Lunar Lake is Intel's Snapdragon killer. Intel's Core Ultra Series 2 (Lunar Lake) was specifically designed to emphasize low power, but with competitive performance. In this it somewhat succeeds, though the Core Ultra 7 258V chip I tested can still run a distant second, or third, behind AMD's mobile Ryzen processors. But Lunar Lake also provides incredibly good, Snapdragon-like battery life with a powerful, embedded GPU capable of playing yesterday's top-tier games. Intel supplied us with a Lunar Lake-powered Asus ZenBook S14 laptop for review, and we've spent the last week or so testing it to answer the question: Of the AMD Ryzen AI 300, Intel's Lunar Lake, and the Qualcomm Snapdragon X Elite, which is the best laptop processor so far in 2024? And how does Lunar Lake compare to its predecessor, Meteor Lake?

Traffic routing is vital for the proper functioning of the Internet. As users and network traffic increase, researchers try to develop adaptive and intelligent routing algorithms that can fulfill various QoS requirements. Reinforcement Learning (RL) based routing algorithms have shown better performance than traditional approaches. We developed a QoS-aware, reusable RL routing algorithm, RLSR-Routing over SDN. During the learning process, our algorithm ensures loop-free path exploration. While finding the path for one traffic demand (a source destination pair with certain amount of traffic), RLSR-Routing learns the overall network QoS status, which can be used to speed up algorithm convergence when finding the path for other traffic demands. By adapting Segment Routing, our algorithm can achieve flow-based, source packet routing, and reduce communications required between SDN controller and network plane. Our algorithm shows better performance in terms of load balancing than the traditional approaches. It also has faster convergence than the non-reusable RL approach when finding paths for multiple traffic demands.

The rapid advancement in generative pre-training models is propelling a paradigm shift in technological progression from basic applications such as chatbots towards more sophisticated agent-based systems. It is with huge potential and necessity that the 6G system be combined with the copilot of large language model (LLM) agents and digital twins (DT) to manage the highly complicated communication system with new emerging features such as native AI service and sensing. With the 6G-oriented agent, the base station could understand the transmission requirements of various dynamic upper-layer tasks, automatically orchestrate the optimal system workflow. Through continuously get feedback from the 6G DT for reinforcement, the agents can finally raise the performance of practical system accordingly. Differing from existing LLM agents designed for general application, the 6G-oriented agent aims to make highly rigorous and precise planning with a vast amount of extra expert knowledge, which inevitably requires a specific system design from model training to implementation. This paper proposes a novel comprehensive approach for building task-oriented 6G LLM agents. We first propose a two-stage continual pre-training and fine-tuning scheme to build the field basic model and diversities of specialized expert models for meeting the requirements of various application scenarios. Further, a novel inference framework based on semantic retrieval for leveraging the existing communication-related functions is proposed. Experiment results of exemplary tasks, such as physical-layer task decomposition, show the proposed paradigm's feasibility and effectiveness.

Large Language models (LLMs) have brought about substantial advancements in the field of Question Answering (QA) systems. These models do remarkably well in addressing intricate inquiries in a variety of disciplines. However, because of domain-specific vocabulary, complex technological concepts, and the requirement for exact responses applying LLMs to specialized sectors like telecommunications presents additional obstacles. GPT-3.5 has been used in recent work, to obtain noteworthy accuracy for telecom-related questions in a Retrieval Augmented Generation (RAG) framework. Notwithstanding these developments, the practical use of models such as GPT-3.5 is restricted by their proprietary nature and high computing demands. This paper introduces QMOS, an innovative approach which uses a Question-Masked loss and Option Shuffling trick to enhance the performance of LLMs in answering Multiple-Choice Questions in the telecommunications domain. Our focus was on using opensource, smaller language models (Phi-2 and Falcon-7B) within an enhanced RAG framework. Our multi-faceted approach involves several enhancements to the whole LLM-RAG pipeline of finetuning, retrieval, prompt engineering and inference. Our approaches significantly outperform existing results, achieving accuracy improvements from baselines of 24.70% to 49.30% with Falcon-7B and from 42.07% to 84.65% with Phi-2.

Effective internet traffic prediction in smaller ISP networks is challenged by limited data availability. This paper explores this issue using transfer learning and data augmentation techniques with two LSTM-based models, LSTMSeq2Seq and LSTMSeq2SeqAtn, initially trained on a comprehensive dataset provided by Juniper Networks and subsequently applied to smaller datasets. The datasets represent real internet traffic telemetry, offering insights into diverse traffic patterns across different network domains. Our study revealed that while both models performed well in single-step predictions, multi-step forecasts were challenging, particularly in terms of long-term accuracy. In smaller datasets, LSTMSeq2Seq generally outperformed LSTMSeq2SeqAtn, indicating that higher model complexity does not necessarily translate to better performance. The models' effectiveness varied across different network domains, reflecting the influence of distinct traffic characteristics. To address data scarcity, Discrete Wavelet Transform was used for data augmentation, leading to significant improvements in model performance, especially in shorter-term forecasts. Our analysis showed that data augmentation is crucial in scenarios with limited data. Additionally, the study included an analysis of the models' variability and consistency, with attention mechanisms in LSTMSeq2SeqAtn providing better short-term forecasting consistency but greater variability in longer forecasts. The results highlight the benefits and limitations of different modeling approaches in traffic prediction. Overall, this research underscores the importance of transfer learning and data augmentation in enhancing the accuracy of traffic prediction models, particularly in smaller ISP networks with limited data availability.

Abstract--Efficient satellite communications play an enormously important role in all of our daily lives. This includes the transmission of data for communication purposes, the operation of IoT applications or the provision of data for ground stations. More and more, AI-based methods are finding their way into these areas. This paper gives an overview of current research in the field of intelligent optimization of satellite communication. For this purpose, a text-mining based literature review was conducted and the identified papers were thematically clustered and analyzed. The identified clusters cover the main topics of routing, resource allocation and, load balancing. Through such a clustering of the literature in overarching topics, a structured analysis of the research papers was enabled, allowing the identification of latest technologies and approaches as well as research needs for intelligent optimization of satellite communication.

This paper presents a novel approach to compressing IP flow records using deep learning techniques, specifically autoencoders. Our method aims to significantly reduce data volume while maintaining the utility of the compressed data for downstream analysis tasks. We demonstrate the effectiveness of our approach through extensive experiments on a large-scale, real-world network traffic dataset. The proposed autoencoder-based compression achieves a 3.28x reduction in data size while preserving 99.20% accuracy in a multi-class traffic classification task, compared to 99.77% accuracy with uncompressed data. This marginal decrease in performance is offset by substantial gains in storage efficiency and potential improvements in processing speed. Our method shows particular promise in distinguishing between various modern application protocols, including encrypted traffic from popular services. The implications of this work extend to more efficient network monitoring, real-time analysis in resource-constrained environments, and scalable network management solutions.

Each specification defines the expected model output for a given input space ( 2.1). The increasing adoption of neural networks in learning-augmented systems highlights the importance Specifically, researchers have relied on their domain knowledge of model safety and robustness, particularly and intuition about individual applications to manually in safety-critical domains. Despite progress in create specifications. For instance, in adaptive video streaming, the formal verification of neural networks, current where a neural network is employed to determine the practices require users to manually define model bitrate for the next video chunk based on recent network specifications--properties that dictate expected conditions, Eliyahu et al. (2021) define a specification as, model behavior in various scenarios. This manual "[if video] chunks were downloaded quickly (more quickly process, however, is prone to human error, limited than it takes to play a chunk), the DNN should eventually in scope, and time-consuming. In this paper, not choose the worst resolution." Similar manual specifications we introduce AutoSpec, the first framework to are devised for other learning-augmented systems, e.g., automatically generate comprehensive and accurate database indexes (Tan et al., 2021), memory allocators (Wei specifications for neural networks in learningaugmented et al., 2023), and job schedulers (Wu et al., 2022).

It's possible to distribute the Internet to users via drones. However it is then necessary to place the drones according to the positions of the users. Moreover, the 5th Generation (5G) New Radio (NR) technology is designed to accommodate a wide range of applications and industries. The NGNM 5G White Paper \cite{5gwhitepaper} groups these vertical use cases into three categories: - enhanced Mobile Broadband (eMBB) - massive Machine Type Communication (mMTC) - Ultra-Reliable Low-latency Communication (URLLC). Partitioning the physical network into multiple virtual networks appears to be the best way to provide a customised service for each application and limit operational costs. This design is well known as \textit{network slicing}. Each drone must thus slice its bandwidth between each of the 3 user classes. This whole problem (placement + bandwidth) can be defined as an optimization problem, but since it is very hard to solve efficiently, it is almost always addressed by AI in the litterature. In my internship, I wanted to prove that viewing the problem as an optimization problem can still be useful, by building an hybrid solution involving on one hand AI and on the other optimization. I use it to achieve better results than approaches that use only AI, although at the cost of slightly larger (but still reasonable) computation times.

They enable efficient management of resources and network traffic, a definite advantage in the age of increasingly complex networks requiring dynamic management. By centralizing control and enabling flexible management, SDN offers new opportunities for network optimization. Nevertheless, fully realizing the potential of SDN requires the development of advanced and adaptive control methods. This article focuses on analyzing current methods of flexible control for SDN networks and presenting a solution to improve the efficiency and adaptability of network management. The approach presented is based on the application of machine learning, specifically the Reinforcement Learning (RL) [2]. This technique allows networks to make autonomous decisions based on previous experiences and dynamically changing conditions, which is similar to the way humans learn. The goal of the proposed solution is to not only increase network performance, but to improve its flexibility and real-time adaptability. The use of reinforcement learning enables dynamic and flexible control of network traffic, resulting in more efficient and responsive resource management [3]. The article reviews existing solutions and describes in detail the original approach developed in-its own, pointing out its potential benefits and implementation possibilities.