The Internet of Vehicles (IoV) is rapidly expanding, and vehicles continuously generate complex and interdependent computation tasks, which contain precedence constraints that complicate their real-time offloading. These tasks must be processed in real time and with strong security guarantees, which introduces significant challenges in both computation efficiency and trust management. To address these challenges, we investigate secure online offloading of large-scale dependent tasks in distributed edge-assisted IoV environments and develops a blockchain-assisted multi-level feedback queue framework that ensures transparent task registration, tamper-proof execution logs, and fairness among vehicular nodes. To optimize the offloading strategy under dynamic and uncertain system states, the problem is formulated as a multi-stage mixed-integer nonlinear program, and a trust-aware blockchain reinforcement learning algorithm (TaBRL) is developed. The blockchain consensus layer provides verifiable feedback to guide adaptive learning and strengthen decision reliability. Extensive experiments demonstrate that the proposed framework achieves superior task throughput, queue stability, and defense robustness, effectively ensuring secure, trustworthy, and efficient coordination in edge-assisted IoV environments.

In 3GPP networks, Access Traffic Steering, Switching, and Splitting (ATSSS) is specified to enable network-assisted use of multiple paths based on a multipath-enabled QUIC tunnel. Using multiple paths can introduce packet reordering because of path asymmetry, loss, and differing delays. Packet reordering may be mistaken for congestion and thus lead to unnecessary reductions in throughput. While various packet-based schedulers have been proposed to address these issues, stream-aware concepts have not been widely explored when multiple end-to-end flows use the same tunnel. In this paper, we present our scheduling framework that supports stream-aware scheduling and compare state-of-the-art packet-based schedulers to stream-aware approaches in the multipath tunnel scenario, with particular focus on the Bandwidth-Limited Application Demand Estimating (BLADE) scheduler. Our results show that stream-aware scheduling concepts mitigate reordering and improve throughput in this scenario. This paper provides an overview of the current state of stream-aware scheduling for proxy scenarios. Future work is necessary and should investigate stream-aware techniques, adapt packet-based schedulers to be stream-aware, and propose novel stream-aware schedulers.

In Release 19, the Third Generation Partnership Project (3GPP) not only focused on the commercial deployment needs of 5G-Advanced but also prepared channel models for 6G. Targeting feasible channel models that satisfy the requirements of upcoming 6G studies and evaluation, two study items (SIs) on channel modeling for the 7-24 GHz spectrum and integrated sensing and communication (ISAC) were conducted in Release 19. The existing channel model was validated for the emerging 7-24 GHz spectrum and extended to accommodate characteristics introduced by larger antenna arrays. Meanwhile, a new ISAC channel model was proposed to bridge the gap in ISAC performance evaluation. This article presents a comprehensive overview of Release 19 channel modeling and discusses potential implementations for 6G performance evaluation.

As next-generation mobile networks evolve toward 6G and beyond, intelligent semantic-aware networking across heterogeneous environments becomes paramount for real-time, mission-critical analytics. This work introduces a trust-centric networking framework for the future mobile core, built around a novel, multi-dimensional Quality of Trust (QoT) profile. This profile enables applications to specify dynamic trust requirements, allowing for adaptive and trustworthy service orchestration. The framework is also designed with quantum-ready capabilities to ensure robust data protection in future infrastructures. We validate the core principles of our framework on a testbed emulating an Uncrewed Aerial Vehicle (UAV)-assisted disaster-response mission. Using real-world measurements from classical nodes and analytical models for more secure scenarios, we demonstrate that our trust-aware orchestration preserves the critical latency and bandwidth gains of edge offloading while virtually eliminating processing errors from untrusted nodes.

Affine frequency division multiplexing (AFDM) is an emerging waveform candidate for future sixth-generation (6G) systems offering a range of promising features, such as enhanced robustness in heterogeneous and high-mobility environments, as well as inherent suitability for integrated sensing and communications (ISAC) applications. In addition, unlike other candidates such as orthogonal time-frequency space (OTFS) modulation, AFDM provides several unique advantages that strengthen its relevance to practical deployment and standardization in 6G. Notably, as a natural generalization of orthogonal frequency division multiplexing (OFDM), strong backward compatibility with existing conventional systems is guaranteed, while also offering novel possibilities in waveform design, for example to enable physical-layer security through its inherent chirp parametrization. In all, this article provides an overview of AFDM, emphasizing its suitability as a candidate waveform for 6G standardization. First, we provide a concise introduction to the fundamental properties and unique characteristics of AFDM, followed by highlights of its advantageous features, and finally a discussion of its potential and challenges in 6G standardization efforts and representative requirements.

6G aims to provide significant advantages compared to 5G while being designed to be energy-efficient and quantum-safe from the start. Goals for 6G include superior radio performance, ubiquitous connectivity using Satellite (Non-Terrestrial Networks, NTN) and Wi-Fi, native artificial intelligence (AI) support, a modular design enabling delivery of new features in a modular manner and scalable networks for diverse device types. In this paper, we present a modular Non-Access Stratum (NAS) approach allowing new NAS features to be upgraded and deployable without impacting other NAS features that are already deployed. We include a high-level view of the 6G System architecture, and a detailed view of modular NAS protocol stack, including NAS message routing, User equipment (UE) identifiers and NAS security architecture. We conclude with an outlook for future work.