With a more complex network comes more complex problems -- in the sense that tracking down the cause of a service outage or other issue will be more difficult because of the system's complexity. More importantly, the plethora of possibilities makes planning and managing a complex ecosystem more difficult, from determining how best to supply services based on current and future demand to where and how to deploy equipment and services in the most efficient manner possible. Such a complex network requires a simplified solution, and deploying artificial intelligence (AI) and machine learning (ML) to solve these problems could be the solution carriers need to ensure they remain in control of their networks. AI/ML solutions would enable carriers to bring the benefits of 5G to customers, as well as aligning to a CTO's KPIs, overcoming the current situation in which monolithic, centralized networks strain themselves to deliver the advanced services customers are increasingly demanding. AI solutions, of course, use machine learning to discover patterns in large datasets, providing a clear picture of cause and effect in resource usage to enable data enrichment for better prediction and decision-making timing, demand and a thousand other factors that determine the quality of connections, whether voice or data.

With a growth in advertisements and cold-messaging we are now receiving a nonstop coherent threads of commercial messages and emails. A user, like you and I, sometimes find it difficult to find a text/email which is actually useful to us or the one which we seek. Detection systems such as Spam detection system are becoming increasingly useful to classify the important data amongst the bundle of raw and undesired data. In this post we'll look at one such detection model, a spam detection model using NLP (natural language processing) and also learn about classification using Naïve Bayes. You can see that we are interested in calculating the posterior probability of P(h d) from the prior probability p(h) with P(D) and P(d h). UCI have an available data set of more than 5000 mixed text messages, click here.

Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time and reduces the testing effort. We have built a learning-based defect prediction model for a telecommunication company in the space of one year.

It is no secret that artificial intelligence (AI) is a technical marketing whitewash. Many companies claim that its algorithms and data scientists enable a differentiated approach in the networking infrastructure space. However, what are the practical applications of AI for connectivity and, in particular, 5G? Here I will provide my insights into each and highlight what I believe is the practical functionality for operators, subscribers and equipment providers. Automation is all about reducing human error and improving network performance and uptime through activities such as low to no-touch device configuration, provisioning, orchestration, monitoring, assurance and reactive issue resolution.

Is Artificial Intelligence(AI) making us lazy or efficient? I think it's making us efficient. Due to COVID-19, people are more often found interacting with their peers via social media and text messages. For instance, my push notifications are up by 37%, and positively enough I have reconnected with my school friends, old friends per se. However, this arose a problem of constantly sticking to my phone and suffering from Nomophobia and Phantom vibration syndrome.

Large content providers and content distribution network operators usually connect with large Internet service providers (eyeball networks) through dedicated private peering. The capacity of these private network interconnects is provisioned to match the volume of the real content demand by the users. Unfortunately, in case of a surge in traffic demand, for example due to a content trending in a certain country, the capacity of the private interconnect may deplete and the content provider/distributor would have to reroute the excess traffic through transit providers. Although, such overflow events are rare, they have significant negative impacts on content providers, Internet service providers, and end-users. These include unexpected delays and disruptions reducing the user experience quality, as well as direct costs paid by the Internet service provider to the transit providers. If the traffic overflow events could be predicted, the Internet service providers would be able to influence the routes chosen for the excess traffic to reduce the costs and increase user experience quality. In this article we propose a method based on an ensemble of deep learning models to predict overflow events over a short term horizon of 2-6 hours and predict the specific interconnections that will ingress the overflow traffic. The method was evaluated with 2.5 years' traffic measurement data from a large European Internet service provider resulting in a true-positive rate of 0.8 while maintaining a 0.05 false-positive rate. The lockdown imposed by the COVID-19 pandemic reduced the overflow prediction accuracy. Nevertheless, starting from the end of April 2020 with the gradual lockdown release, the old models trained before the pandemic perform equally well.

Amdocs' Machine Learning-driven optimization and analytics solution will now deliver a higher quality of service for 5G massive multi-antenna systems. A leading provider of software and services to communications and media companies Amdocs chose to partner with Intel to enhance 5G adoption across diverse global markets. The integration of Amdocs' SmartRAN optimization solution with Intel's FlexRAN software reference architecture will enable mobile operators to better fulfill service level objectives and deliver exceptional user experiences across their 5G vRAN by embedding intelligence in every layer of the RAN. Amdocs SmartRAN currently serves as a blueprint to help speed the development of virtualized RAN (vRAN) solutions. More on 5G Technology: Nokia Signs 5G Deal To Become BT's Largest Infrastructure Partner Integrating Intel's FlexRAN with machine learning libraries into the solution further enhances SmartRAN's data analytics and management capabilities so that customers can better tune their network performance. Amdocs is also developing its SmartRAN solution to support testing use cases that are established by the O-RAN Alliance, starting with the massive multi-antenna use case.

Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. It has been considered to be one of the key missing components in the existing 5G network and is widely recognized to be one of the most sought-after functions for tomorrow's wireless 6G cellular systems. In this article, we identify the key requirements and challenges of edge-native AI in 6G. A self-learning architecture based on self-supervised Generative Adversarial Nets (GANs) is introduced to \blu{demonstrate the potential performance improvement that can be achieved by automatic data learning and synthesizing at the edge of the network}. We evaluate the performance of our proposed self-learning architecture in a university campus shuttle system connected via a 5G network. Our result shows that the proposed architecture has the potential to identify and classify unknown services that emerge in edge computing networks. Future trends and key research problems for self-learning-enabled 6G edge intelligence are also discussed.

The exponential rise in mobile traffic originating from mobile devices highlights the need for making mobility management in future networks even more efficient and seamless than ever before. Ultra-Dense Cellular Network vision consisting of cells of varying sizes with conventional and mmWave bands is being perceived as the panacea for the eminent capacity crunch. However, mobility challenges in an ultra-dense heterogeneous network with motley of high frequency and mmWave band cells will be unprecedented due to plurality of handover instances, and the resulting signaling overhead and data interruptions for miscellany of devices. Similarly, issues like user tracking and cell discovery for mmWave with narrow beams need to be addressed before the ambitious gains of emerging mobile networks can be realized. Mobility challenges are further highlighted when considering the 5G deliverables of multi-Gbps wireless connectivity, <1ms latency and support for devices moving at maximum speed of 500km/h, to name a few. Despite its significance, few mobility surveys exist with the majority focused on adhoc networks. This paper is the first to provide a comprehensive survey on the panorama of mobility challenges in the emerging ultra-dense mobile networks. We not only present a detailed tutorial on 5G mobility approaches and highlight key mobility risks of legacy networks, but also review key findings from recent studies and highlight the technical challenges and potential opportunities related to mobility from the perspective of emerging ultra-dense cellular networks.

By Sam Nussey2 Min ReadTOKYO (Reuters) – SoftBank's robotics arm said on Monday it will bring a food service robot developed by California-based Bear Robotics to Japan as restaurants grapple with labour shortages and seek to ensure social distancing during the COVID-19 pandemic.Slideshow ( 3 images)The robot named Servi, which has layers of trays and is equipped with 3D cameras and Lidar sensors for navigation, will launch in January, SoftBank Group Corp said.Servi will cost 99,800 yen ($950) per month excluding tax on a three year plan.The launch leverages SoftBank's long experience in bringing overseas technology to Japan but reflects the shift away from CEO Masayoshi Son's earlier focus on humanoid robots.Servi has been tested by Japanese restaurant operators, including Seven & i Holdings at its Denny's chain, as the sector grapples with an aging workforce and deepening labour shortages.SoftBank's humanoid Pepper robot became the face of the company following its 2014 unveiling but failed to find a global customer base.The firm in 2018 announced cleaning robot Whiz, which employs technology from group portfolio company Brain Corp and has sold more than 10,000 units worldwide.SoftBank is touting the use of Whiz as a coronavirus countermeasure, …