Large AI Models Empowered Edge Intelligence for Next-Gen Consumer Electronics

Towards Intelligent 6G Networks: A Comprehensive Review of AI-Driven Control and Optimization

The findings indicate that while AI techniques substantially improve network adaptability, resource management, and autonomous operation, significant challenges remain regarding scalability, computational complexity, data dependency, interoperability, explainability, and deployment in real-world environments.

Self-organizing Architecture of Receptron Units: a Hardware-Aware Framework for Edge Intelligence

The growing demand for intelligent processing at the edge of IoT networks is constrained by the severe computational and memory limitations of microcontroller units, which render impractical conventional deep learning approaches. We propose a neuromorphicinspired classifier based on the Receptron model, a single-unit architecture capable of implementing non-linearly separable decision boundaries, without resorting to multi-layer networks. The model is designed for direct deployment on mid-range MCUs, while supporting continuous on-device adaptation. Experimental evaluation on basic dataset benchmarks yields cross-validated accuracies compatible with standard machine learning method baselines. These results position the Receptron as a viable and interpretable alternative for resource-constrained neuromorphic edge systems operating in dynamic, non-stationary environments.

6G communications: empowering the future with advanced AI and ML techniques

Deep Reinforcement Learning for 6G AI-RAN: A Comprehensive Survey

This article presents the first dedicated and comprehensive survey of DRL for Open AI-RAN, and reviews the foundations of model-free, model-based, offline, safe, multi-agent, federated, and transfer learning, and provides an O-RAN-aware framework for formulating RAN control problems through states, observations, actions, rewards, constraints, and temporal structure.

A unified machine learning framework for intelligent resource allocation toward 6G wireless communications.

A Dual-Stage Multi-Time-Scale Temporal Attention-Based LSTM network (D-MTSTA-LSTM) has been architected, which effectively learns short- and long-term relationships in network trends, thereby precisely predicting optimal communication routes and associated power and spectrum allocation.

A Generative Artificial Intelligence–based ANFIS Approach for Low-Latency Video Communication in Cloud Environments

Generative AI (GAI) refers to advanced models that can generate new, realistic, context-aware data or solutions by learning from existing datasets, making them extremely valuable in adaptive intelligent systems. Traditional AI technologies often face limitations, including a lack of interpretability, sensitivity to noisy data, and an inability to generalize to dynamic environments. These limitations can be effectively addressed by GAI-driven adaptive neuro-fuzzy inference systems (ANFIS). To facilitate the ease of scalable processing and real-time deployment, the proposed framework is developed in a cloud computing system, whereby the aggregation of large-scale QoE data, the training of generative models and the optimization of the fuzzy rules are handled using the cloud computing resources, and latencysensitive inference is done effectively at the edge. To address these challenges, we propose a Generative Reinforcement Learning (GRL) method that enhances decision-making skills by generating artificial experiences, utilizing the video transmission system as an environment, and learns policies to optimize latency. The Generative Diffusion Model (GDM) for feature denoising offers a powerful mechanism for removing noise in high- dimensional data, thereby enhancing the accuracy and stability of prediction tasks. Physics-Guided Generative Modeling (PG2M) combines domain-specific physics laws with AI learning to ensure scientifically consistent and interpretable outputs. Finally, Generative Adversarial Networks (GANs) are employed for generative rule evolution, combining evolutionary computation with adversarial learning to dynamically generate and improve classification rules. ANFIS utilizes fuzzy rules to model delay shapes, while GRL optimizes video transmission strategies based on the output of ANFIS. Reduce latency dynamically by learning adaptive frame scheduling; it improves network responsiveness to fluctuations. The presented method achieved 94% accuracy and improved the latency of the video communication service.