Due to the advancement in technologies, the next-generation wireless network will be very diverse, complicated, and according to the changed demands of the consumers. The current network operator methodologies and approaches are traditional and cannot help the next generation networks to utilize their resources most appropriately. The limited capability of the traditional tools will not allow the network providers to fulfill the demands of the network's subscribers in the future. Therefore, this paper will focus on machine learning, automation, artificial intelligence, and big data analytics for improving the capacity and effectiveness of next-generation wireless networks. The paper will discuss the role of these new technologies in improving the service and performance of the network providers in the future. The paper will find out that machine learning, big data analytics, and artificial intelligence will help in making the next-generation wireless network self-adaptive, self-aware, prescriptive, and proactive. At the end of the paper, it will be provided that future wireless network operators cannot work without shifting their operational framework to AI and machine learning technologies.

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6G will move mobile networks towards increasing levels of complexity. To deal with this complexity, optimization of network parameters is key to ensure high performance and timely adaptivity to dynamic network environments. The optimization of the antenna tilt provides a practical and cost-efficient method to improve coverage and capacity in the network. Previous methods based on Reinforcement Learning (RL) have shown great promise for tilt optimization by learning adaptive policies outperforming traditional tilt optimization methods. However, most existing RL methods are based on single-cell features representation, which fails to fully characterize the agent state, resulting in suboptimal performance. Also, most of such methods lack scalability, due to state-action explosion, and generalization ability. In this paper, we propose a Graph Attention Q-learning (GAQ) algorithm for tilt optimization. GAQ relies on a graph attention mechanism to select relevant neighbors information, improve the agent state representation, and update the tilt control policy based on a history of observations using a Deep Q-Network (DQN). We show that GAQ efficiently captures important network information and outperforms standard DQN with local information by a large margin. In addition, we demonstrate its ability to generalize to network deployments of different sizes and densities.

The telecommunications industry is no longer limited to providing basic telephone and Internet services; It is now at the epicentre of technology growth, led by mobile and broadband services in the Internet of Things (IoT) age. This growth will continue, and its main engine will be Artificial Intelligence (AI). Today's communications service providers face a growing demand for higher quality services and a better customer experience. Telecommunications companies are taking advantage of these opportunities by using the vast amount of data collected from their immense customer bases over the years. This data telecom companies take from devices, networks, mobile applications, geolocation, detailed customer profiles, service use and billing information.

It's a telecommunications company that provides home phone and internet services to residents in the USA. The company noticed that their customers have been churning for a while. And this has impacted their customer base and business revenue, hence they need a plan to retain their customers. What do they mean by Customer Churn or Churned Customers? People who stopped using their home phone and internet services are known as churned customers.

Next-generation solutions and products are hitting a wall with wi-fi: it's not fast enough, and latency and connectivity issues mean it's not reliable enough. What's an innovator to do? Focus on what's next: 5G and software-defined networking. Nick McKeown, senior vice president and general manager of the network and edge group at Intel Corporation says this technical leap is what will make future innovation possible, "Once you've got a software platform where you can change its behavior, you can start introducing previously absurd-sounding ideas," including, he continues, "fanciful ideas of automatic, real-time, closed-loop control of an entire network." While nascent, these technological advancements are already showing promise in practical applications. For example, in industrial settings where there's more analysis happening at the edge, having greater observability into the network is allowing for fine timescale responses to mechanical errors and broken equipment. "Corrective action could be something as mundane as a broken link, a broken piece of equipment, but it could actually be a functional incorrectness in the software that is controlling it," says McKeown. Grad students and programmers are taking advantage of the advancements in network technology to try out new ideas through academic projects. "One of the key ideas," says McKeown, "is to verify in real time that the network is operating according to a specification, formally checking against that specification in real time, as packets fly around in the network. This has never been done before." And although this idea remains in the realm of research projects, McKeown believes it exemplifies the promise of a software-based 5G networking future. Software-defined 5G networking promises applications that we can't yet even imagine, says McKeown. "New IoT apps combined with both public and private 5G is going to create a'Cambrian explosion' of new ideas that will manifest in ways that if we were to try to predict, we would get it wrong." Laurel Ruma: From MIT Technology Review, I'm Laurel Ruma and this is Business Lab. The show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

Robotic Process Automation (RPA): Robotic Process Automation is a technology that configures computer software to capture data and manipulate applications in the way it is done by humans. With RPA telecommunication providers can automate back-end activities such as data entry, reconciliation, or validation, streamline customer support as well as perform cross-sell and up-sell utilizing AI-powered assisted calls. RPA applications allow CSPs to reduce costs, enhance accuracy, improve efficiency and deliver a better customer experience. Intelligent Virtual Agents: Intelligent Virtual Agents based on AI technologies gain traction in the telecommunication sector, resulting in improved customer experience and satisfaction. Telecom providers have turned to virtual assistance to optimize the processing of the huge number of support requests for troubleshooting, billing inquiries, maintenance, device settings, etc. AI-powered assistants handle all service-type questions and process transactions efficiently and at high speed.

The DiTEC (Digital Twin for Evolutionary Changes in water networks) project proposes an evolutionary approach to real-time monitoring of sensor-rich critical infrastructures that detects inconsistency between measured sensor data and the expected situation, and performs real-time model update without needing additional calibration. Deep learning will be applied to create a data-driven simulation of the system. The system is applied to water networks, where, in case of leaks, valve degradation or sensor faults, the model will be adapted to the degraded network until the maintenance takes place, which can take a long time. The project will analyse the effect on data readings of different malfunctions, and construct a mitigating mechanism that allows to continue using the data, albeit in a limited capacity. As part of the DiTEC project, the role of the PhD student will be to analyse historical and real-time sensor data, which includes parameters such as water speed, pressure, quality, network topology, and construct a number of deep learning (such as CNN and LSTM) models to explain and predict the behavior of the network short and long term.

The next generation of satellite constellations is designed to better address the future needs of our connected society: highly-variable data demand, mobile connectivity, and reaching more under-served regions. Artificial Intelligence (AI) and learning-based methods are expected to become key players in the industry, given the poor scalability and slow reaction time of current resource allocation mechanisms. While AI frameworks have been validated for isolated communication tasks or subproblems, there is still not a clear path to achieve fully-autonomous satellite systems. Part of this issue results from the focus on subproblems when designing models, instead of the necessary system-level perspective. In this paper we try to bridge this gap by characterizing the system-level needs that must be met to increase satellite autonomy, and introduce three AI-based components (Demand Estimator, Offline Planner, and Real Time Engine) that jointly address them. We first do a broad literature review on the different subproblems and identify the missing links to the system-level goals. In response to these gaps, we outline the three necessary components and highlight their interactions. We also discuss how current models can be incorporated into the framework and possible directions of future work.

How confident can we be in the generalization capacity of a predictive model? Of the many devices discussed in the statistical learning literature [1, 2, 3], a simple random split of the original data into training and test sets, and methods of folded cross-validation, stand out as the most common tools used to tackle the generalization issue. Availability of point estimates for the generalization error given by these procedures naturally raises the question of how to quantify the uncertainty involved in these estimates spending a manageable computational cost. Random forests [4] elegantly provide an alternative low cost (almost free) point estimate of the generalization error without requiring splittings of the data, and avoiding the computational burden of retraining the predictive model several times. The bagging mechanism [5] used to construct the ensemble of trees implies that each training data point is not used (stays "out-of-bag") when growing approximately 36.8% of the trees in the forest. This property gives us the so called out-of-bag estimate of the random forest generalization error: for each observation, using a suitable loss function, we compute the predictive error made by the random subforest whose trees didn't include the observation under consideration in its training process; the out-of-bag estimate is the average of these prediction errors over the whole training sample. 1