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This work explores systems that deliver source updates requiring multiple sequential processing steps. We model and analyze the Age of Information (AoI) performance of various system designs under both parallel and series server setups. In parallel setups, each processor executes all computation steps with multiple processors working in parallel, while in series setups, each processor performs a specific step in sequence. In practice, processing faster is better in terms of age but it also consumes more power. To address this age-power trade-off, we formulate and solve an optimization problem to determine the optimal service rates for each processing step under a given power budget. Our analysis focuses on a special case where updates require two computational steps. The results show that the service rate of the second step should generally be faster than that of the first step to achieve minimum AoI and reduce power wastage. Furthermore, parallel processing is found to offer a better age-power trade-off compared to series processing. 

Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiberoptical domain. This paper introduces the Decentralized Multi- Party, Multi-Network AI (DMMAI) framework for integrating AI into 6G networks deployed at scale. DMMAI harmonizes AI-driven controls across diverse network platforms and thus facilitates networks that autonomously configure, monitor, and repair themselves. This is particularly crucial at the network edge, where advanced applications meet heightened functionality and security demands. The radio/optical integration is vital due to the current compartmentalization of AI research within these domains, which lacks a comprehensive understanding of their interaction. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks. The DMMAI framework is a step towards a global standard for AI in 6G, aiming to establish reference use cases, data and model management methods, and benchmarking platforms for future AI/ML solutions. 

Dynamic Spectrum Access (DSA) is a key mechanism for meeting the ever-increasing demand for emerging wireless services. DSA involves managing and assigning available spectrum resources in a way that minimizes interference and allows RF coexistence between heterogeneous devices and systems. Spectrum Consumption Models (SCMs)- defined in the IEEE 1900.5.2 standard, offer a mechanism for RF devices to: (i) declare the characteristics of their intended spectrum use and their interference protection needs; and (ii) determine compatibility (non-interference) with existing devices. In this paper, we propose a novel SCM-based Spectrum Deconfliction (SD) algorithm that dynamically configures RF operational parameters (e.g., center frequency and transmission power) of a target transmitter-receiver pair aiming to minimize interference with existing devices/systems. We also propose sequential and distributed DSA methods that use the SD algorithm for assigning spectrum in large-scale networks. To evaluate the performance of our methods in terms of computation time, spectrum assignment efficiency, and overhead, we use two custom-made simulation platforms. Finally, to experimentally demonstrate the feasibility of our methods, we build a proof-of-concept implementation in the NSF PAWR COSMOS wireless testbed. The results reveal the advantages of using SCMs and their capabilities to conduct spectrum assignments in dynamic and congested communication environments.