The upcoming sixth generation (6G) mobile communication system is expected to operate across a wide range of spectrum that includes not only the bands used by previous generations but also higher frequency bands such as millimeter wave (mmWave), which are currently assigned to fifth generation (5G) networks, terahertz (THz), and optical spectrum. By utilizing a broader range of frequencies, it will be possible to support 6G applications with faster data rates, higher capacity, and lower latency. However, the higher frequency bands pose unique challenges such as higher path loss, absorption loss, and engineering difficulties for antennas and radio frequency (RF) circuitry design, which require advanced technologies and innovative solutions. Given that the spectrum is a scarce resource, efficient management is crucial to ensure the most effective exploitation of frequency bands. The spectrum management has evolved over the years, with different approaches being used to assign and utilize frequency bands. In this paper, we provide a review of spectrum management approaches, including their use in awarding 5G spectrum, and explore their expected use in 6G. We then offer a brief overview of spectrum sharing and its role in enabling the efficient use of spectrum resources. The regulations, standardization, features, limitations, and potential use cases of higher frequency bands such as, mmWave, THz, and visible light (VL) are analyzed to provide a comprehensive understanding of the spectrum options available for the upcoming 6G technology.

The unprecedented advancement of Artificial Intelligence (AI) has positioned Explainable AI (XAI) as a critical enabler in addressing the complexities of next-generation wireless communications. With the evolution of the 6G networks, characterized by ultra-low latency, massive data rates, and intricate network structures, the need for transparency, interpretability, and fairness in AI-driven decision-making has become more urgent than ever. This survey provides a comprehensive review of the current state and future potential of XAI in communications, with a focus on network slicing, a fundamental technology for resource management in 6G. By systematically categorizing XAI methodologies–ranging from model-agnostic to model-specific approaches, and from pre-model to post-model strategies–this paper identifies their unique advantages, limitations, and applications in wireless communications. Moreover, the survey emphasizes the role of XAI in network slicing for vehicular network, highlighting its ability to enhance transparency and reliability in scenarios requiring real-time decision-making and high-stakes operational environments. Real-world use cases are examined to illustrate how XAI-driven systems can improve resource allocation, facilitate fault diagnosis, and meet regulatory requirements for ethical AI deployment. By addressing the inherent challenges of applying XAI in complex, dynamic networks, this survey offers critical insights into the convergence of XAI and 6G technologies. Future research directions, including scalability, real-time applicability, and interdisciplinary integration, are discussed, establishing a foundation for advancing transparent and trustworthy AI in 6G communications systems.

This paper provides a comprehensive overview of the evolution of Machine Learning (ML), from traditional to advanced, in its application and integration into unmanned aerial vehicle (UAV) communication frameworks and practical applications. The manuscript starts with an overview of the existing research on UAV communication and introduces the most traditional ML techniques. It then discusses UAVs as versatile actors in mobile networks, assuming different roles from airborne user equipment (UE) to base stations (BS). UAV have demonstrated considerable potential in addressing the evolving challenges of next-generation mobile networks, such as enhancing coverage and facilitating temporary hotspots but pose new hurdles including optimal positioning, trajectory optimization, and energy efficiency. We therefore conduct a comprehensive review of advanced ML strategies, ranging from federated learning, transfer and meta-learning to explainable AI, to address those challenges. Finally, the use of state-of-the-art ML algorithms in these capabilities is explored and their potential extension to cloud and/or edge computing based network architectures is highlighted.

In this letter, we investigate the pilot contamination problem in massive MIMO networks from a system level point of view. We propose two game-theoretic approaches that model the pilot assignment problem. We also prove that one of these games is a potential game, and show that the adaptation processes following best and better response dynamics will converge to a Nash equilibrium. We then model the problem as an optimization problem. Finally, we compare the game theoretic results with the optimal and random pilot assignments. Our simulation results show that the game solution significantly outperforms the random assignment and performs as well as the optimal pilot assignment.

End users increasingly expect ubiquitous connectivity while on the move. With a variety of wireless access technologies available, we expect to always be connected to the technology that best matches our performance goals and price points. Meanwhile, sophisticated onboard units (OBUs) enable geolocation and complex computation in support of handover. In this paper, we present an overview of vertical handover techniques and propose an algorithm empowered by the IEEE 802.21 standard, which considers the particularities of the vehicular networks (VNs), the surrounding context, the application requirements, the user preferences, and the different available wireless networks [i.e., Wireless Fidelity (Wi-Fi), Worldwide Interoperability for Microwave Access (WiMAX), and Universal Mobile Telecommunications System (UMTS)] to improve users' quality of experience (QoE). Our results demonstrate that our approach, under the considered scenario, is able to meet application requirements while ensuring user preferences are also met.

To provide cellular systems with additional spectral resources, the wireless industry is considering the aggregation of frequency carriers in licensed, unlicensed, and shared access (SA) bands. In this article, we focus on reliable carrier aggregation/channel bonding (CA/CB) techniques, in which when CA/CB between the licensed, unlicensed, and SA carriers is performed, the licensed carrier is used for the primary and secondary carriers, and the unlicensed and SA carriers operate as additional secondary carriers. We provide a taxonomy of the use of CA/CB in cellular networks and highlight the differences between different CA/CB approaches. We make the case that although the licensed primary carrier can give reliable transmission of control signaling, due to the nature of unlicensed and SA bands, for the efficient aggregation of secondary carriers there is a need for new CA/CB methods. To illustrate our case for novel CA/CB methods, we provide examples for different network environments where intelligent CA/CB decisions can increase throughput compared to the traditional CA/CB methods. Finally, we highlight challenges in the design of novel CA/CB techniques in unlicensed and SA bands.