When implementing hierarchical federated learning over wireless networks, scalability assurance and the ability to handle both interference and device data heterogeneity are crucial. This work introduces a new two-level learning method designed to address these challenges, along with a scalable over-the-air aggregation scheme for the uplink and a bandwidth-limited broadcast scheme for the downlink that efficiently use a single wireless resource. To provide resistance against data heterogeneity, we employ gradient aggregations. Meanwhile, the impact of uplink and downlink interference is minimized through optimized receiver normalizing factors. We present a comprehensive mathematical approach to derive the convergence bound for the proposed algorithm, applicable to a multi-cluster wireless network encompassing any count of collaborating clusters, and provide special cases and design remarks. As a key step to enable a tractable analysis, we develop a spatial model for the setup by modeling devices as a Poisson cluster process over the edge servers and rigorously quantify uplink and downlink error terms due to the interference. Finally, we show that despite the interference and data heterogeneity, the proposed algorithm not only achieves high learning accuracy for a variety of parameters but also significantly outperforms the conventional hierarchical learning algorithm.

Online changepoint detection aims to detect anomalies and changes in real-time in high-frequency data streams, sometimes with limited available computational resources. This is an important task that is rooted in many real-world applications, including and not limited to cybersecurity, medicine and astrophysics. While fast and efficient online algorithms have been recently introduced, these rely on parametric assumptions which are often violated in practical applications. Motivated by data streams from the telecommunications sector, we build a flexible nonparametric approach to detect a change in the distribution of a sequence. Our procedure, NP-FOCuS, builds a sequential likelihood ratio test for a change in a set of points of the empirical cumulative density function of our data. This is achieved by keeping track of the number of observations above or below those points. Thanks to functional pruning ideas, NP-FOCuS has a computational cost that is log-linear in the number of observations and is suitable for high-frequency data streams. In terms of detection power, NP-FOCuS is seen to outperform current nonparametric online changepoint techniques in a variety of settings. We demonstrate the utility of the procedure on both simulated and real data.

Since the advent of personal computing devices, intelligent personal assistants (IPAs) have been one of the key technologies that researchers and engineers have focused on, aiming to help users efficiently obtain information and execute tasks, and provide users with more intelligent, convenient, and rich interaction experiences. With the development of smartphones and IoT, computing and sensing devices have become ubiquitous, greatly expanding the boundaries of IPAs. However, due to the lack of capabilities such as user intent understanding, task planning, tool using, and personal data management etc., existing IPAs still have limited practicality and scalability. Recently, the emergence of foundation models, represented by large language models (LLMs), brings new opportunities for the development of IPAs. With the powerful semantic understanding and reasoning capabilities, LLM can enable intelligent agents to solve complex problems autonomously. In this paper, we focus on Personal LLM Agents, which are LLM-based agents that are deeply integrated with personal data and personal devices and used for personal assistance. We envision that Personal LLM Agents will become a major software paradigm for end-users in the upcoming era. To realize this vision, we take the first step to discuss several important questions about Personal LLM Agents, including their architecture, capability, efficiency and security. We start by summarizing the key components and design choices in the architecture of Personal LLM Agents, followed by an in-depth analysis of the opinions collected from domain experts. Next, we discuss several key challenges to achieve intelligent, efficient and secure Personal LLM Agents, followed by a comprehensive survey of representative solutions to address these challenges.

In Integrated Sensing and Communication (ISAC) systems, matching the radar targets with communication user equipments (UEs) is functional to several communication tasks, such as proactive handover and beam prediction. In this paper, we consider a radar-assisted communication system where a base station (BS) is equipped with a multiple-input-multiple-output (MIMO) radar that has a double aim: (i) associate vehicular radar targets to vehicular equipments (VEs) in the communication beamspace and (ii) predict the beamforming vector for each VE from radar data. The proposed target-to-user (T2U) association consists of two stages. First, vehicular radar targets are detected from range-angle images, and, for each, a beamforming vector is estimated. Then, the inferred per-target beamforming vectors are matched with the ones utilized at the BS for communication to perform target-to-user (T2U) association. Joint multi-target detection and beam inference is obtained by modifying the you only look once (YOLO) model, which is trained over simulated range-angle radar images. Simulation results over different urban vehicular mobility scenarios show that the proposed T2U method provides a probability of correct association that increases with the size of the BS antenna array, highlighting the respective increase of the separability of the VEs in the beamspace. Moreover, we show that the modified YOLO architecture can effectively perform both beam prediction and radar target detection, with similar performance in mean average precision on the latter over different antenna array sizes.

The deployment of federated learning (FL) within vertical heterogeneous networks, such as those enabled by high-altitude platform station (HAPS), offers the opportunity to engage a wide array of clients, each endowed with distinct communication and computational capabilities. This diversity not only enhances the training accuracy of FL models but also hastens their convergence. Yet, applying FL in these expansive networks presents notable challenges, particularly the significant non-IIDness in client data distributions. Such data heterogeneity often results in slower convergence rates and reduced effectiveness in model training performance. Our study introduces a client selection strategy tailored to address this issue, leveraging user network traffic behaviour. This strategy involves the prediction and classification of clients based on their network usage patterns while prioritizing user privacy. By strategically selecting clients whose data exhibit similar patterns for participation in FL training, our approach fosters a more uniform and representative data distribution across the network. Our simulations demonstrate that this targeted client selection methodology significantly reduces the training loss of FL models in HAPS networks, thereby effectively tackling a crucial challenge in implementing large-scale FL systems.

Recent advances in electronics are enabling substantial processing to be performed at each node (robots, sensors) of a networked system. Local processing enables data compression and may mitigate measurement noise, but it is still slower compared to a central computer (it entails a larger computational delay). However, while nodes can process the data in parallel, the centralized computational is sequential in nature. On the other hand, if a node sends raw data to a central computer for processing, it incurs communication delay. This leads to a fundamental communication-computation trade-off, where each node has to decide on the optimal amount of preprocessing in order to maximize the network performance. We consider a network in charge of estimating the state of a dynamical system and provide three contributions. First, we provide a rigorous problem formulation for optimal real-time estimation in processing networks in the presence of delays. Second, we show that, in the case of a homogeneous network (where all sensors have the same computation) that monitors a continuous-time scalar linear system, the optimal amount of local preprocessing maximizing the network estimation performance can be computed analytically. Third, we consider the realistic case of a heterogeneous network monitoring a discrete-time multi-variate linear system and provide algorithms to decide on suitable preprocessing at each node, and to select a sensor subset when computational constraints make using all sensors suboptimal. Numerical simulations show that selecting the sensors is crucial. Moreover, we show that if the nodes apply the preprocessing policy suggested by our algorithms, they can largely improve the network estimation performance.

Qualcomm is ringing in the new year at CES 2024 in Las Vegas with some updates in its chip lineup that power virtual and mixed-reality headsets. The keynote, which will detail more about what's new for its anticipated AI-powered chip, will happen on January 10 at 5pm ET. It can be streamed on Qualcomm's website or directly on the CES keynote page. There might be some information divulged about Meta and Qualcomm's chip collaboration and how it could improve functionality on new gen VR headsets. Qualcomm has said that the technology has been engineered into a single chip architecture that allows it to support smaller and sleeker headsets.

Abstract--Spectrum sharing allows different protocols of the same standard (e.g., 802.11 family) or different standards (e.g., LTE and DVB) to coexist in overlapping frequency bands. Third, it utilizes an extensive 66 GB dataset of over-the-air (OTA) WiFi transmissions for training, which is released along with the code for community use. Legacy protocol detection methods are integrated into the RF receiver network interface card, enabling fast preamble I. However, updating the system for new protocols leads to backward compatibility issues and The increasing demand for wireless services has caused a potential detection errors in challenging wireless channels. This results in congested the other hand, software-defined radio (SDR) systems offer wireless spectrum environments as various communication flexibility but introduce higher latencies due to data transfer protocols coexist in the same frequency bands [2]. This paper transmissions further raise security concerns, posing demonstrates that using edge devices with CPU and GPU risks to critical operations [3]. Detecting diverse protocols in on a system-on-a-module (SOM), along with careful software crowded spectrums allows for intelligent strategies to mitigate design, can overcome the these limitations and enable realtime interference, improve spectral efficiency, and enhance overall processing for complex ML wireless applications on an wireless system performance, benefiting regulatory bodies, SDR-based edge device.

In the contemporary digital era, the Internet functions as an unparalleled catalyst, dismantling geographical and linguistic barriers particularly evident in texting. This evolution facilitates global communication, transcending physical distances and fostering dynamic cultural exchange. A notable trend is the widespread use of transliteration, where the English alphabet is employed to convey messages in native languages, posing a unique challenge for language technology in accurately detecting the source language. This paper addresses this challenge through a dataset of phone text messages in Hindi and Russian transliterated into English utilizing BERT for language classification and Google Translate API for transliteration conversion. The research pioneers innovative approaches to identify and convert transliterated text, navigating challenges in the diverse linguistic landscape of digital communication. Emphasizing the pivotal role of comprehensive datasets for training Large Language Models LLMs like BERT, our model showcases exceptional proficiency in accurately identifying and classifying languages from transliterated text. With a validation accuracy of 99% our models robust performance underscores its reliability. The comprehensive exploration of transliteration dynamics supported by innovative approaches and cutting edge technologies like BERT, positions our research at the forefront of addressing unique challenges in the linguistic landscape of digital communication. Beyond contributing to language identification and transliteration capabilities this work holds promise for applications in content moderation, analytics and fostering a globally connected community engaged in meaningful dialogue.

Deep Learning (DL) has enabled a paradigm shift in wireless communication system with data driven end-to-end (E2E) learning and optimization of the Physical Layer (PHY). By leveraging the representation learning of DL, E2E systems exhibit enhanced adaptability and performance in complex wireless environments, fulfilling the demands of 5G and beyond network systems and applications. The evolution of data-driven techniques in the PHY has enabled advanced semantic applications across various modalities including text, image, audio, video, and multi-modal transmissions. These applications transcend from traditional bit-level communication to semantic-level intelligent communication systems, which are capable of understanding and adapting to the context and intent of the data transmission. Although PHY as a DL architecture for data-driven E2E communication is a key factor in enabling semantic communication systems (SemCom), and various studies in recent years have surveyed them separately, their combination has not been thoroughly reviewed. Additionally, these are emerging fields that are still in their infancy, with several techniques having been developed and evolved in recent years. Therefore, this article provides a holistic review of data-driven PHY for E2E communication system, and their enabling semantic applications across different modalities. Furthermore, it identifies critical challenges and prospective research directions, providing a pivotal reference for future development of DL in PHY and SemCom.