The acquisition of channel state information (CSI) plays a vital role in enhancing the performance of sixth-generation (6G) wireless communication systems. Conventional channel estimation approaches encounter significant scalability limitations in emerging scenarios, such as high-mobility environments, extremely large-scale multiple-input multiple-output (XL-MIMO) configurations, and multi-band operations, where pilot overhead grows dramatically. CSI extrapolation offers an effective solution to these issues by leveraging limited or partial CSI measurements to reconstruct or predict the full CSI, thereby substantially lowering the required overhead without compromising accuracy. Artificial intelligence (AI) has emerged as a powerful tool to advance CSI extrapolation, enabling more accurate and efficient inference across diverse channel conditions. Although research in this area is expanding rapidly, the literature still lacks a thorough and unified survey that synthesizes the latest developments in AI-based CSI extrapolation methods. This paper aims to bride this gap by providing the first comprehensive review of AI-driven CSI extrapolation techniques, covering their current state, key limitations, and promising research avenues. We begin by outlining the foundational aspects of AI-driven CSI extrapolation. This includes essential wireless channel properties that influence extrapolation performance and an overview of the most commonly employed AI architectures suited to this task. Building on these basics, we systematically examine the major categories of extrapolation approaches, both traditional model-based and modern AI-enhanced ones, across the primary domains: time, frequency, antenna, and multi-domain scenarios. For each category, we highlight representative techniques, their underlying principles, strengths, and limitations, along with distilled insights from comparative studies. Recognizing the strong potential of AI-based methods to satisfy the demanding performance targets of future systems, we also review publicly available open channel datasets and channel simulators that support the development and benchmarking of robust AI-driven extrapolation models. Finally, we identify persistent challenges in the field, and outline forward-looking research directions to guide future progress toward practical deployment in 6G networks.

Programmable networks have transformed network management, particularly within Traffic Engineering (TE), which aims to optimize data flow across the network. By offering flexibility and efficiency, programmable networks facilitate advanced traffic and resource management capabilities. Key technologies in this area, such as Software-Defined Networking (SDN) and Programmable Data Planes (PDPs), enable real-time, dynamic routing and network control. These frameworks further allow the integration of Artificial Intelligence (AI) mechanisms to automate and refine network management processes. Specifically, Reinforcement Learning (RL) is a promising approach for TE applications due to its adaptability to evolving network conditions. We provide a survey with an in-depth review of TE solutions that leverage RL and programmable networks to improve network performance, including works from 2018 to 2025. The proposed survey includes a timely update on the state-of-the-art and presents a taxonomy that categorizes existing solutions based on RL principles and specific TE objectives. Our analysis highlights key findings and insights, contributing valuable knowledge for implementing TE mechanisms within programmable networks.

Space-air-ground integrated networks (SAGINs) are emerging as a fundamental architecture for 6G systems to enable massive connectivity, novel applications, extreme data rates, ultra-low latency, and multi-dimensional networking. The complex nature of SAGINs has driven researchers to explore classical machine learning (CML) as a powerful tool for modeling and optimizing these multi-layer networks. Although CML offers many benefits, it will suffer from exponential complexity for SAGIN design, mainly due to high-dimensional data and problem spaces. Therefore, the NP-hard problems are challenging for CML to enable SAGINs efficiently. To address this critical challenge, we present a comprehensive survey examining the integration of quantum ML (QML) with SAGINs. We systematically evaluate quantum-enhanced SAGIN technologies through detailed performance metrics and create a new classification system for hybrid network architectures. We thoroughly examine key integration strategies and analyze major technical challenges, which include how quantum states degrade in space, how to distribute resources optimally, and how to develop security protocols. We show how QML can solve the growing complexity of 6G networks by improving network capacity, intelligence, and security. The research also creates a comprehensive framework for quantum-enhanced SAGINs that researchers and industry professionals can use as a guide when developing next-generation communication systems. Our findings examine the transformative potential of QML in reshaping global communications infrastructure, which presents a structured approach to navigating the intricate challenges of advanced networking technologies and also provides a critical pathway for addressing the exponentially growing computational demands of future communication ecosystems.