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].

About US: Wise Guy Reports is part of the Wise Guy Consultants Pvt. Ltd. and offers premium progressive statistical surveying, market research reports, analysis & forecast data for industries and governments around the globe.

About US: Wise Guy Reports is part of the Wise Guy Consultants Pvt. Ltd. and offers premium progressive statistical surveying, market research reports, analysis & forecast data for industries and governments around the globe.

A research team at the NYU Tandon School of Engineering, with the support of the National Science Foundation's National Robotics Initiative 2.0, is building the foundations of a wireless system that takes advantage of superfast fifth-generation (5G) wireless communications to outsource a mobile robots' artificial intelligence (AI) functions to the edge cloud--the server in the cloud closest to the robot. The collaborators, all of whom are members of the faculty of NYU Tandon's renowned NYU WIRELESS center for telecommunications research, will design manipulation and locomotion algorithms that address some important technical hurdles to making 5G networks a viable bridge between robot and server. Shifting AI capabilities from the robot to a remote server offers tantalizing operational benefits, such as allowing robots to perceive the environment, perform complex operations, and make decisions autonomously, all without incurring major energy and weight costs from onboard computational and power-generation equipment. Comprising Ludovic Righetti, professor in the Departments of Electrical and Computer Engineering and Mechanical and Aerospace Engineering; and Siddharth Garg, Sundeep Rangan and Elza Erkip, professors in the Department of Electrical and Computer Engineering, the team will focus on solving issues of reliability, safety of robotic operation under communication degradation, and scalability to multi-robot systems. The collaboration brings expertise in robotics (Righetti), computer architecture and computation (Garg), wireless networks (Rangan and Erkip), and information theory (Erkip).

Finland's Ultimate.ai is in the business of creating superhuman customer service employees. Reetu Kainulainen is uneasy with the idea that artificial intelligence and machine learning should be utilised to make customer service employees redundant. "There has been a lot of talk about automation and productivity. Customer service, after all, is a very unique organisation that has one-on-one conversations with customers even if we're talking about a giant telecommunications company with tens of millions of customers," he reminds. "We don't believe that artificial intelligence should replace customer service employees, but that it should be used to give them superpowers."

Over the years, Data Science has become an integral part of many industries like Agriculture, Marketing Analytics, Manufacturing, among others. As the technology deals with processes and systems that are utilized to convert a large amount of data into insights, the role of a data scientist is now a buzzworthy career. Data science leverages lots of theories and methods that are a part of other fields like information science, mathematics, statics, chemometrics, and computer science. As data science is becoming an extremely dynamic space in India in today's digital age, candidates are talking more about the salaries of this emerging field. Here we amassed together top Data Scientist's salary to watch in October that offers the best opportunities to job seekers for this position.

Because relationships can be hard, and because it is woefully easy to misinterpret a text message, there's a new messaging service that features artificial intelligence to help you get it right. Mei is a mobile messaging startup that uses learning algorithms to help you understand the subtext of conversations. Studies have shown that about 44 per cent of the time, most text recipients fail to tell the difference between sarcasm and seriousness. Es Lee, a Harvard graduate with a degree in computer science, came up with the service. "One of the difficulties of maintaining relationships through text is that it's possible to come across as crass or rude--even when that was never your intention," says Lee. "Emotion is lost in text messages. Mei uses natural language processing along with learning algorithms to pick up on the nuances that you might miss. It can determine gender and age just from the types of emoji that a person uses. Reviewing the messages between two individuals, Mei can figure out the relationship between the two and how strong it is. To do so, it calculates a "compatibility percentage" that is based on openness, emotional control, extraversion, agreeableness, and conscientiousness. "When you're a 25-year-old woman texting a 40-year-0ld man," explains Lee, "you might think that from the one-word messages he's sending, he's not into you.

Round-Trip Times are one of the most commonly collected performance metrics in computer networks. Measurement platforms such as RIPE Atlas provide researchers and network operators with an unprecedented amount of historical Internet delay measurements. It would be very useful to automate the processing of these measurements (statistical characterization of paths performance, change detection, recognition of recurring patterns, etc.). Humans are pretty good at finding patterns in network measurements but it can be difficult to automate this to enable many time series being processed at the same time. In this article we introduce a new model, the HDP-HMM or infinite hidden Markov model, whose performance in trace segmentation is very close to human cognition. This is obtained at the cost of a greater complexity and the ambition of this article is to make the theory accessible to network monitoring and management researchers. We demonstrate that this model provides very accurate results on a labeled dataset and on RIPE Atlas and CAIDA MANIC data. This method has been implemented in Atlas and we introduce the publicly accessible Web API.

SAP is renowned for its enterprise software, providing solutions across finance, supply chain and more. Another side of its business, however, lies in advising customers on the adoption of innovative technology. Frank Wilde is a Vice President for SAP's Global Center of Excellence (COE), which serves to provide this advice and expertise. "The Global COE is designed to be an incubator to support the sales motion and create a linkage to our product organization," he explains. "We help introduce new innovations and showcase the latest aspects of our portfolio to drive new customer conversations. A core component lies in making it easier for our sales teams to learn about new aspects of our portfolio, and then turn those into customer driven conversations. We're fundamentally changing the relationship with customers to be much more customer focused and much more agile as a result."

This paper explores the use of a novel form of Hierarchical Graph Neurons (HGN) for in-operation behaviour selection in a swarm of robotic agents. This new HGN is called Robotic-HGN (R-HGN), as it matches robot environment observations to environment labels via fusion of match probabilities from both temporal and intra-swarm collections. This approach is novel for HGN as it addresses robotic observations being pseudo-continuous numbers, rather than categorical values. Additionally, the proposed approach is memory and computation-power conservative and thus is acceptable for use in mobile devices such as single-board computers, which are often used in mobile robotic agents. This R-HGN approach is validated against individual behaviour implementation and random behaviour selection. This contrast is made in two sets of simulated environments: environments designed to challenge the held behaviours of the R-HGN, and randomly generated environments which are more challenging for the robotic swarm than R-HGN training conditions. R-HGN has been found to enable appropriate behaviour selection in both these sets, allowing significant swarm performance in pre-trained and unexpected environment conditions.