The telecom is an essential part of our lives in the modern world. With the advent of 5G era and the rapid advance in other technology, the telecom network equipment is growing dramatically, which brings new complexities and challenges to operations - the management of co-existence of new and legacy networks. This causes a huge interest in AI among telecoms in a hope to resolve this inherent complexity. According Tractica's prediction, the telecom industry is going to invest $36.7 billion annually in AI developments. The global AI in telecommunication market is expected to reach $14.99B.

Social media, professional sports, and video games are driving rapid growth in live video streaming, on platforms such as Twitch and YouTube Live. Live streaming experience is very susceptible to short-time-scale network congestion since client playback buffers are often no more than a few seconds. Unfortunately, identifying such streams and measuring their QoE for network management is challenging, since content providers largely use the same delivery infrastructure for live and video-on-demand (VoD) streaming, and packet inspection techniques (including SNI/DNS query monitoring) cannot always distinguish between the two. In this paper, we design, build, and deploy ReCLive: a machine learning method for live video detection and QoE measurement based on network-level behavioral characteristics. Our contributions are four-fold: (1) We analyze about 23,000 video streams from Twitch and YouTube, and identify key features in their traffic profile that differentiate live and on-demand streaming. We release our traffic traces as open data to the public; (2) We develop an LSTM-based binary classifier model that distinguishes live from on-demand streams in real-time with over 95% accuracy across providers; (3) We develop a method that estimates QoE metrics of live streaming flows in terms of resolution and buffer stall events with overall accuracies of 93% and 90%, respectively; and (4) Finally, we prototype our solution, train it in the lab, and deploy it in a live ISP network serving more than 7,000 subscribers. Our method provides ISPs with fine-grained visibility into live video streams, enabling them to measure and improve user experience.

In general, worst-case assumptions are utilized to calculate the quality of transmission (QoT) with provisioning of high-margin requirements. Thus, precise estimation of the QoT for the lightpath (LP) establishment is crucial for reducing the provisioning margins. We propose and compare several data-driven machine learning (ML) models to make an accurate calculation of the QoT before the actual establishment of the LP in an unseen network. The proposed models are trained on the data acquired from an already established LP of a completely different network. The metric considered to evaluate the QoT of the LP is the generalized signal-to-noise ratio (GSNR), which accumulates the impact of both nonlinear interference and amplified spontaneous emission noise.

Active Position Estimation (APE) is the task of localizing one or more targets using one or more sensing platforms. APE is a key task for search and rescue missions, wildlife monitoring, source term estimation, and collaborative mobile robotics. Success in APE depends on the level of cooperation of the sensing platforms, their number, their degrees of freedom and the quality of the information gathered. APE control laws enable active sensing by satisfying either pure-exploitative or pure-explorative criteria. The former minimizes the uncertainty on position estimation; whereas the latter drives the platform closer to its task completion. In this paper, we define the main elements of APE to systematically classify and critically discuss the state of the art in this domain. We also propose a reference framework as a formalism to classify APE-related solutions. Overall, this survey explores the principal challenges and envisages the main research directions in the field of autonomous perception systems for localization tasks. It is also beneficial to promote the development of robust active sensing methods for search and tracking applications.

Predicting the bandwidth utilization on network links can be extremely useful for detecting congestion in order to correct them before they occur. In this paper, we present a solution to predict the bandwidth utilization between different network links with a very high accuracy. A simulated network is created to collect data related to the performance of the network links on every interface. These data are processed and expanded with feature engineering in order to create a training set. We evaluate and compare three types of machine learning algorithms, namely ARIMA (AutoRegressive Integrated Moving Average), MLP (Multi Layer Perceptron) and LSTM (Long Short-Term Memory), in order to predict the future bandwidth consumption. The LSTM outperforms ARIMA and MLP with very accurate predictions, rarely exceeding a 3\% error (40\% for ARIMA and 20\% for the MLP). We then show that the proposed solution can be used in real time with a reaction managed by a Software-Defined Networking (SDN) platform.

As the telecommunications space focuses most of its efforts on facilitating the transition from 4G to 5G, the R&D (research and development) world is already working on what's next--6G, the sixth generation of wireless technology. While we mustn't put the cart before the horse, research and analysis firms are already predicting 6G could be viable around 2030--and, in some predictions, even earlier. For instance, Statista's latest report on 6G suggests the 6G market in North America will be worth $364 million by 2028. Based on the R&D already underway in the U.S., Statista's report suggests the nation will be an early leader in 6G. ABI Research has also pinned 2028 and 2029 as early commercial deployment years for 6G.

Qualcomm today at its Snapdragon Summit 2021 announced a collaboration with Google Cloud to bring the latter's Neural Architecture Search to Qualcomm platforms. The move is designed to speed up the development of AI models at the edge. Qualcomm claims the announcement will make it the first system-on-a-chip (SoC) customer to offer the Google Cloud Vertex AI Neural Architecture Search services. It will first be available on the Snapdragon 8, Gen 1 Mobile Platform, followed by the Snapdragon portfolio across mobile, IoT, automotive, and XR platforms. As AI/ML hardware has become more widespread, attention has turned to the software stack, which often consists of point solutions.

On Wednesday, Qualcomm launched the Snapdragon 8cx Gen 3 processor platform for PCs, claiming that the chip will offer up to 85 percent more performance than the prior generation. That's good, given that the pandemic severely undercut the value proposition of Snapdragon-powered PCs--long battery life and always-on mobile connectivity--as office work life moved to the home office desk and couch. Qualcomm's Snapdragon 8cx Gen 2 arrived this past spring at about the performance of the original Microsoft Surface Laptop, though with substantially higher graphics chops. Qualcomm's aggressive performance predictions about the Snapdragon 8cx Gen 3 are based on its new process shrink--from 7nm in the Gen 2 to a new, aggressive 5nm node in Gen 3. In all, Qualcomm believes that the chip will offer 85 percent more CPU performance than the prior generation, and 60 percent additional GPU performance. "We focused on really driving these features and capabilities in the mainstream PC segment," said Miguel Nunes, vice president of product management for Qualcomm, in a briefing with reporters.

Qualcomm launched the Snapdragon 8 Gen 1 mobile processor for smartphones at the Qualcomm Tech Summit in Hawaii late on Tuesday, adding substantially more performance and AI-powered features to 2022 smartphones. However, one of those may be controversial. While you may be used to your phone always listening for commands, are you ready for its camera to be always on, too? In an interesting twist, Snapdragon 8 phones will even be able to mint NFTs. Now the new Snapdragon 8 Gen 1 is poised to help launch even more, beginning in the fourth quarter of this year.

Every December for the last few years, Qualcomm has held an annual event in Hawaii to announce its latest flagship mobile chipset. This year was no different with the company taking the opportunity to unveil the Snapdragon 8 Gen 1. That's right, for the second year in a row, Qualcomm is moving away from the sequential numbering scheme that has defined its processors for years. Just as the Snapdragon 865 gave way to the 888, the company will now replace the 888 with the Gen 1. The company says it's capable of theoretical download speeds of 10Gbps. That's one of those specs that's impressive on paper, but won't mean much out in the real world since some of the fastest 5G networks can't deliver speeds greater than 4Gbps in ideal conditions.