--Enabling cellular connectivity for drones introduces a wide set of challenges and opportunities. Communication of cellular-connected drones is influenced by 3-dimensional mobility and line-of-sight channel characteristics which results in higher number of handovers with increasing altitude. Our cell planning simulations in coexistence of aerial and terrestrial users indicate that the severe interference from drones to base stations is a major challenge for uplink communications of terrestrial users. Here, we first present the major challenges in coexistence of terrestrial and drone communications by considering real geographical network data for Stockholm. Then, we derive analytical models for the key performance indicators (KPIs), including communications delay and interference over cellular networks, and formulate the handover and radio resource management (H-RRM) optimization problem. Afterwards, we transform this problem into a machine learning problem, and propose a deep reinforcement learning solution to solve H-RRM problem. Especially, the heat-maps of handover decisions in different drone's altitudes/speeds have been presented, which promote a revision of the legacy handover schemes and redefining the boundaries of cells in the sky. I NTRODUCTION Commercial drone applications have attracted profound interest in recent years in a wide set of use-cases, including area monitoring, surveillance, and delivery [1].

To see exactly how the networks are performing, what speeds to expect and the extent of coverage, I toured the UK to test 5G in five major cities: London, Cardiff, Birmingham, Manchester and Edinburgh. The next-generation wireless technology promises a big boost in speed and responsiveness, bringing not just a faster connection to your phone, but also enabling advancements like telemedicine and self-driving cars. The UK deployment is among several happening worldwide from the US to South Korea, as 5G slowly turns from hype to reality. EE and Vodafone have the largest UK networks so far, while O2 and Three are ramping up. I visited the cities across the course of a week, seeking out a variety of locations in each place that showed as 5G-enabled zones on network coverage maps.

Given a publicly available pool of machine learning models constructed for various tasks, when a user plans to build a model for her own machine learning application, is it possible to build upon models in the pool such that the previous efforts on these existing models can be reused rather than starting from scratch? Here, a grand challenge is how to find models that are helpful for the current application, without accessing the raw training data for the models in the pool. In this paper, we present a two-phase framework. In the upload phase, when a model is uploading into the pool, we construct a reduced kernel mean embedding (RKME) as a specification for the model. Then in the deployment phase, the relatedness of the current task and pre-trained models will be measured based on the value of the RKME specification. Theoretical results and extensive experiments validate the effectiveness of our approach.

Device to Device (D2D) Communication is one of the technology components of the evolving 5G architecture, as it promises improvements in energy efficiency, spectral efficiency, overall system capacity, and higher data rates. The above noted improvements in network performance spearheaded a vast amount of research in D2D, which have identified significant challenges that need to be addressed before realizing their full potential in emerging 5G Networks. Towards this end, this paper proposes the use of a distributed intelligent approach to control the generation of D2D networks. More precisely, the proposed approach uses Belief-Desire-Intention (BDI) intelligent agents with extended capabilities (BDIx) to manage each D2D node independently and autonomously, without the help of the Base Station. The paper includes detailed algorithmic description for the decision of transmission mode, which maximizes the data rate, minimizes the power consumptions, while taking into consideration the computational load. Simulations show the applicability of BDI agents in jointly solving D2D challenges.

When artificial intelligence is fully operational, it will transform the media and marketing industries. In particular, I believe that synthetic personalities powered by AI will change the way we learn about new products and how to use them. In my previous article, I showed how the collapse of broadcast TV exposed a huge weakness in the advertising industry. And I pointed to the nascent field known as Influencer Media, and especially Virtual Influencers, as a harbinger of the future of engagement brand-building. What happens when artificial intelligence is available to any app, any advertising campaign, and any brand marketer? How will that change things? Here's my answer: the media landscape will be transformed so deeply that it will be completely unrecognizable. All the leftover junk from the 20th century will be kaputt, including one-size-fits-all video programs for mass audiences, appointment viewing of a TV schedule and the very concept of TV channels, and the outdated intrusion of interruption advertising. Personalized programming and fully-responsive adbots will be the new norm.

The report presents an in-depth assessment of the Artificial Intelligence-Emotion Recognition including enabling technologies, key trends, market drivers, challenges, standardization, regulatory landscape, deployment models, operator case studies, opportunities, future roadmap, value chain, ecosystem player profiles and strategies. The report also presents forecasts for Artificial Intelligence-Emotion Recognition investments from 2019 till 2025. The Global Artificial Intelligence-Emotion Recognition Market is expected to grow from USD 813.56 Million in 2018 to USD 1,890.67 The positioning of the Global Artificial Intelligence-Emotion Recognition Market vendors in FPNV Positioning Matrix are determined by Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) and placed into four quadrants (F: Forefront, P: Pathfinders, N: Niche, and V: Vital). The report presents the market competitive landscape and a corresponding detailed analysis of the major vendor/key players in the market.

This paper provides a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack (PHY, MAC and network). First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning for non-machine learning experts to understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

In mid December, LinkedIn revealed that Artificial Intelligence (AI) Specialist was the top emerging job for 2020; this may not be of much surprise but it does beg the question of what AI specialists actually do, and what the job entails. What are the exact skills and knowledge businesses need from AI Specialists? Artificial Intelligence is a huge buzz word, and is discussed regularly in the media, sometimes conjuring images of robot workers and other futuristic scenarios. We want to break through the AI haze and confusion, and drill down on what AI actually is; if these specialists are so in demand this calendar year, what is it that they will actually be delivering for small business? Dynamic Business has previously explored what AI is, and today we are building on that understanding, with an interview from Jeff Olson, who is Head of Applied AI & Analytics for ANZ, Cognizant.

The AIOps Catalyst team's work has resulted in a new collaborative workstream focused around the topic within TM Forum. Artificial intelligence (AI) offers huge opportunities for communications service providers (CSPs) to do things better, faster and cheaper. In fact, they have no choice but to introduce AI into operations and business processes due to growing complexity and the sheer volume of data and transactions. However, as well as delivering huge benefits, the introduction of AI also creates new challenges relating to the management of services and processes. A TM Forum Catalyst team is taking a two-pronged approach, tackling both these areas simultaneously to ensure CSPs – and their customers – reap the rewards of AI.

In mid December, LinkedIn revealed that Artificial Intelligence (AI) Specialist was the top emerging job for 2020; this may not be of much surprise but it does beg the question of what AI specialists actually do, and what the job entails. What are the exact skills and knowledge businesses need from AI Specialists? Artificial Intelligence is a huge buzz word, and is discussed regularly in the media, sometimes conjuring images of robot workers and other futuristic scenarios. We want to break through the AI haze and confusion, and drill down on what AI actually is; if these specialists are so in demand this calendar year, what is it that they will actually be delivering for small business? Dynamic Business has previously explored what AI is, and today we are building on that understanding, with an interview from Jeff Olson, who is Head of Applied AI & Analytics for ANZ, Cognizant.