The University of Tokyo and SoftBank will set up an artificial intelligence (AI) research base to develop bright minds and start-up ambitions for Japan. Called the Beyond AI Institute, it aims to be an organisation that brings together researchers from the University of Tokyo and universities abroad. The bank will spend $184 million over the next ten years for this institute. Specific initiatives include work in quantum physics and the combination of AI and biofunctions. The Tokyo-based institute will also look at areas such as "health and medical," "public works and social infrastructure" and "manufacturing."

Generally, in dynamic spectrum access (DSA) networks, co-operations and centralized control are unavailable and DSA users have to carry out wireless transmissions individually. DSA users have to know other users' behaviors by sensing and analyzing wireless environments, so that DSA users can adjust their parameters properly and carry out effective wireless transmissions. In this thesis, machine learning and deep learning technologies are leveraged in DSA network to enable appropriate and intelligent spectrum managements, including both spectrum access and power allocations. Accordingly, a novel spectrum management framework utilizing deep reinforcement learning is proposed, in which deep reinforcement learning is employed to accurately learn wireless environments and generate optimal spectrum management strategies to adapt to the variations of wireless environments. Due to the model-free nature of reinforcement learning, DSA users only need to directly interact with environments to obtain optimal strategies rather than relying on accurate channel estimations.

UAV enabled communications and networking can enhance wireless connectivity and support emerging services. However, this would require system-level understanding to modify and extend the existing terrestrial network infrastructure. In this paper, we integrate UAVs both as user equipment and base stations into existing LTE-Advanced heterogeneous network (HetNet) and provide system-level insights of this three-tier LTE-Advanced air-ground HetNet (AG-HetNet). This AG-HetNet leverages cell range expansion (CRE), ICIC, 3D beamforming, and enhanced support for UAVs. Using system-level understanding and through brute-force technique and heuristics algorithms, we evaluate the performance of AG-HetNet in terms of fifth percentile spectral efficiency (5pSE) and coverage probability. We compare 5pSE and coverage probability, when aerial base-stations (UABS) are deployed on a fixed hexagonal grid and when their locations are optimized using genetic algorithm (GA) and elitist harmony search algorithm based on genetic algorithm (eHSGA). Our simulation results show the heuristic algorithms outperform the brute-force technique and achieve better peak values of coverage probability and 5pSE. Simulation results also show that trade-off exists between peak values and computation time when using heuristic algorithms. Furthermore, the three-tier hierarchical structuring of FeICIC provides considerably better 5pSE and coverage probability than eICIC.

Communication service providers (CommSPs) are already saving money and generating revenue from network transformation investments. There is an expectation these benefits will continue to increase as NFV functions scale across the various elements of the infrastructure--enterprise, radio access network, wireless core, cable and cloud. New 5G and edge computing use cases promise to deliver new revenue along with even more data that must be moved, stored, processed and analyzed. The industry is looking to Artificial Intelligence (AI) and Machine Learning (ML) to enable CommSPs to solve problems and unlock value for their own business operations and their customers. As an example, distributed AI based on reinforcement learning will play a key role in building automated and self-managed networks.

SoftBank Corp. and the University of Tokyo have agreed to open artificial-intelligence centers staffed with specialists from the university and around the world, to swiftly turn research into profitable business ventures so Japan can keep up with the U.S. and China. "If they are stuck with research … their funds and passions will drain away," SoftBank Group CEO Masayoshi Son said at an event announcing the joint project at the prestigious university. "By teaming with the University of Tokyo, we want to give students a chance to learn and start a business," said Son, who has stressed the importance of AI for years. Under the arrangement, a pair of facilities -- one on the university's Hongo campus in Bunkyo Ward and the other at a planned new SoftBank office in the Takeshiba district -- will be established in spring and winter of 2020 at the earliest, respectively, under the brand Beyond AI. The Hongo base will handle basic research on the evolution of AI and potential ways to apply the technology in physics, robotics, brain science and other fields.

The following keynote speakers have been confirmed for IEEE GLOBECOM 2019. Abstract: We are well into the "Internet of Things" era for the Internet. Billions of devices are expected and it is not uncommon to find a dozen or even a score of Internet-enabled devices in residences and offices around the world. These systems run on software - some of which has not been well tested for safety and security. We need to introduce and promote an ethic of software safety and extended maintenance to protect the users of these devices.

A Clustering Approach to Edge Controller Placement in Software Defined Networks with Cost Balancing Reza Soleymanifar, Amber Srivastava, Carolyn Beck, Srinivasa Salapaka Abstract -- In this work we introduce two novel deterministic annealing based clustering algorithms to address the problem of Edge Controller Placement (ECP) in wireless edge networks. These networks lie at the core of the fifth generation (5G) wireless systems and beyond. These algorithms, ECP-LL and ECP-LB, address the dominant leader-less and leader-based controller placement topologies and have linear computational complexity in terms of network size, maximum number of clusters and dimensionality of data. Each algorithm tries to place controllers close to edge node clusters and not far away from other controllers to maintain a reasonable balance between synchronization and delay costs. While the ECP problem can be conveniently expressed as a multi-objective mixed integer nonlinear program (MINLP), our algorithms outperform state of art MINLP solver, BARON both in terms of accuracy and speed. Our proposed algorithms have the competitive edge of avoiding poor local minima through a Shannon entropy term in the clustering objective function. Most ECP algorithms are highly susceptible to poor local minima and greatly depend on initialization. Keywords: Clustering, deterministic annealing, 5G networks, software defined networks, wireless edge networks, edge controller placement I.

Artificial Intelligence (AI) has the ability to innovate and advance conventional practices and business operations. To bring AI to the edge, ADLINK takes a heterogeneous approach and offers a comprehensive solution portfolio of deep learning platforms and servers including acceleration engines, inference platforms, and training servers to infuse the power of AI into the smart manufacturing, smart city, logistics and warehousing, telecommunications applications and more.

Last week, the Australian state of New South Wales announced a plan to crack down on drivers using their phones on the road. The state's transport agency said it had integrated machine vision into roadside cameras to spot offenders. The AI automatically flags suspects, humans confirm what's going on, and a warning letter is sent out to the driver. "It's a system to change the culture," the assistant police commissioner of New South Wales, Michael Corboy, told Australian media, noting that police hoped the technology would cut fatalities on the road by a third over two years. It seems an admirable scheme, top to bottom.