Combing Artificial Intelligence (AI) and wireless communication technologies has become one of the major technologies trends towards 2030. This includes using AI to improve the efficiency of the wireless transmission and supporting AI deployment with wireless networks. In this article, the latest progress of the Third Generation Partnership Project (3GPP) standards development is introduced. Concentrating on AI model distributed transfer and AI for Beam Management (BM) with wireless network, we introduce the latest studies and explain how the existing standards should be modified to incorporate the results from academia.

Recently, Unmanned Aerial Vehicles (UAVs) have attracted the attention of researchers in academia and industry for providing wireless services to ground users in diverse scenarios like festivals, large sporting events, natural and man-made disasters due to their advantages in terms of versatility and maneuverability. However, the limited resources of UAVs (e.g., energy budget and different service requirements) can pose challenges for adopting UAVs for such applications. Our system model considers a UAV swarm that navigates an area, providing wireless communication to ground users with RIS support to improve the coverage of the UAVs. In this work, we introduce an optimization model with the aim of maximizing the throughput and UAVs coverage through optimal path planning of UAVs and multi-RIS phase configurations. The formulated optimization is challenging to solve using standard linear programming techniques, limiting its applicability in real-time decision-making. Therefore, we introduce a two-step solution using deep reinforcement learning and particle swarm optimization. We conduct extensive simulations and compare our approach to two competitive solutions presented in the recent literature. Our simulation results demonstrate that our adopted approach is 20 \% better than the brute-force approach and 30\% better than the baseline solution in terms of QoS.

We consider a dynamic mechanism design problem where an auctioneer sells an indivisible good to two groups of buyers in every round, for a total of $T$ rounds. The auctioneer aims to maximize their discounted overall revenue while adhering to a fairness constraint that guarantees a minimum average allocation for each group. We begin by studying the static case ($T=1$) and establish that the optimal mechanism involves two types of subsidization: one that increases the overall probability of allocation to all buyers, and another that favors the group which otherwise has a lower probability of winning the item. We then extend our results to the dynamic case by characterizing a set of recursive functions that determine the optimal allocation and payments in each round. Notably, our results establish that in the dynamic case, the seller, on the one hand, commits to a participation reward to incentivize truth-telling, and on the other hand, charges an entry fee for every round. Moreover, the optimal allocation once more involves subsidization in favor of one group, where the extent of subsidization depends on the difference in future utilities for both the seller and buyers when allocating the item to one group versus the other. Finally, we present an approximation scheme to solve the recursive equations and determine an approximately optimal and fair allocation efficiently.

Due to the continuously improving capabilities of mobile edges, recommender systems start to deploy models on edges to alleviate network congestion caused by frequent mobile requests. Several studies have leveraged the proximity of edge-side to real-time data, fine-tuning them to create edge-specific models. Despite their significant progress, these methods require substantial on-edge computational resources and frequent network transfers to keep the model up to date. The former may disrupt other processes on the edge to acquire computational resources, while the latter consumes network bandwidth, leading to a decrease in user satisfaction. In response to these challenges, we propose a customizeD slImming framework for incompatiblE neTworks(DIET). DIET deploys the same generic backbone (potentially incompatible for a specific edge) to all devices. To minimize frequent bandwidth usage and storage consumption in personalization, DIET tailors specific subnets for each edge based on its past interactions, learning to generate slimming subnets(diets) within incompatible networks for efficient transfer. It also takes the inter-layer relationships into account, empirically reducing inference time while obtaining more suitable diets. We further explore the repeated modules within networks and propose a more storage-efficient framework, DIETING, which utilizes a single layer of parameters to represent the entire network, achieving comparably excellent performance. The experiments across four state-of-the-art datasets and two widely used models demonstrate the superior accuracy in recommendation and efficiency in transmission and storage of our framework.

The introduction of fifth-generation (5G) radio technology has revolutionized communications, bringing unprecedented automation, capacity, connectivity, and ultra-fast, reliable communications. However, this technological leap comes with a substantial increase in energy consumption, presenting a significant challenge. To improve the energy efficiency of 5G networks, it is imperative to develop sophisticated models that accurately reflect the influence of base station (BS) attributes and operational conditions on energy usage.Importantly, addressing the complexity and interdependencies of these diverse features is particularly challenging, both in terms of data processing and model architecture design. This paper proposes a novel 5G base stations energy consumption modelling method by learning from a real-world dataset used in the ITU 5G Base Station Energy Consumption Modelling Challenge in which our model ranked second. Unlike existing methods that omit the Base Station Identifier (BSID) information and thus fail to capture the unique energy fingerprint in different base stations, we incorporate the BSID into the input features and encoding it with an embedding layer for precise representation. Additionally, we introduce a novel masked training method alongside an attention mechanism to further boost the model's generalization capabilities and accuracy. After evaluation, our method demonstrates significant improvements over existing models, reducing Mean Absolute Percentage Error (MAPE) from 12.75% to 4.98%, leading to a performance gain of more than 60%.

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.