This paper introduces a media service model that exploits artificial intelligence (AI) video generators at the receive end. This proposal deviates from the traditional multimedia ecosystem, completely relying on in-house production, by shifting part of the content creation onto the receiver. We bring a semantic process into the framework, allowing the distribution network to provide service elements that prompt the content generator, rather than distributing encoded data of fully finished programs. The service elements include fine-tailored text descriptions, lightweight image data of some objects, or application programming interfaces, comprehensively referred to as semantic sources, and the user terminal translates the received semantic data into video frames. Empowered by the random nature of generative AI, the users could then experience super-personalized services accordingly. The proposed idea incorporates the situations in which the user receives different service providers' element packages; a sequence of packages over time, or multiple packages at the same time. Given promised in-context coherence and content integrity, the combinatory dynamics will amplify the service diversity, allowing the users to always chance upon new experiences. This work particularly aims at short-form videos and advertisements, which the users would easily feel fatigued by seeing the same frame sequence every time. In those use cases, the content provider's role will be recast as scripting semantic sources, transformed from a thorough producer. Overall, this work explores a new form of media ecosystem facilitated by receiver-embedded generative models, featuring both random content dynamics and enhanced delivery efficiency simultaneously.

In this work, we conduct a comparative study on two deep unfolding mechanisms to efficiently perform power control in the next generation wireless networks. The power control problem is formulated as energy efficiency over multiple interference links. The problem is nonconvex. We employ fractional programming transformation to design two solutions for the problem. The first solution is a numerical solution while the second solution is a closed-form solution. Based on the first solution, we design a semi-unfolding deep learning model where we combine the domain knowledge of the wireless communications and the recent advances in the data-driven deep learning. Moreover, on the highlights of the closed-form solution, fully deep unfolded deep learning model is designed in which we fully leveraged the expressive closed-form power control solution and deep learning advances. In the simulation results, we compare the performance of the proposed deep learning models and the iterative solutions in terms of accuracy and inference speed to show their suitability for the real-time application in next generation networks.

Deep learning offers a promising solution to improve spectrum access techniques by utilizing data-driven approaches to manage and share limited spectrum resources for emerging applications. For several of these applications, the sensitive wireless data (such as spectrograms) are stored in a shared database or multistakeholder cloud environment and are therefore prone to privacy leaks. This paper aims to address such privacy concerns by examining the representative case study of shared database scenarios in 5G Open Radio Access Network (O-RAN) networks where we have a shared database within the near-real-time (near-RT) RAN intelligent controller. We focus on securing the data that can be used by machine learning (ML) models for spectrum sharing and interference mitigation applications without compromising the model and network performances. The underlying idea is to leverage a (i) Shuffling-based learnable encryption technique to encrypt the data, following which, (ii) employ a custom Vision transformer (ViT) as the trained ML model that is capable of performing accurate inferences on such encrypted data. The paper offers a thorough analysis and comparisons with analogous convolutional neural networks (CNN) as well as deeper architectures (such as ResNet-50) as baselines. Our experiments showcase that the proposed approach significantly outperforms the baseline CNN with an improvement of 24.5% and 23.9% for the percent accuracy and F1-Score respectively when operated on encrypted data. Though deeper ResNet-50 architecture is obtained as a slightly more accurate model, with an increase of 4.4%, the proposed approach boasts a reduction of parameters by 99.32%, and thus, offers a much-improved prediction time by nearly 60%.

In January, the Spanish startup TravelPerk closed a funding round suited to today's austere times. It raised less than it did two years prior, landing on only a slightly higher valuation of 1.4 billion. The surprising part was that TravelPerk's lead backer was SoftBank, a Japanese investor whose Vision Fund was famous for giving startups outrageous price tags until it racked up immense losses in an investing spree under founder Masayoshi Son. A year later, the Vision Fund is back writing checks, but it's steering clear of the high-flying startups it was once known for championing, like WeWork and failed pizza delivery enterprise Zume. As other tech investors have heaped money into new artificial intelligence firms, Vision Fund has stayed out of the fray.

We present MLTCP, a technique to augment today's congestion control algorithms to accelerate DNN training jobs in shared GPU clusters. MLTCP enables the communication phases of jobs that compete for network bandwidth to interleave with each other, thereby utilizing the network efficiently. At the heart of MLTCP lies a very simple principle based on a key conceptual insight: DNN training flows should scale their congestion window size based on the number of bytes sent at each training iteration. We show that integrating this principle into today's congestion control protocols is straightforward: by adding 30-60 lines of code to Reno, CUBIC, or DCQCN, MLTCP stabilizes flows of different jobs into an interleaved state within a few training iterations, regardless of the number of competing flows or the start time of each flow. Our experiments with popular DNN training jobs demonstrate that enabling MLTCP accelerates the average and 99th percentile training iteration time by up to 2x and 4x, respectively.

SoftBank Group shares surged for a third day on Tuesday, following the explosive rally of its Arm Holdings, the chip designer that has almost doubled in value since making the case last week for how it will benefit from the artificial intelligence (AI) boom. SoftBank's stock climbed as much as 11% to the highest level since May 2021. SoftBank held onto a stake of about 90% in Arm as it took the company public last year. Arm's shares rose 29% on Monday, pushing its gains to more than 90% since it reported financial results on Feb. 7. The company is expanding beyond its traditional base in smartphone technology into new markets like artificial intelligence applications, lifting its outlook.

The fifth generation (5G) cellular network technology is mature and increasingly utilized in many industrial and robotics applications, while an important functionality is the advanced Quality of Service (QoS) features. Despite the prevalence of 5G QoS discussions in the related literature, there is a notable absence of real-life implementations and studies concerning their application in time-critical robotics scenarios. This article considers the operation of time-critical applications for 5G-enabled unmanned aerial vehicles (UAVs) and how their operation can be improved by the possibility to dynamically switch between QoS data flows with different priorities. As such, we introduce a robotics oriented analysis on the impact of the 5G QoS functionality on the performance of 5G-enabled UAVs. Furthermore, we introduce a novel framework for the dynamic selection of distinct 5G QoS data flows that is autonomously managed by the 5G-enabled UAV. This problem is addressed in a novel feedback loop fashion utilizing a probabilistic finite state machine (PFSM). Finally, the efficacy of the proposed scheme is experimentally validated with a 5G-enabled UAV in a real-world 5G stand-alone (SA) network.

The power requirements posed by the fifth-generation and beyond cellular networks are an important constraint in network deployment and require energy-efficient solutions. In this work, we propose a novel user load transfer approach using airborne base stations (BS) mounted on drones for reliable and secure power redistribution across the micro-grid network comprising green small cell BSs. Depending on the user density and the availability of an aerial BS, the energy requirement of a cell with an energy deficit is accommodated by migrating the aerial BS from a high-energy to a low-energy cell. The proposed hybrid drone-based framework integrates long short-term memory with unique cost functions using an evolutionary neural network for drones and BSs and efficiently manages energy and load redistribution. The proposed algorithm reduces power outages at BSs and maintains consistent throughput stability, thereby demonstrating its capability to boost the reliability and robustness of wireless communication systems.

FOX News' Eben Brown reports that with the use of AI, scammers are fleecing Americans in more sophisticated ways. The Federal Communications Commission on Thursday made AI-generated robocalls mimicking the voices of political candidates to fool voters illegal. With the unanimous adoption of a declaratory ruling that recognizes calls made with AI-generated voices are "artificial" under the Telephone Consumer Protection Act (TCPA), a 1991 law restricting junk calls that use artificial and prerecorded voice messages, the FCC said it was giving state attorneys general new tools to go after those responsible for voice cloning scams. The decision was announced days after New Hampshire Attorney General John Formella revealed earlier this week that nefarious robocalls with an AI-generated clone of President Biden's voice urging recipients not to participate in the Jan. 23 primaries – and instead save their votes for the November election – had been traced to two Texas companies. Formella vowed potential civil and criminal action at the state and federal level.

Scam and spam robocalls featuring lifelike AI-generated human voices are now officially illegal, in a unanimous ruling by the Federal Communications Commission. The new ruling, issued Thursday, promised to give'State Attorneys General across the country new tools to go after bad actors behind these nefarious robocalls.' 'Bad actors are using AI-generated voices in unsolicited robocalls to extort vulnerable family members, imitate celebrities, and misinform voters,' FCC Chairwoman Jessica Rosenworcel said in a press release. Following the new ruling, FCC Chairwoman Jessica Rosenworcel (above) said, 'We're putting the fraudsters behind these robocalls on notice.' 'State Attorneys General will now have new tools to crack down on these scams and ensure the public is protected from fraud and misinformation,' Rosenworcel said. The FCC ruling will expand what activities prosecutors can pursue under the Telephone Consumer Protection Act (TCPA), which is currently the primary law allowing the authorities to help limit junk calls.