SHENZHEN: Telecom industry is not in a healthy state but artificial intelligence will help networks to solve structure issues at root and enable self-healing and self-optimizing of networks, according to Eric Xu, Rotating Chairman, Huawei. Speaking at HAS 2018, Xu explained how Huawei has been working on its SoftCOM AI network which is a new architecture enabling automation and digitization of networks. "Less than 10% organizations are actually able to create value from AI," said Xu. Huawei will continue to focus on ICT infrastructure and smart devices and will continue to invest in broadband and home network solutions, Xu added. "..we'll continue to make further investment in big data and network"." Xu told that Huawei will be applying AI to all its products and solutions and even use it for internal operations. "We aim to bring AI to cloud, network and smart devices and use it as extensively as possible," he stated. Talking about data and its applications, Xu said that the government and organizations have a very patchy situation when it comes to data utilization. "..data is not like oil for many enterprises because it's not processed properly." Xu also touched upon the company's situation in United States when asked about it, as it had previously received setbacks in the country. "There have been so many stories between US and China that one would need an entire day to explain.

After surpassing $1 billion in IoT revenue in FY2017, Qualcomm is announcing new product families purpose-built for IoT applications. The company began by announcing a new family of IoT chipsets, the QCS603 and QCS605, along with software and reference designs, all dubbed the Qualcomm Vision Intelligence Platform. The platform brings the image and artificial intelligence (AI) processing capabilities found on its Snapdragon chipsets for premium smartphones to a wide range of consumer and industrial applications. With the transition from connected devices to intelligent devices, there is a push to bring AI processing from the cloud to the devices we use, commonly referred to as the "edge" or "edge devices" referring to the edge of the network. Bringing AI to the edge reduces cloud and connectivity bandwidth requirements while increasing security and system performance. Vision plus AI processing have become critical elements in autonomous cars and many other applications that require local processing.

WASHINGTON – China is slightly ahead of South Korea and the United States in the race to develop fifth generation wireless networks, or 5G, a U.S. study showed Monday. The study released by the CTIA, a U.S.-based industry association of wireless carriers, suggested that the United States is lagging in the effort to deploy the superfast wireless systems that will be needed for self-driving cars, telemedicine and other technologies. The report prepared by the research firm Analysys Mason found that all major Chinese providers have committed to specific launch dates and the government has committed to allocate spectrum for the carriers. The 10-nation study said the U.S. is in the "first tier" of countries in preparing deployment of 5G, along with China, South Korea and Japan. In the second tier are key European markets, including France, Germany and Britain, with Singapore, Russia and Canada in the third tier.

NIST's new Large Antenna Positioning System (LAPS) has two robotic arms designed to position "smart" or adaptable antennas, which can be mounted on base stations that handle signals to and from huge numbers of devices. Future 5G systems will operate at higher frequencies and offer more than 100 times the data-carrying capacity of today's cellphones, while connecting billions of mobile broadband users in complex, crowded signal environments. Among its many special capabilities, the LAPS can test transmissions to and from antennas located on fast-moving mobile devices, which requires coordination between the timing of communication signals and robot motion. "Measurements of antenna signals are a great use for robotics," NIST electronics engineer Jeff Guerrieri said. "The robotic arms provide antenna positioning that would be constrained by conventional measurement systems."

This week in IoT, Qualcomm reveals efforts to have its chips in every single device, with two new models specifically designed for connected devices. There was cause for celebration among internet of things (IoT) device manufacturers this week with news that a'semi-infinite' supply of rare Earth minerals was located off the coast of Japan. The deposit – located entirely within Japan's maritime boundaries – is believed to contain 16m tonnes of minerals necessary to build IoT devices, mobile phones and even electric vehicles. With China holding much of the world's supply right now, the discovery of another major source of rare Earth minerals could help manufacturers to avoid the knock-on effects of a trade war. Not content with cornering the Android smartphone market, Qualcomm revealed this week that it is launching two new systems-on-a-chip for IoT.

Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be integrated into future cellular networks as new aerial mobile users. Providing cellular connectivity to UAVs will enable a myriad of applications ranging from online video streaming to medical delivery. However, to enable a reliable wireless connectivity for the UAVs as well as a secure operation, various challenges need to be addressed such as interference management, mobility management and handover, cyber-physical attacks, and authentication. In this paper, the goal is to expose the wireless and security challenges that arise in the context of UAV-based delivery systems, UAV-based real-time multimedia streaming, and UAV-enabled intelligent transportation systems. To address such challenges, artificial neural network (ANN) based solution schemes are introduced. The introduced approaches enable the UAVs to adaptively exploit the wireless system resources while guaranteeing a secure operation, in real-time. Preliminary simulation results show the benefits of the introduced solutions for each of the aforementioned cellular-connected UAV application use case.

The market for AI is so overhyped, virtually anybody looking for a fast buck can repackage an old abacus and sell it as a "machine learning" platform. Misinformed people buy the idea and the mounting frustration makes it extremely difficult for legitimate AI companies to get their message heard above the din. To successfully stand out in a crowded market, creative marketers have found it critical to create unconventional educational content and enlist the support of credible B2B influencers in their space. In the ever-shifting AI solutions ecosystem, even expert-led and well-thought-out marketing campaigns can fail -- and often do. To reduce costs and save time, marketers of AI products need to learn as early as possible what works.

Leading Finnish telecommunications company Nokia has announced that it has bolstered its Customer Experience Index (CEI) with the unveiling of its latest Cognitive Analytics for Customer Insight software. Using machine learning and intelligent automation, the software will be able to provide transformational real time and personalized consumer experiences to business, IT and engineering companies. "Nokia CEI now taps advanced machine learning and deep learning algorithms co-developed with Nokia Bell Labs to provide new levels of prediction and automation capabilities to improve the subscriber experience," Nokia explains. "The algorithms optimize themselves over time, decreasing the time required for the initial tuning of the index from months to days, and delivering a far more accurate view of subscriber satisfaction." This will allow service providers to quickly identify issues, up to six times faster, and in turn prioritize improvements based on their unique circumstances, better delivering revenue-generating services.

Spurred by the enthusiasm surrounding the "Big Data" paradigm, the mathematical and algorithmic tools of online optimization have found widespread use in problems where the trade-off between data exploration and exploitation plays a predominant role. This trade-off is of particular importance to several branches and applications of signal processing, such as data mining, statistical inference, multimedia indexing and wireless communications (to name but a few). With this in mind, the aim of this tutorial paper is to provide a gentle introduction to online optimization and learning algorithms that are asymptotically optimal in hindsight - i.e., they approach the performance of a virtual algorithm with unlimited computational power and full knowledge of the future, a property known as no-regret. Particular attention is devoted to identifying the algorithms' theoretical performance guarantees and to establish links with classic optimization paradigms (both static and stochastic). To allow a better understanding of this toolbox, we provide several examples throughout the tutorial ranging from metric learning to wireless resource allocation problems.

Instead of continuing to repurpose existing processors for smart home devices, Qualcomm has unveiled two new chipsets built specifically for IoT gadgets. The QCS605 and QCS603 are 10nm system-on-chips (SoCs) that will power things like 360-degree cameras, robot vacuums and smart displays from companies like Ricoh Theta and Kedacom. Qualcomm also launched a Vision Intelligence Platform today that provides frameworks like the company's AI Engine, and the Snapdragon Neural Processing Engine (NPE) to enhance on-device machine learning. It'll also offer SDKs for camera processing and computer vision so that companies who buy these processors can more easily create applications for their products. Some of the uses that the Vision Intelligence Platform will support include object detection, tracking, obstacle avoidance and facial recognition.