Huawei has stated that a dispute with T-Mobile was settled in 2017 after a report said US authorities had opened a criminal investigation into the Chinese telecommunications company. The US justice department (DoJ) was, according to the Wall Street Journal which cited anonymous sources, in the "advanced" stages of a criminal inquiry that could result in an indictment of Huawei. The newspaper said the DoJ was looking into allegations of theft of trade secrets from Huawei's US business partners, including a T-Mobile robotic device used to test smartphones. Huawei and the DoJ declined to comment directly on the report. Huawei said: "Huawei and T-Mobile settled their disputes in 2017 following a US jury verdict finding neither damage, unjust enrichment nor wilful and malicious conduct by Huawei in T-Mobile's trade secret claim."

WASHINGTON - U.S. authorities are in the "advanced" stages of a criminal probe that could result in an indictment of Chinese technology giant Huawei, a report said Wednesday. The Wall Street Journal, citing anonymous sources, said the Justice Department is looking into allegations of theft involving trade secrets from Huawei's U.S. business partners, including a T-Mobile robotic device used to test smartphones. The Justice Department declined to comment on the report and Huawei did not respond to a request for comment. The move would further escalate tensions between the U.S. and China after the arrest last year in Canada of Huawei's chief financial officer Meng Wanzhou, who is the daughter of the company's founder and remains under house arrest, awaiting proceedings. The Meng case has inflamed U.S.-China and Canada-China relations.

Artificial intelligence (AI) is in vogue. It's almost impossible to read an article in any media outlet that doesn't mention AI and the possibility it will reshape the world in which we live. In fact, according to research conducted by AT&T, AI has the potential to double GDP growth across geographies by 2035. Consumers are already interacting with a variety of low-level AI assistants, such as Siri, Cortana, and Alexa. With respect to the telecom sector, AI – supported by machine learning (ML) – is fundamental to controlling and operating communications networks of the future.

Recent years have witnessed a widespread increase of interest in network representation learning (NRL). By far most research efforts have focused on NRL for homogeneous networks like social networks where vertices are of the same type, or heterogeneous networks like knowledge graphs where vertices (and/or edges) are of different types. There has been relatively little research dedicated to NRL for bipartite networks. Arguably, generic network embedding methods like node2vec and LINE can also be applied to learn vertex embeddings for bipartite networks by ignoring the vertex type information. However, these methods are suboptimal in doing so, since real-world bipartite networks concern the relationship between two types of entities, which usually exhibit different properties and patterns from other types of network data. For example, E-Commerce recommender systems need to capture the collaborative filtering patterns between customers and products, and search engines need to consider the matching signals between queries and webpages. This work addresses the research gap of learning vertex representations for bipartite networks. We present a new solution BiNE, short for Bipartite Network Embedding}, which accounts for two special properties of bipartite networks: long-tail distribution of vertex degrees and implicit connectivity relations between vertices of the same type. Technically speaking, we make three contributions: (1) We design a biased random walk generator to generate vertex sequences that preserve the long-tail distribution of vertices; (2) We propose a new optimization framework by simultaneously modeling the explicit relations (i.e., observed links) and implicit relations (i.e., unobserved but transitive links); (3) We explore the theoretical foundations of BiNE to shed light on how it works, proving that BiNE can be interpreted as factorizing multiple matrices.

Every year the technology industry gathers in Las Vegas for the Consumer Electronics Show (CES), an event that often sets the agenda for the coming 12 months. This is what CES 2019 taught us. The first 5G networks are expected to begin rolling out this year, and so the next-generation connectivity technology was being mentioned everywhere at CES. Intel, Qualcomm and Samsung all spoke about harnessing the technology to not just offer faster mobile internet speeds, but also to connect more devices and appliances to each other and be able to handle more data in the process. Experts at the show also commented on the higher capacity of 5G networks being able to support the software needed to power networks of driverless cars and robots. The halls of this year's CES hinted at a world where homes, cars and even entire cities are connected to one another, with people able to use these connections to complete tasks every day.

Discover why AI and accelerated computing are essential to every CTO in telecommunications. The world of ubiquitous connectivity in which we now live, and the tidal wave of consumptive devices serving infinite streams of data are unrelenting. The telecommunications industry has played a significant role in the evolution of apps everywhere--irreversibly altering both developer and consumer behaviors and expectations. We find ourselves surging into a world that will not only be connected, but demand substantial computing resources for each connected device. Cars, refrigerators, and even washing machines will function as connected devices, generating petabytes of data.

Verizon probably isn't the first name that'll come to mind when you think of video games, but the carrier apparently has big gaming plans for the future. According to The Verge, the company is working on and is already alpha testing a cloud gaming service à la PlayStation Now called Verizon Gaming. Based on the forum posts and emails The Verge saw, Verizon has been recruiting testers for a while now, giving qualified applicants a free NVIDIA Shield TV, where the service is already up and running with 135 games, and an Xbox One Controller, which works with the platform. Testers also get a $150 Amazon gift card as a reward. Leaked screenshots of the version being tested show popular games like Fortnite, God of War, Detroit Become Human and Red Dead Redemption.

Think you've come up with a killer idea for exploiting the emerging next-generation wireless networks known as 5G? If Verizon buys into in your vision and considers it commercially viable, the company will issue you up to a cool $1 million in seed money. What's more, you'll be invited to develop the concept on live networks in one of Verizon's 5G incubator labs, in New York City; Cambridge, Massachusetts; Los Angeles; Palo Alto, California, and Washington, D.C. And Verizon will provide training and technical support to the chosen innovators. It's all part of a "Built on 5G Challenge" launched this week at CES in Las Vegas during a keynote address by Verizon CEO Hans Vestberg.

A major obstacle for widespread deployment of frequency division duplex (FDD)-based Massive multiple-input multipleoutput (MIMO)communications is the large signaling overhead for reporting full downlink (DL) channel state information (CSI) back to the basestation (BS), in order to enable closed-loop precoding. We completely remove this overhead by a deep-learning based channel extrapolation (or "prediction") approach and demonstrate that a neural network (NN) at the BS can infer the DL CSI centered around a frequency f UL; nomore pilot/reporting overhead is needed than with a genuine time division duplex (TDD)-based system. The rationale is that scatterers and the large-scale propagation environment are sufficiently similar to allow a NN to learn about the physical connections and constraints between two neighboring frequency bands, and thus provide a well-operating system even when classic extrapolation methods, like the Wiener filter (used as a baseline for comparison throughout) fails. We study its performance for various state-of-the-art Massive MIMO channel models, and, even more so, evaluate the scheme using actual Massive MIMO channel measurements, rendering it to be practically feasible at negligible loss in spectral efficiency when compared to a genuine TDD-based system. I. INTRODUCTION With a significant increase in area throughput, Massive multiple-input multiple-output (MIMO) antenna communication has become an enabling technology for the upcoming fifth generation (5G) wireless mobile communication systems [1], [2], [3], [4]. However, Massive MIMO systems described in current research literature commonly exploit channel reciprocity and hence rely on time division duplex (TDD)-based approaches [1], i.e., uplink (UL) and downlink (DL) channels share the same frequency band in orthogonal time intervals. Achieving such reciprocity in practice requires accurate hardware with costly calibration circuitry. To mitigate this issue, various alternatives to a full Massive MIMO system have been proposed such as the grid of beams [5] and codebook Massive MIMO [6].

BEIJING - Huawei Technologies Ltd. showed off a new processor chip for data centers and cloud computing Monday, expanding into new and growing markets despite Western warnings the company might be a security risk. Huawei and other Chinese technology companies that rely on Western technology are stepping up efforts to develop their own. The company based in southern China's Shenzhen has pushed ahead with commercial initiatives despite the Dec. 1 arrest of its chief financial officer, Meng Wanzhou, the daughter of Huawei founder Ren Zhengfei, in Canada on U.S. charges related to possible violations of trade sanctions on Iran. Huawei said the Kunpeng 920 chip is designed for servers that handle a flood of data from smartphones, video and other network services -- a fast growing sector with the development of artificial intelligence and the "internet of things." The company said it is part of a planned product lineup to support "intelligent computing."