It was a messy Monday if you were a Verizon customer or wanted some PS5 gaming in the evening. First, Verizon mobile customers reported outages across the US on Monday. Reports spiked at almost 105,000 at 11:20AM. Issues included the inability to send texts and a lack of cellular service outright. The issue centered on the East Coast and Midwest. The carrier hasn't elaborated on what caused the issue.

Cellular Radio Access Networks (RANs) are rapidly evolving towards 6G, driven by the need to reduce costs and introduce new revenue streams for operators and enterprises. In this context, AI emerges as a key enabler in solving complex RAN problems spanning both the management and application domains. Unfortunately, and despite the undeniable promise of AI, several practical challenges still remain, hindering the widespread adoption of AI applications in the RAN space. This article attempts to shed light to these challenges and argues that existing approaches in addressing them are inadequate for realizing the vision of a truly AI-native 6G network. Motivated by this lack of solutions, it proposes a generic distributed AI platform architecture, tailored to the needs of an AI-native RAN and discusses its alignment with ongoing standardization efforts.

As first reported by Reuters, ByteDance, the Chinese parent company of TikTok, is planning to train and develop an AI model using chips from fellow Chinese company Huawei. Three anonymous sources approached Reuters with this information; a fourth source couldn't confirm that ByteDance was using Huawei chips but did say that a new AI model was in development. Previously, ByteDance's AI projects used NVIDIA's H20 AI chips, which were designed for the Chinese market and avoided the trade restrictions the US government placed in 2022. Chinese customers were only allowed to purchase select models of AI chips, which was an attempt to slow down Chinese technological advancement. ByteDance has ordered 100,000 Ascend 910B chips from Huawei this year but only received 30,000 of them.

The rapid growth in mobile broadband usage and increasing subscribers have made it crucial to ensure reliable network performance. As mobile networks grow more complex, especially during peak hours, manual collection of Key Performance Indicators (KPIs) is time-consuming due to the vast data involved. Detecting network failures and identifying unusual behavior during busy periods is vital to assess network health. Researchers have applied Deep Learning (DL) and Machine Learning (ML) techniques to understand network behavior by predicting throughput, analyzing call records, and detecting outages. However, these methods often require significant computational power, large labeled datasets, and are typically specialized, making retraining for new scenarios costly and time-intensive. This study introduces a novel approach Multi-Scale Convolutional LSTM with Transfer Learning (TL) to detect anomalies in cellular networks. The model is initially trained from scratch using a publicly available dataset to learn typical network behavior. Transfer Learning is then employed to fine-tune the model by applying learned weights to different datasets. We compare the performance of the model trained from scratch with that of the fine-tuned model using TL. To address class imbalance and gain deeper insights, Exploratory Data Analysis (EDA) and the Synthetic Minority Over-sampling Technique (SMOTE) are applied. Results demonstrate that the model trained from scratch achieves 99% accuracy after 100 epochs, while the fine-tuned model reaches 95% accuracy on a different dataset after just 20 epochs.

As wireless networks grow to support more complex applications, the Open Radio Access Network (O-RAN) architecture, with its smart RAN Intelligent Controller (RIC) modules, becomes a crucial solution for real-time network data collection, analysis, and dynamic management of network resources including radio resource blocks and downlink power allocation. Utilizing artificial intelligence (AI) and machine learning (ML), O-RAN addresses the variable demands of modern networks with unprecedented efficiency and adaptability. Despite progress in using ML-based strategies for network optimization, challenges remain, particularly in the dynamic allocation of resources in unpredictable environments. This paper proposes a novel Meta Deep Reinforcement Learning (Meta-DRL) strategy, inspired by Model-Agnostic Meta-Learning (MAML), to advance resource block and downlink power allocation in O-RAN. Our approach leverages O-RAN's disaggregated architecture with virtual distributed units (DUs) and meta-DRL strategies, enabling adaptive and localized decision-making that significantly enhances network efficiency. By integrating meta-learning, our system quickly adapts to new network conditions, optimizing resource allocation in real-time. This results in a 19.8% improvement in network management performance over traditional methods, advancing the capabilities of next-generation wireless networks.

Next-generation multiple access (NGMA) serves as an umbrella term for transmission schemes distinct from conventional orthogonal methods. A key candidate of NGMA, non-orthogonal multiple access (NOMA), emerges as a solution to enhance connectivity by allowing multiple users to share time, frequency, and space concurrently. However, NOMA faces challenges in implementation, particularly in canceling inter-user interference. In this paper, we discuss the principles behind NOMA and review conventional NOMA methods. Then, to address these challenges, we present asynchronous transmission and interference-aware modulation techniques, enabling decoding without successive interference cancellation. The goal is to design constellations that dynamically adapt to interference, minimizing bit error rates (BERs) and enhancing user throughput in the presence of inter-user, inter-carrier, and inter-cell interference. The traditional link between minimizing BER and increasing spectral efficiency is explored, with deep autoencoders for end-to-end communication emerging as a potential solution to improve BERs. Interference-aware modulation can revolutionize constellation design for non-orthogonal channels. Rate-splitting multiple access (RSMA) is another promising interference management technique in multi-user systems. In addition to addressing challenges in finite-alphabet NOMA, this paper offers new insights and provides an overview of code-domain NOMA, trellis-coded NOMA, and RSMA as key NGMA candidates. We also discuss the evolution of channel coding toward low-latency communication and examine modulation and coding schemes in 5G networks. Finally, we highlight future research directions, emphasizing their importance for realizing NOMA from concept to functional technology.

Federated Learning (FL) provides a privacy-preserving framework for training machine learning models on mobile edge devices. Traditional FL algorithms, e.g., FedAvg, impose a heavy communication workload on these devices. To mitigate this issue, Hierarchical Federated Edge Learning (HFEL) has been proposed, leveraging edge servers as intermediaries for model aggregation. Despite its effectiveness, HFEL encounters challenges such as a slow convergence rate and high resource consumption, particularly in the presence of system and data heterogeneity. However, existing works are mainly focused on improving training efficiency for traditional FL, leaving the efficiency of HFEL largely unexplored. In this paper, we consider a two-tier HFEL system, where edge devices are connected to edge servers and edge servers are interconnected through peer-to-peer (P2P) edge backhauls. Our goal is to enhance the training efficiency of the HFEL system through strategic resource allocation and topology design. Specifically, we formulate an optimization problem to minimize the total training latency by allocating the computation and communication resources, as well as adjusting the P2P connections. To ensure convergence under dynamic topologies, we analyze the convergence error bound and introduce a model consensus constraint into the optimization problem. The proposed problem is then decomposed into several subproblems, enabling us to alternatively solve it online. Our method facilitates the efficient implementation of large-scale FL at edge networks under data and system heterogeneity. Comprehensive experiment evaluation on benchmark datasets validates the effectiveness of the proposed method, demonstrating significant reductions in training latency while maintaining the model accuracy compared to various baselines.

However, the use of LLMs for orchestrating We assign one chat group to the OTN domain and another networks across multiple domains has not yet been to the robotic domain, each containing multiple intelligent demonstrated. Cross-domain network orchestration is essential agents with specific roles: 1) a manager automatically selects for delivering dynamic, scalable, and high-performance the appropriate agent based on the task request, each agent's services in today's vertical networks[3].

In this paper, a novel generative adversarial imitation learning (GAIL)-powered policy learning approach is proposed for optimizing beamforming, spectrum allocation, and remote user equipment (RUE) association in NTNs. Traditional reinforcement learning (RL) methods for wireless network optimization often rely on manually designed reward functions, which can require extensive parameter tuning. To overcome these limitations, we employ inverse RL (IRL), specifically leveraging the GAIL framework, to automatically learn reward functions without manual design. We augment this framework with an asynchronous federated learning approach, enabling decentralized multi-satellite systems to collaboratively derive optimal policies. The proposed method aims to maximize spectrum efficiency (SE) while meeting minimum information rate requirements for RUEs. To address the non-convex, NP-hard nature of this problem, we combine the many-to-one matching theory with a multi-agent asynchronous federated IRL (MA-AFIRL) framework. This allows agents to learn through asynchronous environmental interactions, improving training efficiency and scalability. The expert policy is generated using the Whale optimization algorithm (WOA), providing data to train the automatic reward function within GAIL. Simulation results show that the proposed MA-AFIRL method outperforms traditional RL approaches, achieving a $14.6\%$ improvement in convergence and reward value. The novel GAIL-driven policy learning establishes a novel benchmark for 6G NTN optimization.

Optical fiber amplifiers are key elements in present optical networks. Failures of these components result in high financial loss of income of the network operator as the communication traffic over an affected link is interrupted. Applying Remaining useful lifetime (RUL) prediction in the context of Predictive Maintenance (PdM) to optical fiber amplifiers to predict upcoming system failures at an early stage, so that network outages can be minimized through planning of targeted maintenance actions, ensures reliability and safety. Optical fiber amplifier are complex systems, that work under various operating conditions, which makes correct forecasting a difficult task. Increased monitoring capabilities of systems results in datasets that facilitate the application of data-driven RUL prediction methods. Deep learning models in particular have shown good performance, but generalization based on comparatively small datasets for RUL prediction is difficult. In this paper, we propose Sparse Low-ranked self-Attention Transformer (SLAT) as a novel RUL prediction method. SLAT is based on an encoder-decoder architecture, wherein two parallel working encoders extract features for sensors and time steps. By utilizing the self-attention mechanism, long-term dependencies can be learned from long sequences. The implementation of sparsity in the attention matrix and a low-rank parametrization reduce overfitting and increase generalization. Experimental application to optical fiber amplifiers exemplified on EDFA, as well as a reference dataset from turbofan engines, shows that SLAT outperforms the state-of-the-art methods.