On Tuesday, at its annual Snapdragon Summit in Hawaii, Qualcomm announced a major addition to its line of mobile chips with the Snapdragon X Elite, which the company is calling its most powerful processor to date. The Arm-based Snapdragon X Elite is the successor to last year's Snapdragon 8cx Gen 3 line of laptop chips, which recently got a name change to reflect the huge leap in performance for this upcoming generation. Powered by 12 Oryon cores, Qualcomm claims the X Elite provides up to two times faster CPU performance compared to Intel's 13th-gen Core i7-1360P and i7-1355U processors while also drawing up to 68 percent less power. The chip is based on a 4nm design fabricated by TSMC with standard clock speeds of 3.8GHz with a dual-core boost of up to 4.3GHz. Qualcomm also includes 42MB of total cache with an LPDDR5x memory bandwidth of 136 GB/s.

Qualcomm and its Snapdragon chips are officially back inside the PC. Today, Qualcomm formally launched the Snapdragon X Elite, the flagship platform of its Snapdragon X family that leverages its Oryon CPU core, and promises to double -- yes, double -- the performance of some of the most popular 13th-gen Core chips from AMD and Intel. Qualcomm promised the same with its earlier Snapdragon 8-series chips, and really didn't deliver. But after buying chip designer Nuvia in 2021, Qualcomm is trying again, hoping that its superpowered Arm chips can once again make Windows on Arm PCs a competitor to conventional X86 PCs when they launch in mid-2024. And they're talking some big numbers to prove it.

While companies like Microsoft and Nvidia are all-in on the power of next-generation machine learning algorithms, some regulators are dreading what it might mean for our already-stressed communication networks. The chairwoman of the US Federal Communications Commission, for one, who's just proposed an investigation into what "AI" could mean for even more spam calls and texts. The FCC will vote to adopt a multi-tiered action in November. Chairwoman Rosencworcel, who's served on the Commission since 2012 and as its executive since being confirmed late in 2021, is particularly concerned with how newly empowered AI tools could affect senior citizens. The FCC's initial press release (PDF link) lists four main goals: determining whether AI technologies fall under the Comission's jurisdiction via the Telephone Consumer Protection Act of 1991, if and when future AI tech might do the same, how AI impacts existing regulatory frameworks, and if the FCC should consider ways to verify the authenticity of auto-generated AI voice and text from "trusted sources."

In recent years, the need for resources for handling processes with high computational complexity for mobile robots is becoming increasingly urgent. More specifically, robots need to autonomously operate in a robust and continuous manner, while keeping high performance, a need that led to the utilization of edge computing to offload many computationally demanding and time-critical robotic procedures. However, safe mechanisms should be implemented to handle situations when it is not possible to use the offloaded procedures, such as if the communication is challenged or the edge cluster is not available. To this end, this article presents a switching strategy for safety, redundancy, and optimized behavior through an edge computing-based Model Predictive Controller (MPC) and a low-level onboard-PID controller for edge-connected Unmanned Aerial Vehicles (UAVs). The switching strategy is based on the communication Key Performance Indicators (KPIs) over 5G to decide whether the UAV should be controlled by the edge-based or have a safe fallback based on the onboard controller.

In the context of IoT deployments, a multitude of devices concurrently require network access to transmit data over a shared communication channel. Employing symmetric strategies can effectively facilitate the collaborative use of the communication medium among these devices. By adopting such strategies, devices collectively optimize their transmission parameters, resulting in minimized collisions and enhanced overall network throughput. Our primary focus centers on the formulation of symmetric (i.e., identical) strategies for the sensors, aiming to optimize a finite horizon team objective. The imposition of symmetric strategies introduces novel facets and complexities into the team problem. To address this, we embrace the common information approach and adapt it to accommodate the use of symmetric strategies. This adaptation yields a dynamic programming framework grounded in common information, wherein each step entails the minimization of a single function mapping from an agent's private information space to the space of probability distributions over possible actions. Our proposed policy/method incurs a reduced cumulative cost compared to other methods employing symmetric strategies, a point substantiated by our simulation results.

We introduce TeleQnA, the first benchmark dataset designed to evaluate the knowledge of Large Language Models (LLMs) in telecommunications. Comprising 10,000 questions and answers, this dataset draws from diverse sources, including standards and research articles. This paper outlines the automated question generation framework responsible for creating this dataset, along with how human input was integrated at various stages to ensure the quality of the questions. Afterwards, using the provided dataset, an evaluation is conducted to assess the capabilities of LLMs, including GPT-3.5 and GPT-4. The results highlight that these models struggle with complex standards related questions but exhibit proficiency in addressing general telecom-related inquiries. Additionally, our results showcase how incorporating telecom knowledge context significantly enhances their performance, thus shedding light on the need for a specialized telecom foundation model. Finally, the dataset is shared with active telecom professionals, whose performance is subsequently benchmarked against that of the LLMs. The findings illustrate that LLMs can rival the performance of active professionals in telecom knowledge, thanks to their capacity to process vast amounts of information, underscoring the potential of LLMs within this domain. The dataset has been made publicly accessible on GitHub.

Traffic prediction represents one of the crucial tasks for smartly optimizing the mobile network. The research in this topic concentrated in making predictions in a centralized fashion, i.e., by collecting data from the different network elements. This translates to a considerable amount of energy for data transmission and processing. In this work, we propose a novel prediction framework based on edge computing which uses datasets obtained on the edge through a large measurement campaign. Two main Deep Learning architectures are designed, based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), and tested under different training conditions. In addition, Knowledge Transfer Learning (KTL) techniques are employed to improve the performance of the models while reducing the required computational resources. Simulation results show that the CNN architectures outperform the RNNs. An estimation for the needed training energy is provided, highlighting KTL ability to reduce the energy footprint of the models of 60% and 90% for CNNs and RNNs, respectively. Finally, two cutting-edge explainable Artificial Intelligence techniques are employed to interpret the derived learning models.

This paper examines the problem of information routing in a large-scale communication network, which can be formulated as a constrained statistical learning problem having access to only local information. We delineate a novel State Augmentation (SA) strategy to maximize the aggregate information at source nodes using graph neural network (GNN) architectures, by deploying graph convolutions over the topological links of the communication network. The proposed technique leverages only the local information available at each node and efficiently routes desired information to the destination nodes. We leverage an unsupervised learning procedure to convert the output of the GNN architecture to optimal information routing strategies. In the experiments, we perform the evaluation on real-time network topologies to validate our algorithms. Numerical simulations depict the improved performance of the proposed method in training a GNN parameterization as compared to baseline algorithms.

Data Augmentation (DA)-augmenting training data with synthetic samples-is wildly adopted in Computer Vision (CV) to improve models performance. Conversely, DA has not been yet popularized in networking use cases, including Traffic Classification (TC). In this work, we present a preliminary study of 14 hand-crafted DAs applied on the MIRAGE19 dataset. Our results (i) show that DA can reap benefits previously unexplored in TC and (ii) foster a research agenda on the use of generative models to automate DA design.

The rapid advancements in Artificial Intelligence (AI), particularly in generative AI and foundational models (FMs), have ushered in transformative changes across various industries. Large language models (LLMs), a type of FM, have demonstrated their prowess in natural language processing tasks and content generation, revolutionizing how we interact with software products and services. This article explores the integration of FMs in the telecommunications industry, shedding light on the concept of AI native telco, where AI is seamlessly woven into the fabric of telecom products. It delves into the engineering considerations and unique challenges associated with implementing FMs into the software life cycle, emphasizing the need for AI native-first approaches. Despite the enormous potential of FMs, ethical, regulatory, and operational challenges require careful consideration, especially in mission-critical telecom contexts. As the telecom industry seeks to harness the power of AI, a comprehensive understanding of these challenges is vital to thrive in a fiercely competitive market.