial upgrades of existing networks, with many seeing 5G technology simply as a way to deliver incremental capacity increases instead of new features. Advances in mobile data networks in the next decade could bring a number of benefits, according to the White House.

Imagine you were traveling on business and you just arrived into town and had an emergency. Your luggage was sent to the wrong city or you were late for a critical meeting and you needed directions to the convention center. The check-in counter has a dozen guests waiting and the concierge is busy. Well, you may be in luck, because a four-foot robot with an open tablet computer and no waiting line is standing in the corner, and just may be able to come to your assistance. Pepper is an intelligent assistant that costs a bit more than a smart speaker, and can engage you in ways that go beyond ordering pizza or playing your favorite Top 40 tunes on the radio.

Mobile networks possess information about the users as well as the network. Such information is useful for making the network end-to-end visible and intelligent. Big data analytics can efficiently analyze user and network information, unearth meaningful insights with the help of machine learning tools. Utilizing big data analytics and machine learning, this work contributes in three ways. First, we utilize the call detail records (CDR) data to detect anomalies in the network. For authentication and verification of anomalies, we use k-means clustering, an unsupervised machine learning algorithm. Through effective detection of anomalies, we can proceed to suitable design for resource distribution as well as fault detection and avoidance. Second, we prepare anomaly-free data by removing anomalous activities and train a neural network model. By passing anomaly and anomaly-free data through this model, we observe the effect of anomalous activities in training of the model and also observe mean square error of anomaly and anomaly free data. Lastly, we use an autoregressive integrated moving average (ARIMA) model to predict future traffic for a user. Through simple visualization, we show that anomaly free data better generalizes the learning models and performs better on prediction task.

A group of computer vision researchers from ETH Zurich want to do their bit to enhance AI development on smartphones. To wit: They've created a benchmark system for assessing the performance of several major neural network architectures used for common AI tasks. They're hoping it will be useful to other AI researchers but also to chipmakers (by helping them get competitive insights); Android developers (to see how fast their AI models will run on different devices); and, well, to phone nerds -- such as by showing whether or not a particular device contains the necessary drivers for AI accelerators. The app, called AI Benchmark, is available for download on Google Play and can run on any device with Android 4.1 or higher -- generating a score the researchers describe as a "final verdict" of the device's AI performance. AI tasks being assessed by their benchmark system include image classification, face recognition, image deblurring, image super-resolution, photo enhancement or segmentation.