Beam alignment (BA) is to ensure the transmitter and receiver beams are accurately aligned to establish a reliable communication link in millimeter-wave (mmwave) systems. Existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair, which incurs significant BA latency on the order of seconds in the worst case. In this paper, we develop a learning algorithm to reduce BA latency, namely Hierarchical Beam Alignment (HBA) algorithm. We first formulate the BA problem as a stochastic multi-armed bandit problem with the objective to maximize the cumulative received signal strength within a certain period. The proposed algorithm takes advantage of the correlation structure among beams such that the information from nearby beams is extracted to identify the optimal beam, instead of searching the entire beam space. Furthermore, the prior knowledge on the channel fluctuation is incorporated in the proposed algorithm to further accelerate the BA process. Theoretical analysis indicates that the proposed algorithm is asymptotically optimal. Extensive simulation results demonstrate that the proposed algorithm can identify the optimal beam with a high probability and reduce the BA latency from hundreds of milliseconds to a few milliseconds in the multipath channel, as compared to the existing BA method in IEEE 802.11ad.

Global telecom giant Huawei will continue to go ahead with its India strategy related to artificial intelligence technology as planned, unfazed by the pressure tactics by America through ban and escalating US-China trade war. However, it maintained silence on 5G roll-out plans in the country. We are going to develop Atlas card and servers based on the standard processors, and we are also going to work on MDC (mobile data centre) based on standard processes as well," Huawei Rotating Chairman Eric Xu said. Xu was responding to a question on what will be the company's India strategy following the looming uncertainty over 5G roll-out, at the launch of its AI processor Ascend 910 and AI computing framework MindSpore in Shenzhen recently. The Chinese telecom equipment maker has caught itself in a bitter battle with the US, which has provided a 90-day reprieve to continue doing business as usual with the US suppliers. The US imposed the ban on the company over concerns of security. Washington has also been putting pressure on other countries to restrict operations of the Chinese telecom firm. Restricting the replies around the company's AI plans, Xu said, "Our AI strategy is not necessarily tied to 5G.

First announced at BUILD 2018, Microsoft and Qualcomm have debuted their Vision AI Developer Kit for building computer vision applications. The kit is built on Qualcomm's Vision Intelligence 300 Platform and can run AI models locally or in the cloud using Microsoft's Azure ML and Azure IoT Edge platforms. The hardware runs Yocto Linux, uses a Qualcomm Snapdragon 603 chip, has 4GB of LDDR4X memory, and 64GB of storage. The camera is 8-megapixel, records in 4K, and captures audio using an array of four microphones. An SDK combining Visual Studio Code, a module which can recognise in excess of 183 unique objects, prebuilt Azure IoT deployment configurations, Python modules, and a Vision AI Developer kit extension for Visual Studio is available on GitHub.

Kryon, a leading robotic process automation (RPA) provider known for its business-centric approach and unique full-cycle automation solutions, announces its upcoming launch of AI Booster, a set of new artificial intelligence services based on Microsoft Azure's Cognitive Services, to create a unified RPA and AI experience that delivers smarter end-to-end deployment and management through a single, intuitive platform. The unique AI Booster will be included in Kryon's Automation Suite Version 19.4 expected in the Fall of 2019. Kryon's AI Booster will allow business users to deploy and manage AI technology by simply dragging and dropping these elements right into personal process workflows without having to develop complicated AI applications. By connecting seamlessly to Kryon's full-cycle Automation Suite, these advanced AI capabilities will create a superior, unified RPA and AI experience. "This exciting fusion of RPA and AI is a direct result of Kryon's ongoing strategic cooperation with Microsoft. This launch takes us to the next level of consciously feeding information and understanding an enterprise's data movements, generating more intelligent analysis and superior outcomes," said Harel Tayeb, CEO of Kryon.

--We study collaborative machine learning (ML) across wireless devices, each with its own local dataset. Offloading these datasets to a cloud or an edge server to implement powerful ML solutions is often not feasible due to latency, bandwidth and privacy constraints. Instead, we consider federated edge learning (FEEL), where the devices share local updates on the model parameters rather than their datasets. We consider a heterogeneous cellular network (HCN), where small cell base stations (SBSs) orchestrate FL among the mobile users (MUs) within their cells, and periodically exchange model updates with the macro base station (MBS) for global consensus. We employ gradient sparsification and periodic averaging to increase the communication efficiency of this hierarchical federated learning (FL) framework. We then show using CIF AR-10 dataset that the proposed hierarchical learning solution can significantly reduce the communication latency without sacrificing the model accuracy. V ast amount of data is generated today by mobile devices, from smart phones to autonomous vehicles, drones, and various Internet-of-things (IoT) devices, such as wearable sensors, smart meters, and surveillance cameras. Machine learning (ML) is key to exploit these massive datasets to make intelligent inferences and predictions. Most ML solutions are centralized; that is, they assume that all the data collected from numerous devices in a distributed manner is available at a central server, where a powerful model is trained on the data.

Microsoft and Qualcomm have released the Vision AI Developer Kit. The dev kit includes a camera and software required to work with multiple Azure platforms. With it, companies can use Azure IoT Edge and Azure Machine Learning. The kit is based on the Qualcomm Vision Intelligence Platform and powered by Qualcomm AI Engine. The dev kit was announced last year and was available in preview last summer, but it is now generally available to the public.

The 5th Huawei Asia-Pacific Innovation Day was held in Chengdu, China. This year's Innovation Day is themed "Innovation Enables Asia-Pacific Digitization". More than 200 representatives from government, industry and academia of Asia-Pacific countries and regions got together to discuss innovative 5G technologies and applications, sustainable development, as well as technology, humanity, and nature. As a ubiquitous technology, 5G is the cornerstone of a smart world in which everything is connected. Today, as we usher in the 5G era, we are also at a critical stage of digital transformation across industries worldwide.

Artificial intelligence (AI) workloads include megabytes of data and potentially billions of calculations. With advancements in hardware, it is now possible to run time-sensitive AI workloads on the edge while also sending outputs to the cloud for downstream applications. AI scenarios processed on the edge can facilitate important business scenarios, such as verifying if every person on a construction site is wearing a hardhat, or detecting whether items are out-of-stock on a store shelf. The combination of hardware, software, and AI models needed to support these scenarios can be difficult to organize. To remove this barrier, we announced a developer kit last year with Qualcomm, to accelerate AI inferencing at the intelligent edge.

Huawei presented at Hot Chips an AI accelerator it aims to scale from inference on wearables to training jobs in data centers. It also described systems based on them spanning SoCs for smartphones, cars, and cellular base stations as well as servers and a 512-petaflop cluster. The presentation showed state-of-the-art work in silicon, software and systems. In many respects Huawei appeared ahead of rivals such as Intel or even risk-taking startups such as Cerebras which is narrowly focused on data center training. The first generation of chips based on DaVinci cores were designed in just 11 months.

As artificial intelligence (AI) and machine learning become ubiquitous, we will soon be hard-pressed to find any industry not capitalizing on the benefits they can provide. Telecommunications is one of the fastest-growing industries as well as one that uses artificial intelligence and machine learning in many aspects of their business from enhancing the customer experience to predictive maintenance to improving network reliability. The largest telecoms in the world rely on artificial intelligence and machine learning in a number of ways. Here are the most common applications. Nearly every telecom uses artificial intelligence and machine learning to improve its customer service primarily by using virtual assistants and chatbots.