When people think about the Internet of Things (IoT) today they're often overly focused on the things--the devices that first gave the IoT its name. But like all things digital, the IoT is rapidly evolving from the very early days of simple sensors designed to help manufacturers automate their processes, to the wide-variety of connected devices that record our steps using our watches, and allow us to talk to people through our doorbells. Early on, in an effort to make sense of just how these devices fit into our world, people started referring to them as dumb and smart. This was fine for a while. Dumb devices (think sensors and actuators) were pretty easily defined as something that just did a simple task like reading temperature or sensing vibration and then, every now and then, reporting that information back to some kind of control system.

Existing approaches to solving combinatorial optimization problems on graphs suffer from the need to engineer each problem algorithmically, with practical problems recurring in many instances. The practical side of theoretical computer science, such as computational complexity, then needs to be addressed. Relevant developments in machine learning research on graphs are surveyed for this purpose. We organize and compare the structures involved with learning to solve combinatorial optimization problems, with a special eye on the telecommunications domain and its continuous development of live and research networks.

While the consumer-facing telecoms companies talk only about the speed of downloads, for manufacturing, the focus turns to ultra-reliable low-latency, density and ubiquitous connectivity. It's these lesser-known features, beyond the breakneck 5G speed, that will encourage industry to construct private 5G network infrastructure in industrial plants and warehouses. The sector is a production line for buzzwords; everything from the Industrial Internet of Things (IIoT) to Industry 4.0 are common, with'smart factories' and'edge computing' not far behind. From high-precision assembly lines and augmented reality overlays, to cloud robotics and cable-free factories, here are 12 ways 5G could transform manufacturing. Although it's an overstated part of 5G, there is no getting away from the fact that the ability to download data much, much faster will be a major attraction of 5G to the manufacturing industry.

It's clear that 5G is not only going to revolutionise the way consumers connect with each other, but also how enterprises around the world will streamline their operations. With this in mind, says Andrew Burrell, head of Ultra Broadband & Analytics services, Nokia, service providers need to carefully consider how they are going to ensure they can provide and manage the necessary quality standards to meet this increased demand when upgrading their systems to enable 5G delivery. While consumers may be forgiving of some buffering while streaming a film, the effect of latency on enterprises could interrupt their day-to-day business and potentially significantly increase their costs. It is not enough for service providers to invest in the hardware in order to deliver reliable 5G to consumers and businesses, the real stand-out value – and profit – lies in intelligent, automated operations to protect their networks and assure service quality. With 5G, network slicing will be imperative for service providers.

Google appeared to discontinue the original Home speaker when it recently marked the product as "no longer available" in its store. That move suggested a replacement smart speaker is on the way, and now we've gotten what's likely our first proper look at it. Recently certified by the FCC, here is our first look at GXCA6, the new @Google Nest Speaker, replacing the original Google Home. The Federal Communications Commission just certified some type of "wireless device" from the company, though many of the details about the gadget remained confidential. Reports have suggested that Google was working on a Nest-branded speaker to replace the Home and this filing fits the bill.

The Internet of Things (IoT), hailed as the enabler of the next industrial revolution, will require ubiquitous connectivity, context-aware and dynamic service mobility, and extreme security through the wireless network infrastructure. Artificial Intelligence (AI), thus, will play a major role in the underlying network infrastructure. However, a number of challenges will surface while using the concepts, tools and algorithms of AI in wireless networks used by IoT. In this article, the main challenges in using AI in the wireless network infrastructure that facilitate end-to-end IoT communication are highlighted with potential generalized solution and future research directions.

In some following posts, I will explore these other methods, such as Random Forest, Support Vector Modeling, and XGboost, to see if we can improve on this customer churn model! In my previous post, we completed a pretty in-depth walk through of the exploratory data analysis process for a customer churn analysis dataset. Our data, sourced from Kaggle, is centered around customer churn, the rate at which a commercial customer will leave the commercial platform that they are currently a (paying) customer, of a telecommunications company, Telco. Now that the EDA process has been complete, and we have a pretty good sense of what our data tells us before processing, we can move on to building a Logistic Regression classification model which will allow for us to predict whether a customer is at risk to churn from Telco's platform. The complete GitHub repository with notebooks and data walkthrough can be found here.

This report provides in detail, the market size, growth spectrum, and the competitive scenario of Artificial Intelligence (AI) for Telecommunication Market …

The strict latency and reliability requirements of ultra-reliable low-latency communications (URLLC) use cases are among the main drivers in fifth generation (5G) network design. Link adaptation (LA) is considered to be one of the bottlenecks to realize URLLC. In this paper, we focus on predicting the signal to interference plus noise ratio at the user to enhance the LA. Motivated by the fact that most of the URLLC use cases with most extreme latency and reliability requirements are characterized by semi-deterministic traffic, we propose to exploit the time correlation of the interference to compute useful statistics needed to predict the interference power in the next transmission. This prediction is exploited in the LA context to maximize the spectral efficiency while guaranteeing reliability at an arbitrary level. Numerical results are compared with state of the art interference prediction techniques for LA. We show that exploiting time correlation of the interference is an important enabler of URLLC.

In this paper, we propose a deep reinforcement learning (RL)-based precoding framework that can be used to learn an optimal precoding policy for complex multiple-input multiple-output (MIMO) precoding problems. We model the precoding problem for a single-user MIMO system as an RL problem in which a learning agent sequentially selects the precoders to serve the environment of MIMO system based on contextual information about the environmental conditions, while simultaneously adapting the precoder selection policy based on the reward feedback from the environment to maximize a numerical reward signal. We develop the RL agent with two canonical deep RL (DRL) algorithms, namely deep Q-network (DQN) and deep deterministic policy gradient (DDPG). To demonstrate the optimality of the proposed DRL-based precoding framework, we explicitly consider a simple MIMO environment for which the optimal solution can be obtained analytically and show that DQN- and DDPG-based agents can learn the near-optimal policy to map the environment state of MIMO system to a precoder that maximizes the reward function, respectively, in the codebook-based and non-codebook based MIMO precoding systems. Furthermore, to investigate the robustness of DRL-based precoding framework, we examine the performance of the two DRL algorithms in a complex MIMO environment, for which the optimal solution is not known. The numerical results confirm the effectiveness of the DRL-based precoding framework and show that the proposed DRL-based framework can outperform the conventional approximation algorithm in the complex MIMO environment.