Audi is no stranger to developing networked, internet-savvy cars. It still believes it needs a boost, however. To that end, it just signed a memorandum of understanding with Huawei that will see the two companies collaborate on "intelligent connected vehicles." These cars need a fast, reliable data connection, Audi said, and Huawei's involvement is bound to help. They see the alliance helping for everything from autonomous driving through to online services. The two haven't provided specific goals or long-term roadmaps.

Huawei's SoftCOM AI solution introduces AI to All Cloud Networks. Designed to create self-driving network architecture, SoftCOM AI is a transformative solution that can help operators compete with OTT companies by using predictive AI to minimize network faults. Telecom services are divided into three tiers: device, network and IT infrastructure, and upper-layer applications. However, in today's telecom's landscape, cross-sector competition is threatening telco revenue models. Thanks to dramatic increases in network speeds, IT and Internet companies are offering cloud services in traditional telco territory: backbone networks, some MANs, IT infrastructure, and IT applications.

Fresh from fighting content filters in the EU, Google is working on the ultimate content filter– which seals the user off in a spam-free bubble. It's a nuisance caller detection feature built into Android, and it could have unintended consequences. The feature, spotted in recent commits to the Android Open Source Project (AOSP), offers to "screen unwanted calls with real-time audio transcription and quick responses". The commits were spotted by sharp-eyed devs at XDA here. Many phone apps already include a blacklist feature and third-party phone apps, such as Drupe and Truecaller, use caller ID to screen unwanted calls.

Nokia and China Mobile, the world's largest mobile carrier in terms of subscribers, have signed an Memorandum of Understanding (MoU) to investigate the potential of artificial intelligence (AI) and machine learning to optimize future networks and enable the delivery of new edge cloud and 5G services. Under the terms of the agreement, the two companies will jointly establish a laboratory in Hangzhou, China to develop the demo system to verify technology use cases using Nokia's 5G Future X architecture. The deal also stipulates that China Mobile will lead the research in terms of scenario selection, requirements confirmation, open API standardization and solution definition. Under the MoU, Nokia and China Mobile will work together to research the application of AI and machine learning to ensure any changes in demand are predicted and network resources are automatically allocated to meet all service demands with consistent high quality and reliability, the two firms said. At the new laboratory in Hangzhou, Nokia and China Mobile will foster an open Radio Access Network and 5G ecosystem working with third parties to leverage AI and machine learning and optimize networks for the delivery of services such as cloud virtual reality gaming.

Q: I am so tired of robocalls. Is there any way to stop them for good? A: The robocall is like the mosquito of telecommunications, bugging us to the point of madness. So why do companies (and criminals) still cling to such an obnoxious method? Enough people still relent or refuse to hang up, or even hand over their credit card numbers that the masterminds behind robocalls would be crazy to give up their racket.

On-device Artificial Intelligence (AI) chips are fast changing the way Indian users interact with their devices and Taiwanese mobile chipset maker MediaTek on Thursday said it will ensure that the experience reaches millions of feature phone and entry-level smartphone users. Showcasing advancements in smartphone AI here, TL Lee, General Manager, Wireless Communication, MediaTek, told IANS that the company is well aware of the huge mobile handset consumer base. There are nearly 650 million mobile phone users in India and just over 300 million of them have a smartphone, according to Counterpoint Research. "We want to democratise AI. The idea of introducing New-Age chipsets with AI capabilities is to empower all mobile users. India offers a great opportunity across price segments and we'll have AI chips to address all of those soon," Lee emphasised.

An important challenge in underlay dynamic spectrum access (DSA) is how to establish an interference limit for the primary network (PN) and how cognitive radios (CRs) in the secondary network (SN) become aware of their created interference on the PN, especially when there is no exchange of information between the primary and the secondary networks. This challenge is addressed in this paper by present- ing a fully autonomous and distributed underlay DSA scheme where each CR operates based on predicting its transmission effect on the PN. The scheme is based on a cognitive engine with an artificial neural network that predicts, without exchanging information between the networks, the adaptive modulation and coding configuration for the primary link nearest to a transmitting CR. By managing the tradeoff between the effect of the SN on the PN and the achievable throughput at the SN, the presented technique maintains the change in the PN relative average throughput within a prescribed maximum value, while also finding transmit settings for the CRs that result in throughput as large as allowed by the PN interference limit. Moreover, the proposed technique increases the CRs transmission opportunities compared to a scheme that can only estimate the modulation scheme.

Your next smartphone might just throw you a curve. Picture this: You pull your phone out of your pocket and unfold it like a napkin into a tablet. You press your finger on the screen, and it unlocks. You switch to the camera app, and a spider-like array of lenses shoot simultaneously to capture one giant photo. These are all things I've seen phones do -- some in prototype form, others in models you can get only in China.

Have you started working with the Qualcomm Snapdragon Neural Processing Engine (NPE) SDK yet? I posted last time about deep learning at the edge on IoT devices and described how you can use the SDK to execute artificial intelligence (AI) workloads on GPU, DSP and CPU. Since then, we've added to the number and strength of deep neural networks (DNN) we support in the SNPE SDK. To help get the wheels of innovation turning for you, in this post I'll describe ways you can apply AI using those DNNs to various IoT devices and use cases, including context-aware AI. How You Can Use AI in the Internet of Things First, we've found that although many developers want to use AI in IoT, there is no comprehensive resource telling them how they can use it.

The existing spectrum management paradigm treats frequency spectrum as a fixed commodity, which leads to spectrum under utilization. Cognitive radio has emerged as a useful strategy to increase spectrum utilization. The existing literature on cognitive radio has largely been focused on the primary/secondary user paradigm, where secondary users need to detect vacant spectrum when available and vacate the occupied spectrum when a primary user wants to transmit. We focus on a different type of spectrum sharing system in which there is no distinction between users, and in which there is no coordination among the users. The collective performance across all users is more important than that of individual users. This is in contrast to the typical primary/secondary user paradigm in which secondary users bear the responsibility for ensuring priority-based spectrum sharing.