Recent years witnessed a remarkable increase in the availability of data and computing resources in communication networks. This contributed to the rise of data-driven over model-driven algorithms for network automation. This paper investigates a Minimization of Drive Tests (MDT)-driven Deep Reinforcement Learning (DRL) algorithm to optimize coverage and capacity by tuning antennas tilts on a cluster of cells from TIM's cellular network. We jointly utilize MDT data, electromagnetic simulations, and network Key Performance indicators (KPIs) to define a simulated network environment for the training of a Deep Q-Network (DQN) agent. Some tweaks have been introduced to the classical DQN formulation to improve the agent's sample efficiency, stability, and performance. In particular, a custom exploration policy is designed to introduce soft constraints at training time. Results show that the proposed algorithm outperforms baseline approaches like DQN and best-fist search in terms of long-term reward and sample efficiency. Our results indicate that MDT-driven approaches constitute a valuable tool for autonomous coverage and capacity optimization of mobile radio networks.

Mathematical optimization (MO) technologies are being utilized today by leading global companies across industries – including aviation, energy, finance, logistics, telecommunications, manufacturing, media, and many more – to solve a wide range of complex, real-world problems, make optimal, data-driven decisions, and achieve greater operational efficiency. An increasing number of data scientists are adding MO into their analytics toolbox and developing applications that combine MO and machine learning (ML) technologies. In this series of webinars, we will show you how – with MO techniques – you can build interpretable models to tackle your prediction and classification problems. How to formulate an MO model. How to build an MO model using the Gurobi Python API.

Cloud computing is a reliable solution to provide distributed computation power. However, real-time response is still challenging regarding the enormous amount of data generated by the IoT devices in 5G and 6G networks. Thus, multi-access edge computing (MEC), which consists of distributing the edge servers in the proximity of end-users to have low latency besides the higher processing power, is increasingly becoming a vital factor for the success of modern applications. This paper addresses the problem of minimizing both, the network delay, which is the main objective of MEC, and the number of edge servers to provide a MEC design with minimum cost. This MEC design consists of edge servers placement and base stations allocation, which makes it a joint combinatorial optimization problem (COP). Recently, reinforcement learning (RL) has shown promising results for COPs. However, modeling real-world problems using RL when the state and action spaces are large still needs investigation. We propose a novel RL framework with an efficient representation and modeling of the state space, action space and the penalty function in the design of the underlying Markov Decision Process (MDP) for solving our problem.

They're old ideas, but machine learning and AI are now the communications industry's hot buzzwords, judging from last week's Mobile World Congress. "For mobile operators around the world, this is no longer an experiment," said Patrick Ostiguy, CEO of Accedian, during an MWC panel discussion on machine learning. Machine learning, which involves training a computer by feeding it examples and counterexamples, has been around for decades. The post office's optical mail-sorting machines are one example. True AI and deep learning, which strive to teach a brain how to teach itself, are also long-standing disciplines.

The idea of private 5G is similar to Network Slicing for public carrier technology. It provides the same function as network slicing i.e. reliability but through dedicated wireless network infrastructure deployed at the Enterprise. That means, one telecom operator will have partnerships with multiple cloud providers and one cloud provider will have partnerships with multiple telecom providers. What does it mean for applications? Whether you are a factory, a stadium or a health care provider, you can provide richer applications that depend on low latency and high bandwidth.

"I look to people like Ajit to give me some foresight into what is likely to happen," said Mr. Ravuri. During his four years leading the FCC, Mr. Pai sought to cut regulations, fight illegal robocalls and make more radio frequencies available for 5G wireless networks. While the U.S. is poised to be a leader in 5G deployment, regulatory disputes could scuttle further adoption, Mr. Pai said on a video call. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. "On the regulatory side, some of the disputes we've seen among federal agencies could slow down, or even potentially stop the reallocation of spectrum for commercial uses, and that is something that would be unfortunate, of course, for all consumers and enterprises and for the country writ large," said Mr. Pai, who was appointed to the helm of the agency by former President Donald Trump.

The number of smartphone users across the world has skyrocketed over the last decade and promises to do so in the future too. Additionally, most business functions can now be executed on mobile devices. However, despite the mobile surge, telecom operators around the world are still not that profitable, with average net profit margins hovering around the 17% mark. The main reasons for the middling profit rates are the high number of market rivals vouching for the same customer base and the high overhead expenses associated with the sector. Communication Service Providers (CSPs) need to become more data-driven to reduce such costs and, automatically, improve their profit margins.

Industries across the enterprise are advancing their use of both private and public 5G networks as an increasing number of CIOs and other leaders identify business opportunities that require the capacity, low latency and reliability that only the fifth generation of broadband cellular technology delivers. Recent research underscores that growth. The global 5G services market is expected to become a $664.75 billion market, according to a recent 5G market report from Grand View Research. That means a compound annual growth rate of 46.2% from 2021 to 2028. The research firm also predicted 5G adoption and use to grow in numerous industries, including agriculture, retail and utilities. CIOs and other executives across verticals will need to identify the business opportunities 5G enables and understand where and how it can be the differentiator.

The radio access network (RAN) is moving towards open interfaces that offer wireless applications rich opportunities for customization and optimization. In 5G and beyond, the use of a large number of antenna elements, referred to as Massive MIMO, is envisioned to be the key physical layer enabling technology to transform wireless access into a high-throughput, low-latency multi-user medium where interference can be mitigated based on beamforming techniques. When the control of these large number of antenna elements are exposed via open interfaces, the RAN becomes a platform for the next-generation learning-based intelligent algorithms to deliver self-optimizing network access and connectivity.

Let's start with a very basic example of hiring which is fully automated now. Basically it kills the demand of human efforts. There are a lot of process in our daily lives too which have gone from fully human labour to fully automated be it security systems, car washing, web check in at airports, arts & designs, AI-enabled photography and what not. Process automation shortens or eases manual tasks, often making the results more accessible to users. Automation typically decreases the need for human deliberation or exertion while performing a task.