For SoftBank Group investors looking for the stock to climb back to all-time highs on a revival of the artificial intelligence boom, DeepSeek poses a major hurdle. SoftBank is steering a 500 billion fundraising for the Stargate Project to develop AI infrastructure in the U.S., a plan that is key to founder Masayoshi Son's drive to establish a leading position in the emerging field. But now DeepSeek's low-cost AI model is begging the question of whether such massive spending is even necessary. DeepSeek may spark a "near-term market correction" for SoftBank and other AI stocks, said Jung In Yun, chief executive officer at Fibonacci Asset Management Global Pte. While the AI rally should pick up again longer term, the focus will be on monetization, he said, adding that "will take years."

For SoftBank investors looking for the stock to climb back to all-time highs on a revival of the artificial intelligence (AI) boom, DeepSeek poses a major hurdle. SoftBank is steering a 500 billion fundraising for the Stargate Project to develop AI infrastructure in the United States, a plan that is key to founder Masayoshi Son's drive to establish a leading position in the emerging field. But now DeepSeek's low-cost AI model is begging the question of whether such massive spending is even necessary. DeepSeek may spark a "near-term market correction" for SoftBank and other AI stocks, said Jung In Yun, chief executive officer at Fibonacci Asset Management. While the AI rally should pick up again longer term, the focus will be on monetization, he said, adding that "will take years."

With the rapid development of various internet of things (IoT) applications, including industrial IoT (IIoT) and visual IoT (VIoT), the demand for direct device-to-device communication to support high data rates continues to grow. To address this demand, 5G-Advanced has introduced sidelink communication over the unlicensed spectrum (SL-U) to increase data rates. However, the primary challenge of SL-U in the unlicensed spectrum is ensuring fair coexistence with other incumbent systems, such as Wi-Fi. In this paper, we address the challenge by designing channel access mechanisms and power control strategies to mitigate interference and ensure fair coexistence. First, we propose a novel collaborative channel access (CCHA) mechanism that integrates channel access with resource allocation through collaborative interactions between base stations (BS) and SL-U users. This mechanism ensures fair coexistence with incumbent systems while improving resource utilization. Second, to further enhance the performance of the coexistence system, we develop a cooperative subgoal-based hierarchical deep reinforcement learning (C-GHDRL) algorithm framework. The framework enables SL-U users to make globally optimal decisions by leveraging cooperative operations between the BS and SL-U users, effectively overcoming the limitations of traditional optimization methods in solving joint optimization problems with nonlinear constraints. Finally, we mathematically model the joint channel access and power control problem and balance the trade-off between fairness and transmission rate in the coexistence system by defining a suitable reward function in the C-GHDRL algorithm. Simulation results demonstrate that the proposed scheme significantly enhances the performance of the coexistence system while ensuring fair coexistence between SL-U and Wi-Fi users.

Mobile network operators constantly optimize their networks to ensure superior service quality and coverage. This optimization is crucial for maintaining an optimal user experience and requires extensive data collection and analysis. One of the primary methods for gathering this data is through drive tests, where technical teams use specialized equipment to collect signal information across various regions. However, drive tests are both costly and time-consuming, and they face challenges such as traffic conditions, environmental factors, and limited access to certain areas. These constraints make it difficult to replicate drive tests under similar conditions. In this study, we propose a method that enables operators to predict received signal strength at specific locations using data from other drive test points. By reducing the need for widespread drive tests, this approach allows operators to save time and resources while still obtaining the necessary data to optimize their networks and mitigate the challenges associated with traditional drive tests.

Operating System (OS) fingerprinting is essential for network management and cybersecurity, enabling accurate device identification based on network traffic analysis. Traditional rule-based tools such as Nmap and p0f face challenges in dynamic environments due to frequent OS updates and obfuscation techniques. While Machine Learning (ML) approaches have been explored, Deep Learning (DL) models, particularly Transformer architectures, remain unexploited in this domain. This study investigates the application of Tabular Transformer architectures-specifically TabTransformer and FT-Transformer-for OS fingerprinting, leveraging structured network data from three publicly available datasets. Our experiments demonstrate that FT-Transformer generally outperforms traditional ML models, previous approaches and TabTransformer across multiple classification levels (OS family, major, and minor versions). The results establish a strong foundation for DL-based OS fingerprinting, improving accuracy and adaptability in complex network environments. Furthermore, we ensure the reproducibility of our research by providing an open-source implementation.

Improving connectivity can both benefit those who most need it most and boost the businesses that provide the service. That's the case telecom industry leaders made during a panel on Feb. 11 at the World Governments Summit in Dubai. Titled "Can we innovate our way to a more connected world?", the panel was hosted by TIME's Editor-in-Chief Sam Jacobs. During the course of the conversation, Margherita Della Valle, CEO of U.K.-based multinational telecom company Vodafone Group, said, "For society today, connectivity is essential. We are moving from the old divide in the world between the haves and the have-nots towards a new divide, which is between those who have access to connectivity and those who don't."

Qualcomm is bringing AI to its mid-range mobile chip lineup with the Snapdragon 6 Gen 4 Mobile Platform, the company announced. The new chips also promise improved CPU and GPU performance, lower power requirements and faster Wi-Fi and mobile connectivity compared to the previous chip. The new AI features are made possible with support for Qualcom's on-device Gen AI support, allowing voice-activated assistants, background noise reduction during calls and more. It's also the first 6-series Snapdragon processor with support for INT4 that allows generative AI to run more efficiently on small devices. Qualcomm is also promising 11 percent improved CPU performance via its latest Kryo CPU and a 29 percent boost in GPU performance.

SoftBank Group swung to a loss in the December quarter due to a drop in the value of its Vision Fund unit's public holdings, boding ill for founder Masayoshi Son who has to raise 500 billion for the Stargate artificial intelligence project. The Tokyo-based company reported a net loss of 369.2 billion ( 2.4 billion) for the fiscal third quarter compared with a profit of 950 billion a year earlier. The Vision Fund unit logged a 309.9 billion loss, hurting the bottom line after shares of public holdings such as Coupang and Didi Global gave up some of their gains from the previous quarter. Volatility in the Vision Fund's quarterly performance consistently dogs SoftBank, which has embarked on a project with OpenAI to invest 500 billion on the infrastructure needed to support and propel AI development. Japanese billionaire Son is exploring project financing to raise money.

Massive multiple-input multiple-output (mMIMO) technology has transformed wireless communication by enhancing spectral efficiency and network capacity. This paper proposes a novel deep learning-based mMIMO precoder to tackle the complexity challenges of existing approaches, such as weighted minimum mean square error (WMMSE), while leveraging meta-learning domain generalization and a teacher-student architecture to improve generalization across diverse communication environments. When deployed to a previously unseen site, the proposed model achieves excellent sum-rate performance while maintaining low computational complexity by avoiding matrix inversions and by using a simpler neural network structure. The model is trained and tested on a custom ray-tracing dataset composed of several base station locations. The experimental results indicate that our method effectively balances computational efficiency with high sum-rate performance while showcasing strong generalization performance in unseen environments. Furthermore, with fine-tuning, the proposed model outperforms WMMSE across all tested sites and SNR conditions while reducing complexity by at least 73$\times$.

Generative Artificial Intelligence (GenAI) models such as LLMs, GPTs, and Diffusion Models have recently gained widespread attention from both the research and the industrial communities. This survey explores their application in network monitoring and management, focusing on prominent use cases, as well as challenges and opportunities. We discuss how network traffic generation and classification, network intrusion detection, networked system log analysis, and network digital assistance can benefit from the use of GenAI models. Additionally, we provide an overview of the available GenAI models, datasets for large-scale training phases, and platforms for the development of such models. Finally, we discuss research directions that potentially mitigate the roadblocks to the adoption of GenAI for network monitoring and management. Our investigation aims to map the current landscape and pave the way for future research in leveraging GenAI for network monitoring and management.