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.

Existing fuzzy neural networks (FNNs) are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep fuzzy neural network, namely deep evolving fuzzy neural networks (DEVFNN). Fuzzy rules can be automatically extracted from data streams or removed if they play little role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely Generic Classifier (gClass), drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of input space dimension due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using six datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four state-of the art data stream methods and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy.

Transaction Network Services (TNS) has been around for decades, and as one of the largest independent providers of inter-carrier call signaling and routing, it's an established player in telecom. But it's that long-time positioning that's helping it compete in the wild, wild west of the robocall detection business. This week, the company announced that its Neighbor Spoofing feature is enabling wireless carriers to protect their subscribers from the robocall tactic that uses local area codes or other means to make the consumer think the call is originating in their local area. The thinking is, if a call matches or closely matches their area code, they're more likely to trust the call is real and pick up. Carriers will use messages like "Potential spam" or "Likely spam" to let their customers know when a call is coming from a bad number so they don't pick up.

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that an edge-based deployment can also be used as an enabler of advanced Machine Learning (ML) applications in cellular networks, thanks to the balance it strikes between a completely distributed and a centralized approach. First, we will present an edge-controller-based architecture for cellular networks. Second, by using real data from hundreds of base stations of a major U.S. national operator, we will provide insights on how to dynamically cluster the base stations under the domain of each controller. Third, we will describe how these controllers can be used to run ML algorithms to predict the number of users, and a use case in which these predictions are used by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that prediction accuracy improves when based on machine learning algorithms that exploit the controllers' view with respect to when it is based only on the local data of each single base station. The next generation of cellular networks (5G) is being designed to satisfy the massive growth in capacity demand, number of connections and the evolving use cases of a connected society for 2020 and beyond [1]. Michele Polese and Michele Zorzi are with the Department of Information Engineering (DEI), University of Padova, Italy. In order to meet these requirements, a new approach in the design of the network is required, and new paradigms have recently emerged [3]. First, the densification of the network will increase the spatial reuse and, combined with the usage of mmWave frequencies, the available throughput. On the other hand, this will introduce new challenges related to mobility management [4].