Japan-based mobile operator SoftBank, looking to improve radio access network (RAN) design as the process becomes more complex with the move to 5G, tested a network automation service from Ericsson which uses machine intelligence and big data analytics. The operator applied the service to dense urban clusters with multi-band complexity in the Tokai region. Ryo Manda, radio technology section manager at SoftBank, said the outcomes exceeded its expectations and it is implementing the design method in other areas. Ericsson said in a statement the foundation of the method is a thorough analysis of the actual radio network environment, for example taking cell coverage overlap, signal strength and receive diversity into consideration. The high number of possible relations between cells requires substantial computational power and state-of-the-art machine learning techniques.

Today on PowerChat, your leading converged telecommunications operator will focus on demystifying what is known as artificial intelligence. The company has found it imperative to share this knowledge with customers, readers and the entirety of the telecommunications fraternity against the background that the world is moving in the realm of interacting and experiencing the power of artificial intelligence (AI). Artificial Intelligence is real – whether in the home, car, business, when travelling around the globe and also on individuals' smartphones, tablets, computer and other smart devices that are digital and/or can connect onto the internet. Defined simply, artificial intelligence (AI) is the development of computers or technology capable of performing tasks that typically would require human intelligence. Global technology companies such as Google, Amazon, Apple and Samsung to name but a few, are making huge investments in AI that are already changing lives and gadgets, and laying the groundwork for a more AI-centric future.

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. In this paper, we consider MEC for a representative mobile user in an ultra-dense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between MU and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN) based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies.

South Africa is facing a shortage of data scientists -- a new breed of analytical data experts with the technical skills to solve complex problems. And because they straddle both the business and IT worlds, they're highly sought-after and well paid. The demand for data scientists is being driven by the emergence of big data -- that unwieldy mass of unstructured information that can no longer be ignored and forgotten. It's a potential gold mine for companies -- as long as there's someone who can dig in and unearth the business insights that no one thought to look for before. South African universities like Wits and UCT have introduced data science degrees at the master's level, but this is producing about 40 data scientists a year, far short of the number that the country's banks, insurers, retailers, health companies and telecommunications providers, among others, require.

The age of Artificial Intelligence (AI) is almost upon us. Rapid developments in machine learning have allowed us to build better, smarter machines that are capable of making decisions and handling tasks similar to humans. Some of these developments are also being implemented in mobiles to create the next generation of smarter phones. I attended the recent Huawei Global Analyst Summit in Shenzhen to speak with the heads of Huawei's development teams and find out more about the future of AI in mobiles. Huawei is a leading brand in mobile phone technology.

Subscribe to receive updates on Industry 4.0 The Fourth Industrial Revolution is upon us. The first three were based, respectively, on mechanization, mass production, and computing/automation; Industry 4.0 is all about the marriage of physical and digital technologies. Just as with the previous revolutions, Industry 4.0 is disrupting and redefining industries. This time, however, the revolution is progressing with unprecedented speed, driven by smart, connected technologies that are developing at an exponential rate.1 These technology innovations--including cloud computing and platform technologies, big data and analytics, mobile solutions, social and collaborative systems, Internet of Things (IoT) technology, and artificial intelligence (AI)--are fueling and accelerating a new era of digital business transformation. They're reshaping how organizations work, innovate, and create products--and enabling completely new kinds of products and services.2 They're spurring businesses to invent new business models and reimagine how they deliver value to their customers and markets. More broadly, industry boundaries are expanding and blurring, and relationships with business partners are being redefined. Yet too many organizations remain unprepared for the new revolution. A recent Deloitte Industry 4.0 study of C-level executives around the world indicates that, across all industries, only 14 percent of CXOs are "highly confident" that their organizations are ready to harness the changes associated with the new era.3

FRISCO, Texas--(BUSINESS WIRE)--Federos, the leading provider of next-generation, service management solutions for telecommunications service providers, managed service providers and enterprises, announced today the availability of an integrated module for its Assure1 solution that provides sophisticated event analytics and machine learning to help customers quickly and accurately pinpoint, analyze and resolve the root cause of service impacting events. Faster mean time to repair results in improved service quality and customer experience, and significant time and resource savings for operations teams. The event analytics module for Assure1 will be offered to customers as an add-on subscription solution. Today, when network and service problems happen, operations teams need to analyze millions of events and data transactions to understand where, when and why the problem occurred before they can fix it. Federos' Assure1 with Event Analytics eliminates and suppresses massive amounts of data noise that can distract operations and affect customer service.

The robot apocalypse has been tentatively scheduled for late 2019. Boston Dynamics, the SoftBank-owned robotics firm (that was once Alphabet's headache), will begin to commercialize its research next year. Marc Raibert, the company's founder, announced onstage at a TechCrunch robotics conference May 11 that Boston Dynamics will begin selling its dog-shaped robot, SpotMini, in 2019. It'll build about 100 robots over the next year, and is currently in the process of contracting manufacturers. The robot, which the company has been developing for the last couple years, is the smaller brother of Spot, a robot Boston Dynamics' researchers have been kicking since 2015. SpotMini weighs about 66 pounds and has a battery life of about 90 minutes, according to the company's website.

Enterprise machine learning within business organizations is all set to double by this year while smartphone usage is set to grow with sales reaching 1.85 billion per year by 2023, a new study has found. According to Deloitte Global's Technology, Media and Telecommunications Predictions, business organizations will likely double their use of machine learning technology by the end of 2018 as they seek to ramp up productivity. The report noted that the growth in new semiconductor chips will increase the use of machine learning, enabling applications to use less power, and at the same time become more responsive, flexible and capable. "We see governments in the GCC playing an active role in promoting the adoption of AI and Machine Learning," said Emmanuel Durou, Partner and Technology, Media and Telecommunications Leader, Deloitte, Middle East. Deloitte Global also predicted that live broadcast and events will generate over $545 billion in direct revenues in 2018.

The world is evolving around us. Chris Reece, who recently spoke about AI and network evolution to a packed house at Mobile World Congress in Barcelona, will be presenting an AI discussion focusing on use cases in the telecommunications industry. Learn how and why service providers are investing in AI to solve their toughest challenges. They are using AI to solve everything from infrastructure challenges to implementing automation. New technologies are fueling AI applications and accelerating adaptive, "self-everything" networks.