In the past decade, Unmanned Aerial Vehicles (UAVs) have grabbed the attention of researchers in academia and industry for their potential use in critical emergency applications, such as providing wireless services to ground users and collecting data from areas affected by disasters, due to their advantages in terms of maneuverability and movement flexibility. The UAVs' limited resources, energy budget, and strict mission completion time have posed challenges in adopting UAVs for these applications. Our system model considers a UAV swarm that navigates an area collecting data from ground IoT devices focusing on providing better service for strategic locations and allowing UAVs to join and leave the swarm (e.g., for recharging) in a dynamic way. In this work, we introduce an optimization model with the aim of minimizing the total energy consumption and provide the optimal path planning of UAVs under the constraints of minimum completion time and transmit power. The formulated optimization is NP-hard making it not applicable for real-time decision making. Therefore, we introduce a light-weight meta-reinforcement learning solution that can also cope with sudden changes in the environment through fast convergence. We conduct extensive simulations and compare our approach to three state-of-the-art learning models. Our simulation results prove that our introduced approach is better than the three state-of-the-art algorithms in providing coverage to strategic locations with fast convergence.

Samsung's big Unpacked event yesterday unashamedly focused on the company's annual flagship phone refresh. Just kidding: It's mostly just changes to cameras and screen size. Same as it's been since the Galaxy S20. While introducing the Galaxy S24, S24 and S24 Ultra, the company wheeled out streamer and YouTuber Pokimane to cheerlead the even brighter screens, while MrBeast -- who Samsung couldn't afford to have there in person? However, beyond the predictable spec bumps, Samsung went to town on AI features this year.

Samsung has leaned heavily into AI tricks for its latest premium S24 Android phones, including instant phone call translation, new Google search and advanced image and video editing features as it attempts to reignite waning consumer interest. The Galaxy S24 series, launched at an event in California on Wednesday, is led by the largest and most expensive titanium-clad "Ultra", which features the very latest Qualcomm chips, the brightest screens and most powerful cameras. But in a change for the dominant South Korean firm, hardware updates have taken a backseat to flashy features powered by its new Galaxy AI brand. Much of its additions play catchup with the competition from Google's Pixel range and others, including the use of the Android-maker's Gemini AI models. Samsung's new live translate feature allows real-time, two-way phone, voice and text conversations between 13 different languages, similar to that offered by Google Translate, while its "Chat Assist" helps check messages for the appropriate tone, going beyond simple spelling and grammar.

Edge caching is a promising solution for next-generation networks by empowering caching units in small-cell base stations (SBSs), which allows user equipments (UEs) to fetch users' requested contents that have been pre-cached in SBSs. It is crucial for SBSs to predict accurate popular contents through learning while protecting users' personal information. Traditional federated learning (FL) can protect users' privacy but the data discrepancies among UEs can lead to a degradation in model quality. Therefore, it is necessary to train personalized local models for each UE to predict popular contents accurately. In addition, the cached contents can be shared among adjacent SBSs in next-generation networks, thus caching predicted popular contents in different SBSs may affect the cost to fetch contents. Hence, it is critical to determine where the popular contents are cached cooperatively. To address these issues, we propose a cooperative edge caching scheme based on elastic federated and multi-agent deep reinforcement learning (CEFMR) to optimize the cost in the network. We first propose an elastic FL algorithm to train the personalized model for each UE, where adversarial autoencoder (AAE) model is adopted for training to improve the prediction accuracy, then {a popular} content prediction algorithm is proposed to predict the popular contents for each SBS based on the trained AAE model. Finally, we propose a multi-agent deep reinforcement learning (MADRL) based algorithm to decide where the predicted popular contents are collaboratively cached among SBSs. Our experimental results demonstrate the superiority of our proposed scheme to existing baseline caching schemes.

The utilization of existing terrestrial infrastructures to provide coverage for aerial users is a potentially low-cost solution. However, the already deployed terrestrial base stations (TBSs) result in weak ground-to-air (G2A) coverage due to the down-tilted antennas. Furthermore, achieving optimal coverage across the entire airspace through antenna adjustment is challenging due to the complex signal coverage requirements in three-dimensional space, especially in the vertical direction. In this paper, we propose a cooperative tri-point (CoTP) model-based method that utilizes cooperative beams to enhance the G2A coverage extension. To utilize existing TBSs for establishing effective cooperation, we prove that the cooperation among three TBSs can ensure G2A coverage with a minimum coverage overlap, and design the CoTP model to analyze the G2A coverage extension. Using the model, a cooperative coverage structure based on Delaunay triangulation is designed to divide triangular prism-shaped subspaces and corresponding TBS cooperation sets. To enable TBSs in the cooperation set to cover different height subspaces while maintaining ground coverage, we design a cooperative beam generation algorithm to maximize the coverage in the triangular prism-shaped airspace. The simulation results and field trials demonstrate that the proposed method can efficiently enhance the G2A coverage extension while guaranteeing ground coverage.

Samsung unveiled its Galaxy S24 devices at its first Unpacked of the year. As expected, the three smartphones have a heavy focus on artificial intelligence-powered features, from the likes of live translations to image editing. Galaxy AI, as Samsung is calling the devices' overarching AI system, is behind a number of communication-focused functions. For one thing, Galaxy S24 devices will natively support live, two-way translations on phone calls without the need for a third-party app, Samsung says. Since processing for most AI features is handled on-device with the help of the Snapdragon 8 Gen 3 Chipset and its neural processing unit, the conversations will stay private (well, aside from eavesdroppers who might catch one half of the chat).

Samsung's biannual Galaxy Unpacked event is typically big on flashy new mobile hardware, but at this year's event--held today in San Jose, California--it's the software that takes the limelight. Powering the new Samsung Galaxy S24, S24, and S24 Ultra is Galaxy AI, the catchall term for many of the new smart features debuting in the handsets. Many of these functions (but not all) are powered by Google's Gemini artificial intelligence model, and some of them already exist on Google's own Pixel smartphones. Google has long dominated Search simply by being the default option everywhere--now it's employing a similar strategy in leveraging Android to bring its AI prowess to a wider stage. If you buy something using links in our stories, we may earn a commission.

The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses, which create a unified ecosystem that blends physical and virtual spaces, transforming drone interaction and virtual exploration. UAV Twins (UTs), as the digital twins of UAVs that revolutionize UAV applications by making them more immersive, realistic, and informative, are deployed and updated on ground base stations, e.g., RoadSide Units (RSUs), to offer metaverse services for UAV Metaverse Users (UMUs). Due to the dynamic mobility of UAVs and limited communication coverages of RSUs, it is essential to perform real-time UT migration to ensure seamless immersive experiences for UMUs. However, selecting appropriate RSUs and optimizing the required bandwidth is challenging for achieving reliable and efficient UT migration. To address the challenges, we propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses. Specifically, we formulate a multi-leader multi-follower Stackelberg model considering a new immersion metric of UMUs in the utilities of UAVs. Then, we design a Tiny Multi-Agent Deep Reinforcement Learning (Tiny MADRL) algorithm to obtain the tiny networks representing the optimal game solution. Specifically, the actor-critic network leverages the pruning techniques to reduce the number of network parameters and achieve model size and computation reduction, allowing for efficient implementation of Tiny MADRL. Numerical results demonstrate that our proposed schemes have better performance than traditional schemes.

Since PONs are primarily used for fiber-to-the-home (FTTH), the end-user transceivers must be cheap and power efficient while covering the increasing demand of data rates. For this reasons, they typically rely on intensity-modulation and direct-detection (IM/DD) of the optical signal. Current research is focusing on data rates beyond recent 50G-PON standardization efforts [1], i.e., towards PONs which are capable of delivering 100 Gbit/s [2]. Since cost-effective hardware hinders increasing the symbol rate, the focus shifts towards higherorder modulation formats such as 4-ary pulse amplitude modulation (PAM4). However, compared to conventional on-off-keying (OOK), which is used until 50G-PON, multi-level modulation formats are more prone to nonlinerities and, due to its reduced signal-to-noise ratio (SNR) tolerance, require optical amplification. The utilized low-cost semiconductor optical amplifiers (SOA) distort the signal at high received optical power (ROP) due to nonlinear gain saturation, which reduces the dynamic range [3].

The Open Radio Access Network (O-RAN) technology has emerged as a promising solution for network operators, providing them with an open and favorable environment. Ensuring effective coordination of x-applications (xAPPs) is crucial to enhance flexibility and optimize network performance within the O-RAN. In this paper, we introduce an innovative approach to the resource allocation problem, aiming to coordinate multiple independent xAPPs for network slicing and resource allocation in O-RAN. Our proposed method focuses on maximizing the weighted throughput among user equipments (UE), as well as allocating physical resource blocks (PRBs). We prioritize two service types, namely enhanced Mobile Broadband and Ultra Reliable Low Latency Communication. To achieve this, we have designed two xAPPs: a power control xAPP for each UE and a PRB allocation xAPP. The proposed method consists of a two-part training phase, where the first part uses supervised learning with a Variational Autoencoder trained to regress the power transmission as well as the user association and PRB allocation decisions, and the second part uses unsupervised learning with a contrastive loss approach to improve the generalization and robustness of the model. We evaluate the performance of our proposed method by comparing its results to those obtained from an exhaustive search algorithm, deep Q-network algorithm, and by reporting performance metrics for the regression task. We also evaluate the proposed model's performance in different scenarios among the service types. The results show that the proposed method is a more efficient and effective solution for network slicing problems compared to state-of-the-art methods.