Millimeter wave (mmWave) communication has emerged as a propelling technology in vehicular communication. Usually, an appropriate decision on user association requires timely channel information between vehicles and base stations (BSs), which is challenging given a fast-fading mmWave vehicular channel. In this paper, relying solely on learning transmission rate, we propose a low-complexity semi-distributed contextual correlated upper confidence bound (SD-CC-UCB) algorithm to establish an up-to-date user association without explicit measurement of channel state information (CSI). Under a contextual multi-arm bandits framework, SD-CC-UCB learns and predicts the transmission rate given the location and velocity of the vehicle, which can adequately capture the intricate channel condition for a prompt decision on user association. Further, SD-CC-UCB efficiently identifies the set of candidate BSs which probably support supreme transmission rate by leveraging the correlated distributions of transmission rates on different locations. To further refine the learning transmission rate over the link to candidate BSs, each vehicle deploys the Thompson Sampling algorithm by taking the interference among vehicles and handover overhead into consideration. Numerical results show that our proposed algorithm achieves the network throughput within 100%-103% of a benchmark algorithm which requires perfect instantaneous CSI, demonstrating the effectiveness of SD-CC-UCB in vehicular communications.

This paper aims to simultaneously optimize indoor wireless and daylight performance by adjusting the positions of windows and the beam directions of window-deployed reconfigurable intelligent surfaces (RISs) for RIS-aided outdoor-to-indoor (O2I) networks utilizing large language models (LLM) as optimizers. Firstly, we illustrate the wireless and daylight system models of RIS-aided O2I networks and formulate a joint optimization problem to enhance both wireless traffic sum rate and daylight illumination performance. Then, we present a multi-modal LLM-based window optimization (LMWO) framework, accompanied by a prompt construction template to optimize the overall performance in a zero-shot fashion, functioning as both an architect and a wireless network planner. Finally, we analyze the optimization performance of the LMWO framework and the impact of the number of windows, room size, number of RIS units, and daylight factor. Numerical results demonstrate that our proposed LMWO framework can achieve outstanding optimization performance in terms of initial performance, convergence speed, final outcomes, and time complexity, compared with classic optimization methods. The building's wireless performance can be significantly enhanced while ensuring indoor daylight performance.

In recent years, several studies have explored deep learning algorithms to predict large-scale signal fading, or path loss, in urban communication networks. The goal is to replace costly measurement campaigns, inaccurate statistical models, or computationally expensive ray-tracing simulations with machine learning models that deliver quick and accurate predictions. We focus on predicting path loss radio maps using convolutional neural networks, leveraging aerial images alone or in combination with supplementary height information. Notably, our approach does not rely on explicit classification of environmental objects, which is often unavailable for most locations worldwide. While the prediction of radio maps using complete 3D environmental data is well-studied, the use of only aerial images remains under-explored. We address this gap by showing that state-of-the-art models developed for existing radio map datasets can be effectively adapted to this task, achieving strong performance. Additionally, we introduce a new model that slightly exceeds the performance of the present state-of-the-art with reduced complexity. The trained models are differentiable, and therefore they can be incorporated in various network optimization algorithms. While an extensive discussion is beyond this paper's scope, we demonstrate this through an example optimizing the directivity of base stations in cellular networks via backpropagation to enhance coverage.

In the field of telecommunications and cloud communications, accurately and in real-time detecting whether a human or an answering machine has answered an outbound call is of paramount importance. This problem is of particular significance during campaigns as it enhances service quality, efficiency and cost reduction through precise caller identification. Despite the significance of the field, it remains inadequately explored in the existing literature. This paper presents an innovative approach to answering machine detection that leverages transfer learning through the YAMNet model for feature extraction. The YAMNet architecture facilitates the training of a recurrent-based classifier, enabling real-time processing of audio streams, as opposed to fixed-length recordings. The results demonstrate an accuracy of over 96% on the test set. Furthermore, we conduct an in-depth analysis of misclassified samples and reveal that an accuracy exceeding 98% can be achieved with the integration of a silence detection algorithm, such as the one provided by FFmpeg.

Cloud robotics enables robots to offload computationally intensive tasks to cloud servers for performance, cost, and ease of management. However, the network and cloud computing infrastructure are not designed for reliable timing guarantees, due to fluctuating Quality-of-Service (QoS). In this work, we formulate an impossibility triangle theorem for: Latency reliability, Singleton server, and Commodity hardware. The LSC theorem suggests that providing replicated servers with uncorrelated failures can exponentially reduce the probability of missing a deadline. We present FogROS2-Probabilistic Latency Reliability (PLR) that uses multiple independent network interfaces to send requests to replicated cloud servers and uses the first response back. We design routing mechanisms to discover, connect, and route through non-default network interfaces on robots. FogROS2-PLR optimizes the selection of interfaces to servers to minimize the probability of missing a deadline. We conduct a cloud-connected driving experiment with two 5G service providers, demonstrating FogROS2-PLR effectively provides smooth service quality even if one of the service providers experiences low coverage and base station handover. We use 99 Percentile (P99) latency to evaluate anomalous long-tail latency behavior. In one experiment, FogROS2-PLR improves P99 latency by up to 3.7x compared to using one service provider. We deploy FogROS2-PLR on a physical Stretch 3 robot performing an indoor human-tracking task. Even in a fully covered Wi-Fi and 5G environment, FogROS2-PLR improves the responsiveness of the robot reducing mean latency by 36% and P99 latency by 33%.

Traditional network management algorithms have relied on prior knowledge of system models and networking scenarios. In practice, a universal optimization framework is desirable where a sole optimization module can be readily applied to arbitrary network management tasks without any knowledge of the system. To this end, knowledge-free optimization techniques are necessary whose operations are independent of scenario-specific information including objective functions, system parameters, and network setups. The major challenge of this paradigm-shifting approach is the requirement of a hyperintelligent black-box optimizer that can establish efficient decision-making policies using its internal reasoning capabilities. This article presents a novel knowledge-free network management paradigm with the power of foundation models called large language models (LLMs). Trained on vast amounts of datasets, LLMs can understand important contexts from input prompts containing minimal system information, thereby offering remarkable inference performance even for entirely new tasks. Pretrained LLMs can be potentially leveraged as foundation models for versatile network optimization. By eliminating the dependency on prior knowledge, LLMs can be seamlessly applied for various network management tasks. The viability of this approach is demonstrated for resource management problems using GPT-3.5-Turbo. Numerical results validate that knowledge-free LLM optimizers are able to achieve comparable performance to existing knowledge-based optimization algorithms. H. Lee is with the Department of Electrical Engineering and the Artificial Intelligence Graduate School, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Korea.

SoftBank Group founder Masayoshi Son sketched out one of the most aggressive timelines for the adoption of artificial intelligence yet, envisioning a near future where the technology would run entire households. AI will soon be able to monitor the health of family members, call the doctor when needed, do grocery shopping, make reservations, judge optimal investments and tutor young children, Son said in a speech at an annual forum for enterprise clients on Thursday. He moved up his expectation for when artificial general intelligence -- the long-term goal for developers from OpenAI to Meta Platforms and Alphabet's Google -- would arrive to within the next two to three years.

There is an ongoing effort to develop feature selection algorithms to improve interpretability, reduce computational resources, and minimize overfitting in predictive models. Neural networks stand out as architectures on which to build feature selection methods, and recently, neuron pruning and regrowth have emerged from the sparse neural network literature as promising new tools. We introduce RelChaNet, a novel and lightweight feature selection algorithm that uses neuron pruning and regrowth in the input layer of a dense neural network. For neuron pruning, a gradient sum metric measures the relative change induced in a network after a feature enters, while neurons are randomly regrown. We also propose an extension that adapts the size of the input layer at runtime. Extensive experiments on nine different datasets show that our approach generally outperforms the current state-of-the-art methods, and in particular improves the average accuracy by 2% on the MNIST dataset. Feature selection is an elemental task in predictive modelling. It can serve to reduce computational resources, improve interpretability by highlighting important features, or improve predictive performance by reducing overfitting (Li et al., 2018). To further these goals has been the driving motivation of large recent efforts to improve existing and develop new feature selection algorithms.

Establishing and maintaining 5G mmWave vehicular connectivity poses a significant challenge due to high user mobility that necessitates frequent triggering of beam switching procedures. Departing from reactive beam switching based on the user device channel state feedback, proactive beam switching prepares in advance for upcoming beam switching decisions by exploiting accurate channel state information (CSI) prediction. In this paper, we develop a framework for autonomous self-trained CSI prediction for mmWave vehicular users where a base station (gNB) collects and labels a dataset that it uses for training recurrent neural network (RNN)-based CSI prediction model. The proposed framework exploits the CSI feedback from vehicular users combined with overhearing the C-V2X cooperative awareness messages (CAMs) they broadcast. We implement and evaluate the proposed framework using deepMIMO dataset generation environment and demonstrate its capability to provide accurate CSI prediction for 5G mmWave vehicular users. CSI prediction model is trained and its capability to provide accurate CSI predictions from various input features are investigated.

The big Windows 11 2024 Update (also known as Windows 11 24H2) is both a brand-new operating system but also one that's been out for several months now. And its best features are really reserved for those who have invested in a next-gen Copilot PC powered by chips from Qualcomm, Intel, and AMD. These seeming contradictions are at the heart of Windows 11 24H2, which begins rolling out today in a "phased" rollout that will last several weeks. But when you get it and what you get with it will all depend on whether you own a Copilot PC. In other words, there's a set of basic features that everyone will receive (including new energy-saving features for laptops and desktops, improved smartphone integration, plus support for Wi-Fi 7 and the upgraded 80Gbps capabilities of USB4), along with more advanced features that are only available to Copilot PC users.