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 transition from fifth-generation (5G) to sixth-generation (6G) communication networks represents a fundamental shift from conventional model-driven architectures toward AI-native, intelligence-driven network ecosystems capable of autonomous control, optimization, and decision-making. Although artificial intelligence (AI) has demonstrated significant potential in enhancing network performance, existing research remains fragmented, with most studies focusing on isolated network functions rather than integrated system-level intelligence. This study presents a systematic literature review (SLR) combined with a critical synthesis to examine the current state of AI-driven control and optimization in 6G communication networks. The review systematically analyzes 87 peer-reviewed studies published between 2020 and 2024, retrieved from IEEE Xplore, ScienceDirect, SpringerLink, and Wiley Online Library using predefined inclusion and exclusion criteria. The selected studies are critically evaluated with respect to machine learning, deep learning, and reinforcement learning techniques, emphasizing their architectural roles, operational capabilities, deployment feasibility, and system-level implications. 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. Furthermore, the review identifies a considerable gap between algorithmic advances and practical implementation, highlighting the need for integrated AI frameworks and architecture-aware design strategies capable of supporting scalable, trustworthy, and autonomous 6G communication systems. By providing a comprehensive synthesis of recent research, comparative analysis of major AI paradigms, and future research directions, this review contributes to bridging the gap between theoretical developments and practical deployment of AI-native communication networks.

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

The evolution toward sixth-generation (6G) networks is transforming the radio access network (RAN) into a programmable and intelligent control platform that must continuously adapt to heterogeneous services, dynamic environments, and competing performance objectives. Open Radio Access Network (O-RAN) provides the open interfaces, disaggregated architecture, and multi-timescale control loops needed to support this transformation, while deep reinforcement learning (DRL) offers a natural framework for optimizing sequential decisions under uncertainty. However, existing surveys either address artificial intelligence (AI) and machine learning (ML) in O-RAN broadly or focus on isolated DRL use cases, leaving a gap in the systematic connection between DRL methodology, O-RAN architecture, and operational deployment. To the best of our knowledge, this article presents the first dedicated and comprehensive survey of DRL for Open AI-RAN. We review the foundations of model-free, model-based, offline, safe, multi-agent, federated, and transfer learning, and provide an O-RAN-aware framework for formulating RAN control problems through states, observations, actions, rewards, constraints, and temporal structure. We classify DRL applications across radio resource management, mobility management, interference control, traffic steering, energy efficiency, network slicing, integrated sensing and communication, security, and massive MIMO. We further examine multi-agent and federated coordination, foundation models and agentic AI, trustworthy DRL, sim-to-real transfer, continual adaptation, resource-efficient inference, and reinforcement learning operations. Finally, we review experimental platforms, benchmarks, standards, and industry activities, and identify research directions toward sample-efficient, safe, scalable, interoperable, and deployable DRL control for 6G Open AI-RAN.

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

For 6G wireless networks, efficient resource allocation is a significant problem, especially with the growing need for ultra-low latency, high-speed communication, and efficient energy consumption. The traditional approach is found to be inadequate to meet the dynamic changes and service-allocation requirements. The application of AI and DL is seen as an efficient approach to making intelligent, timely decisions in complex scenarios. This paper proposes an integrated AI- and DL-based approach for efficient, intelligent resource allocation in 6G wireless communication. The difficulties encountered in dynamic spectrum allocation, energy depletion, and attenuation are addressed through an integrated approach that combines optimal path selection with efficient allocation mechanisms. The input parameters considered are residual battery indicator (RBI), channel matrix (H), normalized spectrum availability (v), SINR values, node pairs (s, d), service levels, and historical statistics. To ensure data quality, a Recursive Hampel Filter-Based Estimation Model (ReHF-EM) has been employed. Furthermore, for fundamental decision-making, 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. Additionally, the parameters of the proposed model have been fine-tuned using the Pied Kingfisher Optimizer (PKfO) for better efficiency, thus reducing complexities associated with the model. The proposed model has been implemented using Python, and various performance parameters such as Spectrum Efficiency (SE), Energy Efficiency (EE), SINR margin, Bit Error Rate (BER), Computational Time (CT), and Accuracy have been considered to evaluate the proposed model. The results show a 23.6% increase in Energy Efficiency and a 19.2% reduction in Bit Error Rate.

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.