In this letter, we design a federated contrastive learning (FedCL) framework aimed at supporting personalized semantic communication. Our FedCL enables collaborative training of local semantic encoders across multiple clients and a global semantic decoder owned by the base station. This framework supports heterogeneous semantic encoders since it does not require client-side model aggregation. Furthermore, to tackle the semantic imbalance issue arising from heterogeneous datasets across distributed clients, we employ contrastive learning to train a semantic centroid generator (SCG). This generator obtains representative global semantic centroids that exhibit intra-semantic compactness and inter-semantic separability. Consequently, it provides superior supervision for learning discriminative local semantic features. Additionally, we conduct theoretical analysis to quantify the convergence performance of FedCL. Simulation results verify the superiority of the proposed FedCL framework compared to other distributed learning benchmarks in terms of task performance and robustness under different numbers of clients and channel conditions, especially in low signal-to-noise ratio and highly heterogeneous data scenarios.

In today's world, the Internet is recognized as one of the essentials of human life, playing a significant role in communications, business, and lifestyle. The quality of internet services can have widespread negative impacts on individual and social levels. Consequently, Quality of Service (QoS) has become a fundamental necessity for service providers in a competitive market aiming to offer superior services. The success and survival of these providers depend on their ability to maintain high service quality and ensure satisfaction.Alongside QoS, the concept of Quality of Experience (QoE) has emerged with the development of telephony networks. QoE focuses on the user's satisfaction with the service, helping operators adjust their services to meet user expectations. Recent research shows a trend towards utilizing machine learning and deep learning techniques to predict QoE. Researchers aim to develop accurate models by leveraging large volumes of data from network and user interactions, considering various real-world scenarios. Despite the complexity of network environments, this research provides a practical framework for improving and evaluating QoE. This study presents a comprehensive framework for evaluating QoE in multimedia services, adhering to the ITU-T P.1203 standard which includes automated data collection processes and uses machine learning algorithms to predict user satisfaction based on key network parameters. By collecting over 20,000 data records from different network conditions and users, the Random Forest model achieved a prediction accuracy of 95.8% for user satisfaction. This approach allows operators to dynamically allocate network resources in real-time, maintaining high levels of customer satisfaction with minimal costs.

Network data is ubiquitous in many disciplines and application domains, including computer science, statistics, biology, and physics. These data, encoding relationships between units represented as nodes, are often accompanied by additional information about the nodes, usually referred to as node covariates, attributes, or metadata (Newman and Clauset, 2016; Liu, 2019; Chunaev, 2020). In these situations, a common goal is to understand the associations between the network connectivity and the node covariates. In our example, we consider international food commodity trade data represented as a network, where the nodes correspond to different countries and edge weights encode food commodity trade volumes between corresponding countries. The covariates at each node consist of economic and geographic information for each country, such as gross domestic product (GDP) per capita, birth rate and region. We wish to exploit that both datasets contain information about the nodes in order to better understand the structure of the network, node covariates and their relationship. Specifically, we seek to understand how economic and geographic factors explain the observed trade between countries, and identify additional information in the network that cannot be explained solely by these variables. There has been substantial work that incorporates network and node covariate information. Some examples include methods that use node covariates to improve community detection (Binkiewicz et al., 2017; Huang et al., 2023), dimensionality reduction (Zhao et al., 2022), regression with network information (Li et al., 2019) and mixed effect models for network edges (Hoff, 2005).

Apple Intelligence is coming, but not to every iPhone out there. In fact, you'll need to have a device with an A17 Pro processor or M-series chip to use many of the features unveiled during the Apple Intelligence portion of WWDC 2024. That means only iPhone 15 Pro owners (and those with an M-series iPad) will get the iOS 18-related Apple Intelligence (AI?) updates like Genmoji, Image Playground, the redesigned Siri and Writing Tools. It's not evident exactly why older devices using an A16 chip (like the iPhone 14 Pro) won't work with Apple Intelligence, given its neural engine seems more than capable compared to the M1. A closer look at the specs sheets of those two processors show that the main differences appear to be in memory and GPU prowess.

Modern wireless networks must reliably support a wide array of connectivity demands, encompassing various user needs across diverse scenarios. Machine-Type Communication (mMTC) is pivotal in these networks, particularly given the challenges posed by massive connectivity and sporadic device activation patterns. Traditional grant-based random access (GB-RA) protocols face limitations due to constrained orthogonal preamble resources. In response, the adoption of grant-free random access (GF-RA) protocols offers a promising solution. This paper explores the application of supervised machine learning models to tackle activity detection issues in scenarios where non-orthogonal preamble design is considered. We introduce a data-driven algorithm specifically designed for user activity detection in Cell-Free Massive Multiple-Input Multiple-Output (CF-mMIMO) networks operating under GF-RA protocols. Additionally, this study presents a novel clustering strategy that simplifies and enhances activity detection accuracy, assesses the resilience of the algorithm to input perturbations, and investigates the effects of adopting floating-to-fixed-point conversion on algorithm performance. Simulations conducted adhere to 3GPP standards, ensuring accurate channel modeling, and employ a deep learning approach to boost the detection capabilities of mMTC GF-RA devices. The results are compelling: the algorithm achieves an exceptional 99\% accuracy rate, confirming its efficacy in real-world applications.

Large Language Models (LLMs) have immense potential to transform the telecommunications industry. They could help professionals understand complex standards, generate code, and accelerate development. However, traditional LLMs struggle with the precision and source verification essential for telecom work. To address this, specialized LLM-based solutions tailored to telecommunication standards are needed. Retrieval-augmented generation (RAG) offers a way to create precise, fact-based answers. This paper proposes TelecomRAG, a framework for a Telecommunication Standards Assistant that provides accurate, detailed, and verifiable responses. Our implementation, using a knowledge base built from 3GPP Release 16 and Release 18 specification documents, demonstrates how this assistant surpasses generic LLMs, offering superior accuracy, technical depth, and verifiability, and thus significant value to the telecommunications field.

Information theory and machine learning are inextricably linked and have even been referred to as "two sides of the same coin". One particularly elegant connection is the essential equivalence between probabilistic generative modeling and data compression or transmission. In this article, we reveal the dual-functionality of deep generative models that reshapes both data compression for efficiency and transmission error concealment for resiliency. We present how the contextual predictive capabilities of powerful generative models can be well positioned to be strong compressors and estimators. In this sense, we advocate for viewing the deep generative modeling problem through the lens of end-to-end communications, and evaluate the compression and error restoration capabilities of foundation generative models. We show that the kernel of many large generative models is powerful predictor that can capture complex relationships among semantic latent variables, and the communication viewpoints provide novel insights into semantic feature tokenization, contextual learning, and usage of deep generative models. In summary, our article highlights the essential connections of generative AI to source and channel coding techniques, and motivates researchers to make further explorations in this emerging topic.

We consider the two-sample testing problem for networks, where the goal is to determine whether two sets of networks originated from the same stochastic model. Assuming no vertex correspondence and allowing for different numbers of nodes, we address a fundamental network testing problem that goes beyond simple adjacency matrix comparisons. We adopt the stochastic block model (SBM) for network distributions, due to their interpretability and the potential to approximate more general models. The lack of meaningful node labels and vertex correspondence translate to a graph matching challenge when developing a test for SBMs. We introduce an efficient algorithm to match estimated network parameters, allowing us to properly combine and contrast information within and across samples, leading to a powerful test. We show that the matching algorithm, and the overall test are consistent, under mild conditions on the sparsity of the networks and the sample sizes, and derive a chi-squared asymptotic null distribution for the test. Through a mixture of theoretical insights and empirical validations, including experiments with both synthetic and real-world data, this study advances robust statistical inference for complex network data.

Sharp said Friday that it has signed a basic agreement to grant telecommunications carrier SoftBank Corp. exclusive negotiating rights for the partial sale of its Sakai plant in Osaka Prefecture. Sharp will halt production at the Sakai plant by the end of September as it scales down its liquid crystal display business. SoftBank plans to take over about 440,000 square meters, or about 60%, of the plant site and build a large data center for the development of generative artificial intelligence. It aims to start construction this autumn and put the data center into full operation in 2025. The price for the part of the plant site will be decided later. SoftBank plans to operate the data center on its own, while allowing external organizations such as universities and research institutions to use it.

Radio Environment Maps (REMs) are crucial for numerous applications in Telecom. The construction of accurate Radio Environment Maps (REMs) has become an important and challenging topic in recent decades. In this paper, we present a method to estimate REMs using Graph Neural Networks. This approach utilizes both physical cell information and sparse geo-located signal strength measurements to estimate REMs. The method first divides and encodes mobile network coverage areas into a graph. Then, it inputs sparse geo-located signal strength measurements, characterized by Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ) metrics, into a Graph Neural Network Model to estimate REMs. The proposed architecture inherits the advantages of a Graph Neural Network to capture the spatial dependencies of network-wide coverage in contrast with network Radio Access Network node locations and spatial proximity of known measurements.