For massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) feedback is critical for exploiting the performance potentials. Recognized by the 3rd Generation Partnership Project as a key use case for the artificial intelligence-native air interface, deep learning (DL)-based CSI feedback has attracted extensive research from academia and industry. However, one of the most significant obstacles to its practical deployment is the limited generalization capabilities of DL models. In this article, we first illustrate the generalization challenge faced by DL-based CSI feedback with the main factors. Then, we systematically analyze its feasible solutions from two perspectives: initial model acquisition and efficient online updating. Finally, to address knowledge-forgetting problems in online updating, a novel alternating optimization framework is proposed to further improve the generalization ability through knowledge review. Hopefully, this article can give inspiring insights for the optimization of deployment and generalization for DL-based CSI feedback systems.

Identity is who we are, the way we think about ourselves, the way we are viewed by the world, and the characteristics that define us. Tradition is the handing down of the information, beliefs, and customs that define our identity from one generation to another. History, the systematic study of how our identity and traditions have evolved through events and evidence from the past, helps us satisfy the very human need to understand where we came from and why things are the way they are today. Historiography is concerned with how history is studied and written.

Deep Reinforcement Learning (DRL) has emerged as a powerful solution for meeting the growing demands for connectivity, reliability, low latency and operational efficiency in advanced networks. However, most research has focused on theoretical analysis and simulations, with limited investigation into real-world deployment. To bridge the gap and support practical DRL deployment for network management, we first present an orchestration framework that integrates ETSI Multi-access Edge Computing (MEC) with Open RAN, enabling seamless adoption of DRL-based strategies across different time scales while enhancing agent lifecycle management. We then identify three critical challenges hindering DRL's real-world deployment, including asynchronous requests from unpredictable or bursty traffic, adaptability and generalization across heterogeneous topologies and evolving service demands, and prolonged convergence and service interruptions due to exploration in live operational environments. To address these challenges, we propose a three-fold solution strategy: advanced time-series integration for handling asynchronized traffic, flexible architecture design such as multi-agent DRL and incremental learning to support heterogeneous scenarios, and simulation-driven deployment with transfer learning to reduce convergence time and service disruptions. Lastly, the feasibility of the MEC-O-RAN architecture is validated on an urban-wide testing infrastructure, and two real-world use cases are presented, showcasing the three identified challenges and demonstrating the effectiveness of the proposed solutions.

In this article, we present a digital agent (DA)-assisted network management framework for future sixth-generation (6G) networks considering user quality of experience (QoE). A novel QoE metric is defined by incorporating the impact of user behavioral dynamics and environmental complexity on quality of service (QoS). A two-level DA architecture is proposed to assist the QoE-driven network slicing and orchestration. Three potential solutions are presented from the perspectives of DA data collection, resource scheduling, and DA deployment. A case study demonstrates that the proposed framework can effectively improve user QoE compared with benchmark schemes.

Polarforming emerges as a promising technique for manipulating the polarization of electromagnetic (EM) waves by shaping the polarization of an antenna into a desired state. By dynamically adjusting antenna polarization, polarforming enables real-time polarization matching or mismatching with received EM waves, thereby leveraging polarization degrees of freedom (DoFs) to enhance wireless communication performance. In this article, we first present an overview of the fundamental principles and design approaches underlying the polarforming technique. Then, we analyze the key advantages of polarforming, including hardware cost reduction, depolarization mitigation, channel adaptation, and signal quality improvement. Furthermore, we explore promising applications of polarforming for next-generation wireless networks. Finally, we discuss the implementation challenges and their potential solutions, motivating future research.

Emerging applications such as networked robotics, intelligent transportation, smart factories, and virtual and augmented reality demand integrated perception and connectivity enabled by wireless communication. This has driven growing interests in integrated sensing, communication, and computation (ISCC) systems, with a primary focus on their efficient co-designs. However, as ISCC systems increasingly support critical applications, they must not only deliver high performance but also demonstrate robustness and resilience. In this context, robustness refers to a system's ability to maintain performance under uncertainties, while resilience denotes its capacity to sustain a minimum level of service in the face of major disruptions. To address this gap, this article presents an overview of ISCC systems from the perspectives of robustness and resilience under limited resources. First, key concepts related to these properties are introduced in the ISCC context. Subsequently, design approaches for realizing robust and resilient ISCC networks are discussed. Finally, the article concludes with the discussions of a case study and open research problems in this area.

The evolution toward Level 4 (L4) Autonomous Networks (AN) represents a strategic inflection point in telecommunications, where networks must transcend reactive automation to achieve genuine cognitive capabilities - fulfilling AN's vision of self-configuring, self-healing, and self-optimizing systems that deliver zero-wait, zero-touch, and zero-fault services. This work bridges the gap between architectural theory and operational reality by implementing Joseph Sifakis's AN Agent reference architecture in a functional cognitive system, deploying coordinated proactive-reactive runtimes driven by hybrid knowledge representation. Through an empirical case study of a Radio Access Network (RAN) Link Adaptation (LA) Agent, we validate this framework's performance. Specifically, the system demonstrates sub-10 ms real-time control in 5G NR sub-6 GHz environments. Empirical results show a 4% increase in downlink throughput over Outer Loop Link Adaptation (OLLA) algorithms for enhanced mobile broadband (eMBB). Furthermore, for the ultra-reliable low-latency communication (URLLC) scenario, the agent achieves an 85% reduction in Block Error Rate (BLER). These improvements confirm the architecture's viability in overcoming traditional autonomy barriers and advancing critical L4-enabling capabilities toward next-generation objectives.