These scenarios are impossible or prohibitively expensive on current cellular networks, but they should be feasible with the next generation of wireless connectivity, 5G. It promises to be 10 to 20 times faster than today's cell-phone networks," Elizabeth Woyke, MIT Technology Review "With worldwide 5G revenues estimated at €225 billion in 2025, 5G is a key asset for Europe to compete in the global market and its cybersecurity is crucial for ensuring the strategic autonomy of the Union. On this basis, Member States should update existing security requirements for network providers and include conditions for ensuring the security of public networks, especially when granting rights of use for radio frequencies in 5G bands," European Council conclusions on a common EU approach to the security of 5G networks "Europe has long been one of the world's foremost innovation hubs. To continue holding its own in an increasingly competitive global market, we believe stakeholders across the region need to unite to establish some key initiatives," William Xu, Huawei's Director of the Board and President of the Institute of Strategic Research Technology giant Huawei and Europe has a long story together. Huawei's presence in Europe started some 20 years ago and, since then, the company has been establishing itself as a driver of innovation in the deployment of critical technological infrastructure of ICTs, collaborating with governments across the European Union as well as positioning itself as a leader in sales of peripherals and devices. This story continues as the collaboration between the Chinese telecom and Europe's institutions are about to get boosted by the announcement of a whole array of joint programs and innovative projects in the areas of ICT such as 5G deployment, AI and Innovation 2.0. "Huawei has been operating in Europe for many years.

Today marked the kickoff of Xiaomi's annual Mi Developer conference in Beijing, and the tech giant wasted no time in announcing updates across its AI portfolio. It took the wraps off the latest release of Mobile AI Compute Engine (MACE), its open source machine learning framework, and it demoed an improved version of its Xiao AI voice assistant (Xiao AI 3.0). Xiao AI, which Xiaomi says is used by 49.9 million users each month, will soon support multi-turn conversations à la Alexa Conversations and Google's Continued Conversation. This will be enabled on select phones, including the Xiaomi Mi 9 Pro and the Xiaomi Mi 9 via a software update, and it will allow for interruptions of the assistant at any time with new requests or commands. Xiao AI 3.0 also boasts improved voice shortcut functionality and a voice reply feature that will let users respond to incoming calls with transcribed text messages. That's in addition to a command to find missing devices, like smartphones, and a male voice option.

Due to digital developments, new platforms and the wide amount of data in circulation, the Telco, Media & Entertainment industry has been reinventing itself to develop synergies among the content, environment and consumers. Data Analytics & Artificial Intelligence solutions offer a set of useful and reliable insights which translate into an effective ally for high-performance development and competitive strategy. Capitalize on digital market opportunities by responding to the real needs and consumers motivations across different channels. Access to business indicators capable to manage a large volume of information in different areas, and simultaneously, aggregates and analyses data quickly and efficiently.

Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations (ABSs) to assist terrestrial infrastructure for keeping wireless connectivity in various emergency scenarios. To maximize the coverage rate of N ground users (GUs) by jointly placing multiple ABSs with limited coverage range is known to be a NP-hard problem with exponential complexity in N. The problem is further complicated when the coverage range becomes irregular due to site-specific blockage (e.g., buildings) on the air-ground channel in the 3-dimensional (3D) space. To tackle this challenging problem, this paper applies the Deep Reinforcement Learning (DRL) method by 1) representing the state by a coverage bitmap to capture the spatial correlation of GUs/ABSs, whose dimension and associated neural network complexity is invariant with arbitrarily large N; and 2) designing the action and reward for the DRL agent to effectively learn from the dynamic interactions with the complicated propagation environment represented by a 3D Terrain Map. Specifically, a novel two-level design approach is proposed, consisting of a preliminary design based on the dominant line-of-sight (LoS) channel model, and an advanced design to further refine the ABS positions based on site-specific LoS/non-LoS channel states. The double deep Q-network (DQN) with Prioritized Experience Replay (Prioritized Replay DDQN) algorithm is applied to train the policy of multi-ABS placement decision. Numerical results show that the proposed approach significantly improves the coverage rate in complex environment, compared to the benchmark DQN and K-means algorithms.

This program unlocks a new chapter for the computing industry in Europe. According to Jiang Tao, VP of Intelligent Computing BU, "Huawei is committed to investing in the AI computing industry in Europe, enabling enterprises and individual developers to leverage the Ascend AI series products for technological and business innovation. Over the next 5 years, Huawei plans to invest 100 million euros in the AI Ecosystem Program in Europe, helping industry organizations, 200,000 developers, 500 ISV partners, and 50 universities and research institutes to boost innovation." First, Huawei will work with partners to shape the AI industry in Europe. Second, Huawei will develop joint solutions with ISV partners.

--The virtualization of compute and network resources enables an unseen flexibility for deploying network services. A wide spectrum of emerging technologies allows an ever-growing range of orchestration possibilities in cloud-based environments. But in this context it remains challenging to rhyme dynamic cloud configurations with deterministic performance. The service operator must somehow map the performance specification in the Service Level Agreement (SLA) to an adequate resource allocation in the virtualized infrastructure. We propose the use of a VNF profile to alleviate this process. This is illustrated by profiling the performance of four example network functions (a virtual router, switch, firewall and cache server) under varying workloads and resource configurations. We then compare several methods to derive a model from the profiled datasets. We select the most accurate method to further train a model which predicts the services' performance, in function of incoming workload and allocated resources. Our presented method can offer the service operator a recommended resource allocation for the targeted service, in function of the targeted performance and maximum workload specified in the SLA. This helps to deploy the softwarized service with an optimal amount of resources to meet the SLA requirements, thereby avoiding unnecessary scaling steps. HE advancements in the domain of cloud computing, Software Defined Networking (SDN) and Network Function Virtualization (NFV) enable a unseen flexibility and pro-grammability of both compute and network configurations. By softwarizing network functions, we move away from dedicated hardware based, monolithic systems to a virtualized solution for offering telecom services. The service is decomposed into multiple microservices which each get an allocated share of resources such as CPU time, memory access or network bandwidth. Typical tasks involved in network services include packet forwarding, routing, inspection or any other form of network traffic processing. Beyond the application layer, the deeper layers of the network traffic are checked or manipulated in a chained configuration. This means that network traffic is sequentially steered through a, possibly lengthy, chain of processors such as routers, firewalls, load-balancers or proxy-servers. In the NFV domain, the main aim is to provide softwarized solutions for each of those network functions, which can be deployed on commercial-of-the-shelf (COTS) servers. Ideally, equally high performance is expected compared to rigid, dedicated hardware middleboxes, but at a lower cost, higher flexibility regarding scaling, configuration and less prone to vendor and technology lock-in. At deployment time of the network service, an estimation of the required capacity and related resource allocation needs to be made. The performance contract is given in the Service Level Agreement (SLA) and should be translated to the required resources.

In this paper we propose a highly efficient and very accurate method for estimating the propagation pathloss from a point x to all points y on the 2D plane. Our method, termed RadioUNet, is a deep neural network. For applications such as user-cell site association and device-to-device (D2D) link scheduling, an accurate knowledge of the pathloss function for all pairs of locations is very important. Commonly used statistical models approximate the pathloss as a decaying function of the distance between the points. However, in realistic propagation environments characterized by the presence of buildings, street canyons, and objects at different heights, such radial-symmetric functions yield very misleading results. In this paper we show that properly designed and trained deep neural networks are able to learn how to estimate the pathloss function, given an urban environment, very accurately and extremely quickly. Our proposed method generates pathloss estimations that are very close to estimations given by physical simulation, but much faster. Moreover, experimental results show that our method significantly outperforms previously proposed methods based on radial basis function interpolation and tensor completion.

Aerendir Mobile Inc. will merge its mathematical deep learning cores and AI infrastructure with innovative SiFive, Inc., RISC-V Core IP to enable a new low-cost board format for deep learning. This combined, unique approach will radically decrease the cost of true AI, allowing it to be enabled at the IoT Edge and End Point inside affordable devices. Aerendir and SiFive expect that the IoT market, bolstered by future 5G networks, will require the most cost-effective high-end distributed learning capabilities. As data collection continues to grow and outstrip the ability of datacenters to store, process and analyze new devices at the edge, end point can help to make accurate machine learning decisions. Local data analysis reduces network congestion and latency, improving local device performance and helping to send important data to the cloud for further analysis.

Nyansa added support for Cisco, Hewlett Packard Enterprise (HPE), Meraki, Juniper, and Alcatel Lucent hardware to its Voyance AIOPs platform in an effort to eliminate what it's calling IT tool glut. The company claims that by using its software, customers can consolidate their network monitoring and telemetry into a single dashboard and eliminate monitoring tools like Cisco Prime, Aruba Airwave, or SolarWinds. "Customers don't want to have to learn, deploy, get trained on, and pay for a bunch of different performance monitoring systems," said David Callisch, VP of marketing at Nyansa. "They want something that's more modern, that provides more intelligence and analytics to allow them to become more proactive." Callisch added that the problem is compounded for enterprises that use networking equipment from multiple vendors, each of which would normally require its own performance monitoring tools.

The world's largest drone manufacturer has announced plans for a new smartphone app that lets users identify mysterious drones flying around their neighborhood. Developed by DJI, the Shenzhen based drone giant, the unnamed app is targeted for a release in early 2020 pending approval by government regulators. The app will have a range of around .6 miles using WiFi Aware, a new communication protocol that allows WiFi-enabled devices to communicate with one another. DJI announced the new app at the United Nations-sponsored Drone Enable conference in Montreal this week. 'We've created a remote identification solution that works with what people already have,' DJI's Brendan Schulman told Reuters.