And to that end, the 34-year-old company already appears to have some wind in its sails. Revenues were down 17% year over year during the company's fiscal year fourth quarter, but it beat Wall Street's expectations and sent company stock up 7%. The company is hinging its future performance on 5G, and highlighted areas of momentum that it expects to fuel growth. CEO Steve Mollenkopf told analysts that the company is actively working with standards bodies to define forthcoming advancements in 5G and positioning itself to support the expansion of 5G into enterprise, industrial IoT, and automotive markets. "The complexity and expansion of cellular technologies beyond the smartphone into nearly every industry play directly to Qualcomm's strengths and are why we believe 5G will represent the single biggest opportunity in Qualcomm's history," he said during an earnings call, according to a Seeking Alpha transcript.

"China can Enslave Humanity with Artificial Intelligence, Robotics & Drones on 5G Network," says The AI Organization What are the inter-connections between AI, U.S, China, Big Tech and the world's use of Facial Recognition, Bio-Metrics, Drones, Smart Phones, Smart Cities, IoT, VR, Mixed Reality, 5G, Robotics, Cybernetics, & Bio-Digital Social Programming? The book will cover present, emerging and future threats of Artificial Intelligence with Big Tech, including technology that can be used for assassination or to control humanity's ability to have free formed thoughts without AI Bio-Digital Social Programming. The book will cover Cyborgs, Super Intelligence and how it can form, and in what ways it can travel undetected through The AI Global Network as it connects with the internet and the Human Bio-Digital Network. Over 50 Companies and Organizations are discussed, such as Huawei, Facebook, Nearalink, Google, Baidu, Megvii Face, and Alibaba. This book takes the reader in a simple way to understand what is Artificial Intelligence, and step by step, it takes the average reader through a process to understand very difficult concepts in a simplistic way.

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.

All networks, whether transport, telecommunications, IT hubs and even entire cities, need proper monitoring and management. Sophisticated processes (and the people who understand them) are required for the kind of deep network drill-down that reveals the causes of problems and ways in which they could be prevented. Similar processes are essential to handle any issues that do slip through the net, to analyze their effects, and to implement damage limitation and permanent solutions. Byte-level analysis of high-quality data makes these processes possible. For telecommunications network operators, such data are today generated in massive volumes.

Guo Ping, rotating chairman of Huawei, has painted a stunningly optimistic picture of high-speed 5G wireless technology, claiming it will be "the new electricity" when combined with other emergent technologies such as artificial intelligence. Guo, who has worked at Huawei for over thirty years since he joined the firm as a 22-year-old, was speaking at the Web Summit technology conference in Lisbon, Portugal on Monday. Huawei employs a system whereby top execs rotate in and out of the position of chairman for months-long stints, while CEO Ren Zhengfei remains in place. Addressing a sold-out crowd, Guo drew a lengthy comparison between 5G and the technological harnessing of electricity for human use. He said: "5G plus'x' will create a smart new era. This'x' can be AI, big data or VR/AR, among other technologies. As you all know, 5G deployment has just begun. AI's applications for a range of industries are still in their infancy. I believe that in the future, 5G plus'x' will create countless possibilities for entrepreneurs."

Federated learning (FL) rests on the notion of training a global model in a decentralized manner. Under this setting, mobile devices perform computations on their local data before uploading the required updates to improve the global model. However, when the participating clients implement an uncoordinated computation strategy, the difficulty is to handle the communication efficiency (i.e., the number of communications per iteration) while exchanging the model parameters during aggregation. Therefore, a key challenge in FL is how users participate to build a high-quality global model with communication efficiency. We tackle this issue by formulating a utility maximization problem, and propose a novel crowdsourcing framework to leverage FL that considers the communication efficiency during parameters exchange. First, we show an incentive-based interaction between the crowdsourcing platform and the participating client's independent strategies for training a global learning model, where each side maximizes its own benefit. We formulate a two-stage Stackelberg game to analyze such scenario and find the game's equilibria. Second, we formalize an admission control scheme for participating clients to ensure a level of local accuracy. Simulated results demonstrate the efficacy of our proposed solution with up to 22 % gain in the offered reward. A preliminary version of this paper has been accepted at IEEE GLOBECOM [1]. Nguyen H. Tran is with the School of Computer Science, The University of Sydney, NSW 2006, Australia, email: nguyen.tran@sydney.edu.au. Mehdi Bennis is with the Center for Wireless Communications, University of Oulu, 90014 Oulu, Finland, email: mehdi.bennis@oulu.fi. I NTRODUCTION A. Background and motivation Recent years have admittedly witnessed a tremendous growth in the use of Machine Learning (ML) techniques and its applications in mobile devices. On one hand, according to International Data Corporation, the shipments of smartphones reached 3 billions in 2018 [2], which implies a large crowd of mobile users generating personalized data via the interaction with mobile applications, or with the use of inbuilt sensors (e.g., cameras, microphones and GPS) exploited efficiently by mobile crowdsensing paradigm (e.g., for indoor localization, traffic monitoring, navigation [3], [4], [5], [6]). On the other hand, mobile devices are getting empowered extensively with specialized hardware architectures and computing engines such as the CPU, GPU and DSP (e.g., energy efficient Qualcomm Hexagon V ector eXtensions on Snapdragon 835 [7]) for solving diverse machine learning problems. Gartner predicts that 80 percent of smartphones will have on-device AI capabilities by 2022.

Years ago, saying that improving telecommunication networks would improve road safety would attract some very strange looks -- surely, this would mean that drivers are more distracted and less focused on the road? The situation these days, however, is quite different -- 5G and road safety might just be the unlikeliest of friends. I recently attended an IP Seminar hosted by Volvo Cars, where connectivity and data were mentioned extensively. Connectivity in vehicles is one of the fastest-growing areas of technology. The proliferation of the 5G network will enable the realisation of technologies which are sure to improve road safety and lead to improved driving.

Understanding urban dynamics, i.e., how the types and intensity of urban residents' activities in the city change along with time, is of urgent demand for building an efficient and livable city. Nonetheless, this is challenging due to the expanding urban population and the complicated spatial distribution of residents. In this paper, to reveal urban dynamics, we propose a novel system UrbanRhythm to reveal the urban dynamics hidden in human mobility data. UrbanRhythm addresses three questions: 1) What mobility feature should be used to present residents' high-dimensional activities in the city? 2) What are basic components of urban dynamics? 3) What are the long-term periodicity and short-term regularity of urban dynamics? In UrbanRhythm, we extract staying, leaving, arriving three attributes of mobility and use a image processing method Saak transform to calculate the mobility distribution feature. For the second question, several city states are identified by hierarchy clustering as the basic components of urban dynamics, such as sleeping states and working states. We further characterize the urban dynamics as the transform of city states along time axis. For the third question, we directly observe the long-term periodicity of urban dynamics from visualization. Then for the short-term regularity, we design a novel motif analysis method to discovery motifs as well as their hierarchy relationships. We evaluate our proposed system on two real-life datesets and validate the results according to App usage records. This study sheds light on urban dynamics hidden in human mobility and can further pave the way for more complicated mobility behavior modeling and deeper urban understanding.

In this paper, a novel experienced deep reinforcement learning (deep-RL) framework is proposed to provide model-free resource allocation for ultra reliable low latency communication (URLLC) in the downlink of a wireless network. The proposed, experienced deep-RL framework can guarantee high end-to-end reliability and low end-to-end latency, under explicit data rate constraints, for each wireless user without any models of or assumptions on the users' traffic. In particular, in order to enable the deep-RL framework to account for extreme network conditions and operate in highly reliable systems, a new approach based on generative adversarial networks (GANs) is proposed. This GAN approach is used to pre-train the deep-RL framework using a mix of real and synthetic data, thus creating an experienced deep-RL framework that has been exposed to a broad range of network conditions. Formally, the URLLC resource allocation problem is posed as a power minimization problem under reliability, latency, and rate constraints. To solve this problem using experienced deep-RL, first, the rate of each user is determined. Then, these rates are mapped to the resource block and power allocation vectors of the studied wireless system. Finally, the end-to-end reliability and latency of each user are used as feedback to the deep-RL framework. It is then shown that at the fixed-point of the deep-RL algorithm, the reliability and latency of the users are near-optimal. Moreover, for the proposed GAN approach, a theoretical limit for the generator output is analytically derived. Simulation results show how the proposed approach can achieve near-optimal performance within the rate-reliability-latency region, depending on the network and service requirements. The results also show that the proposed experienced deep-RL framework is able to remove the transient training time that makes conventional deep-RL methods unsuitable for URLLC. A. Taleb Zadeh Kasgari and W . Saad are with Wireless@VT, Department of ECE, Virgina Tech, Blacksburg, V A, 24060, USA. M. Mozaffari is with Ericsson Research, Santa Clara, CA, 95054, USA, Email: mohammad.mozaffari@ericsson.com. Poor is with the Department of Electrical Engineering, Princeton University, Princeton, NJ, 08544, USA, Email: poor@princeton.edu. A preliminary version of this work appeared in IEEE ICC, [1]. I NTRODUCTION Ultra reliable low latency communication (URLLC) will be one of the most important features in next-generation 5G and beyond cellular networks as it will be necessary for mission critical applications such as Internet of Things (IoT) [2] sensing and control as well as remote control of autonomous vehicles and drones [3], [4]. Thus far, prior URLLC research has been mostly focused on applications that require low data rates such as uplink transmissions of IoT sensors [3], [5].

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