Among the critical elements required to support expanding Internet of Things deployments are wider wireless pipes that consume less power. That's the promise of IoT networks supported by emerging fifth-generation, or 5G, wireless network rollouts that go well beyond mobile phones to support networks of connected devices. As wireless carriers jockey for position--including a proposed merger between T-Mobile and Sprint--emerging IoT applications such as low-power, wide area connections are forecast to grow at a more than 200-percent annual rate through 2021. Industry tracker IHS Markit predicts in an updated forecast released this week that emerging 5G-based connections will help boost machine-to-machine connections as well new IoT platforms and services. "In order to scale up IoT revenues, operators will need to pursue a broader IoT strategy that goes beyond connectivity into IoT platforms, vertical offerings and ecosystem orchestration," notes Julian Watson, senior IoT analyst at IHS. "By pre-integrating partner platforms, hardware and sensors into their IoT offerings, operators can make it easier for enterprises to plan and budget for IoT projects."

Earth right now is in the beginning stages of a radical shift, where us unreliable, distracted money loving human drivers are going to be replaced by artificial intelligence algorithms, that never need to rest and will work for free. This global transition to Driverless Vehicles is going to advance much faster than most people realize, spurred on by tech and auto companies ferociously competing against each other, and also given a huge helping hand by the rapidly approaching fifth generation of cellular mobile communications, also known as 5G. The dramatic increase in the speed of data will also pave the way for a whole new generation of features and methods for businesses to make money. These will include targeted in-car ads based on information relayed back to tech and automakers from the onboard sensors and vehicle analytics, greatly improved 3d Mapping capabilities, as well as crowd-sourced reporting on the road conditions and better real-time analysis on the vehicles performance. Right now there is massive infrastructure development underway by leading Telecom players such as Verizon, T-Mobile and AT&T, who are racing to implement the new generation of 5g services in places like Dallas, Houston, L.A and New York, which will begin initially with broadband capability by the end of this year, followed by the mobile version in 2019.

It should have been a great week for Huawei. Following the news that it had overtaken Apple as the No. 2 phone maker in the world, the company set the stage for the next generation of must-have phones with the unveiling of its Kirin 980 processor and the launch date for the highly anticipated Mate 20 Pro. But instead of a series of positive headlines about what's to come, Huawei's old phones were in the news for all the wrong reasons. It started with an AnandTech report that uncovered some major inconsistencies with benchmark results. Inside the latest version of software on the P20, P20 Pro, and Honor Play, Huawei was discovered gaming its scores by optimizing the system for certain benchmarking apps, most notably the popular 3DMark and GFXBench suites.

Ambient backscatter has been introduced with a wide range of applications for low power wireless communications. In this article, we propose an optimal and low-complexity dynamic spectrum access framework for RFpowered ambient backscatter system. Under the dynamics of the ambient signals, we first adopt the Markov decision process (MDP) framework to obtain the optimal policy for the secondary transmitter, aiming to maximize the system throughput. However, the MDP-based optimization requires complete knowledge of environment parameters, e.g., the probability of a channel to be idle and the probability of a successful packet transmission, that may not be practical to obtain. To cope with such incomplete knowledge of the environment, we develop a low-complexity online reinforcement learning algorithm that allows the secondary transmitter to "learn" from its decisions and then attain the optimal policy. Simulation results show that the proposed learning algorithm not only efficiently deals with the dynamics of the environment, but also improves the average throughput up to 50% and reduces the blocking probability and delay up to 80% compared with conventional methods. Dynamic spectrum access (DSA) has been considered as a promising solution to improve the utilization of radio spectrum [2]. As DSA standard frameworks, the Federal Communications Commission and the European Telecommunications Standardization Institute have recently proposed Spectrum Access Systems (SAS) and Licensed Shared Access (LSA) respectively [3]. In both SAS and LSA, spectrum users are prioritized at different levels/tiers (e.g., there are three types of users with a decreasing order of priority: Incumbent Users (IUs), Priority Access Licensees (PALs), and General Authorized Access (GAAs)). Without loss of generality, in this work, we refer users with higher priority as IUs and users with lower priority as secondary users (SUs). DSA harvests under-utilized spectrum chunks by allowing an SU to dynamically access (temporarily) idle spectrum bands/whitespaces to transmit data.

Abstract-- This paper considers a novel framework to detect communities in a graph from the observation of signals at its nodes. We model the observed signals as noisy outputs of an unknown network process -- represented as a graph filter -- that is excited by a set of low-rank inputs. Rather than learning the precise parameters of the graph itself, the proposed method retrieves the community structure directly; Furthermore, as in blind system identification methods, it does not require knowledge of the system excitation. The paper shows that communities can be detected by applying spectral clustering to the low-rank output covariance matrix obtained from the graph signals. The performance analysis indicates that the community detection accuracy depends on the spectral properties of the graph filter considered. Furthermore, we show that the accuracy can be improved via a low-rank matrix decomposition method when the excitation signals are known. Numerical experiments demonstrate that our approach is effective for analyzing network data from diffusion, consumers, and social dynamics. The emerging field of network science and availability of big data have motivated researchers to extend signal processing techniques to the analysis of signals defined on graphs, motivating a new area of research referred to as graph signal processing (GSP) [2]-[4].

When Samsung launched the Galaxy Home speaker earlier this month, people were quick to point out how its name seemed ripped off from Google. Not to be outdone, Huawei is unveiling its own AI speaker here at IFA 2018, and it's clearly borrowed much more from the Google Home... just not the name. The AI Cube is a cylindrical speaker that looks like a stretched out version of Google's device, though it will offer Amazon's Alexa instead of Assistant. Like Samsung, Huawei is promising high-quality audio on its speaker. That's not all -- the AI Cube is also a 4G router.

This article was originally published on Tractica's sister site Light Reading. In our recent report, Tractica presents seven key use cases where AI will be leveraged in telecom. One of the most intriguing near-term opportunities AI can help address for service providers is improving customer experience (CX). McKinsey recently pointed out that companies focused on CX are seeing revenue gains of 5% to 10% and cost reductions of 15% to 25% within two to three years. Leading companies are giving customers more control, faster resolution and better outcomes tied to a personal context in their interactions.

Artificial Neural Networks (ANNs) are computational models inspired by the central nervous system (especially the brain) of animals and are used to estimate or generate unknown approximation functions relied on large amounts of inputs. Capsule Neural Network (Sabour S, et al.[2017]) is a novel structure of Convolutional Neural Networks which simulates the visual processing system of human brain. In this paper, we introduce psychological theories which called Cognitive Consistency to optimize the routing algorithm of Capsnet to make it more close to the work pattern of human brain. It has been shown in the experiment that a progress had been made compared with the baseline.

The recent CIO Summit saw executives across Australia coming together to discuss cutting-edge technology trends and management strategies. The event featured a heavyweight line-up of speakers and moderators, carefully selected to encourage discussion and challenge the status quo. IT Brief spoke to Huawei Australia chief technology officer David Soldani about his key takeaways from the event. That the world is changing fast, profoundly impacting every person, home, and organisation. For example, by 2025, 80% of people will have access to mobile broadband and the mobile traffic per day will rise from 30MB to 4GB per day; 75% of households will enjoy broadband services with 20 billion devices connected, with 12% of those being smart robots.

The Toilet Board Coalition, in collaboration with the European Space Agency, is currently soliciting applications from technology providers to improve remote data collection, transmission and synthesis to inform the development of next generation sanitation products and services. Multinational telecommunication company Ericsson has led a myriad of water-related projects across the world ranging from the US to Kenya. Ericsson is once again leading in the water space by crafting an entire smart water network around the Internet of Things (IoT). IoT enables inter-operable data acquisition resulting in real-time water monitoring with intel from the source of the water, its distribution throughout the network, and its final discharge into a receiving water body. By utilizing this technology, water data that has always evaded water managers will now be at their fingertips 24/7, 365 days a year.