We propose a mechanism for distributed radio resource management using multi-agent deep reinforcement learning (RL) for interference mitigation in wireless networks. We equip each transmitter in the network with a deep RL agent, which receives partial delayed observations from its associated users, while also exchanging observations with its neighboring agents, and decides on which user to serve and what transmit power to use at each scheduling interval. Our proposed framework enables the agents to make decisions simultaneously and in a distributed manner, without any knowledge about the concurrent decisions of other agents. Moreover, our design of the agents' observation and action spaces is scalable, in the sense that an agent trained on a scenario with a specific number of transmitters and receivers can be readily applied to scenarios with different numbers of transmitters and/or receivers. Simulation results demonstrate the superiority of our proposed approach compared to decentralized baselines in terms of the tradeoff between average and 5 th percentile user rates, while achieving performance close to, and even in certain cases outperforming, that of a centralized information-theoretic scheduling algorithm. We also show that our trained agents are robust and maintain their performance gains when experiencing mismatches between training and testing deployments. I. INTRODUCTION One of the key drivers for improving throughput in future wireless networks, including fifth generation mobile networks (5G), is the densification achieved by deploying more base stations. The authors are with Intel Corporation, Santa Clara, CA 95054. The rise of such ultra-dense network paradigms implies that the limited physical wireless resources (in time, frequency, etc.) need to support an increasing number of simultaneous transmissions. Effective radio resource management procedures are, therefore, critical to mitigate the interference among such concurrent transmissions and achieve the desired performance enhancement in these ultra-dense environments. The radio resource management problem is in general non-convex and therefore computationally complex, especially as the network size increases. There is a rich literature of centralized and distributed algorithms for radio resource management, using various techniques in different areas such as geometric programming [1], weighted minimum mean square optimization [2], game theory [3], information theory [4], [5], and fractional programming [6].

Inside you'll learn about the major market drivers and barriers, technologies, key players, and forecasts related to eight telecom AI use cases:

Network congestion games are a well-understood model of multi-agent strategic interactions. Despite their ubiquitous applications, it is not clear whether it is possible to design information structures to ameliorate the overall experience of the network users. We focus on Bayesian games with atomic players, where network vagaries are modeled via a (random) state of nature which determines the costs incurred by the players. A third-party entity---the sender---can observe the realized state of the network and exploit this additional information to send a signal to each player. A natural question is the following: is it possible for an informed sender to reduce the overall social cost via the strategic provision of information to players who update their beliefs rationally? The paper focuses on the problem of computing optimal ex ante persuasive signaling schemes, showing that symmetry is a crucial property for its solution. Indeed, we show that an optimal ex ante persuasive signaling scheme can be computed in polynomial time when players are symmetric and have affine cost functions. Moreover, the problem becomes NP-hard when players are asymmetric, even in non-Bayesian settings.

The Galaxy S20 Ultra uses artificial intelligence to clean up images taken with the extreme zoom, the company says.

Network capacity planning aims to ensure that sufficient bandwidth is provisioned, allowing network SLA targets, such as delay, jitter, loss, and availability, to be reliably met. Until recently, the network data necessary for insightful capacity planning was generally only available via static, historical, after-the-fact reports. This situation is now rapidly changing. "By pairing advanced data science and cognitive technology such as AI and machine learning, IT can drive new and smarter predictive insights to improve network capacity-planning accuracy," says Ashish Verma, a Deloitte Consulting managing director specializing in cognitive analytics. "This helps organizations unleash data to make more agile decisions, improve operational wisdom, avoid downtime and create a better user experience."

Motorola has hit back at claims that its resurrected Razr can only withstand 27,000 folds before showing signs of damage. CNET conducted a durability test with the new foldable phone last week with a FoldBot and after thousands of rapid folds and just three hours, the Razr's hinge was failing and not fully closing the foldable device. However, the US smartphone-maker says the robot'put undue stress on the hinge' and did not allow the foldable phone to'open and close as intended', the firm told Engadget in a statement. Motorola conducted its own tests with a robot it claims folds the phone properly and revealed users should get'years of use.' In a statement to Engadget, Motorola said: 'razr is a unique smartphone, featuring a dynamic clamshell folding system unlike any device on the market.

This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting. We summarize the general categories of topology for communication structures in MARL literature, which are often manually specified. Then we propose a novel framework termed as Learning Structured Communication (LSC) by using a more flexible and efficient communication topology. Our framework allows for adaptive agent grouping to form different hierarchical formations over episodes, which is generated by an auxiliary task combined with a hierarchical routing protocol. Given each formed topology, a hierarchical graph neural network is learned to enable effective message information generation and propagation among inter- and intra-group communications. In contrast to existing communication mechanisms, our method has an explicit while learnable design for hierarchical communication. Experiments on challenging tasks show the proposed LSC enjoys high communication efficiency, scalability, and global cooperation capability.

Leonard Hobbs Bio: Leonard graduated from University College Cork Ireland in 1986 with a 1st class honours degree in Electrical Engineering and was awarded the title of'graduate of the year' by the college. He completed a Masters degree at the NMRC (now called Tyndall), at UCC in 1988. He has been one of Ireland's leading technologists in the ICT sector with close to 30 years of experience, mostly with Intel, spanning leading edge research to advanced manufacturing. His last role at Intel was Director of Public Affairs with responsibility for driving Intel Ireland's policy, communications, education and community agendas. Leonard is currently the Director of Research and Innovation at Trinity College Dublin where he owns the definition and implementation of the research, innovation and enterprise strategy for the University spanning research programs development, contract management, technology transfer, entrepreneurship and enterprise partnership liaison.

SAN FRANCISCO – A top U.S. partner at SoftBank Group Corp.'s technology fund is stepping down, after the company posted declining returns on its investments and struggled to raise capital for the next Vision Fund. Michael Ronen, the outgoing managing partner, expressed concerns about "issues" at SoftBank in an interview with the Financial Times, which earlier reported his departure. Since joining in 2017 from Goldman Sachs Group Inc., Ronen led a series of investments, most notably a $2.25 billion deal for General Motors Co.'s Cruise self-driving unit. He's at least the second managing partner to leave in the last couple of months. SoftBank bid up the valuation of WeWork parent company The We Co. to $47 billion before a failed attempt at an initial public offering sent the value plummeting and forced the conglomerate to bail out the coworking startup.