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In this paper, we consider a setting in which geographically constrained “local” wireless services operate in a shared spectrum band and compete in the same market for customers who fall within their local coverage areas. When their desired coverage areas overlap, there are multiple ways that spectrum usage could be coordinated. We discuss ways in which this coordination could arise. We then characterize the market impacts of different forms of coordination via a framework of Cournot competition with congestion. Our analysis illustrates the economic trade-offs of different coordination mechanisms for local services. 

Future G networks will require more dynamic, agile support for the management of radio spectrum on a fine-grained basis. The radio access network (RAN) technologies necessary to enable Dynamic Spectrum Access (DSA) have progressed significantly over the past 20 years, but the challenges of realizing the potential for DSA requires the co-evolution of technologies, business models, and regulatory policy. This paper presents a multidisciplinary research effort to develop the building blocks needed to advance DSA. In particular, we focus on the use of standards-based Spectrum Consumption Models (SCMs) and review on-going research to incorporate SCMs in an automated management framework based on incentive-compatible, technically-sound spectrum access contracts referred to as Spectrum Access Agreements (SAAs). This paper introduces the core concepts of the SCM/SAA framework, project goals, and preliminary insights into how the framework can help improve spectrum management. The research on SCM/SAA represents a bottom-up effort to develop the techno-economic building blocks or tools to facilitate marketbased experimentation and development of DSA based spectrum sharing markets, business models, and applications. 

Future G networks will require more dynamic, agile support for the management of radio frequency spectrum on a fine-grained basis. The radio access network (RAN) technologies necessary to enable Dynamic Spectrum Access (DSA) have progressed significantly over the past 20 years, but the challenges of realizing the potential for DSA requires the co-evolution of the technologies, business models/market structures, and regulatory policy for wireless networks. This paper discusses a bottom-up, multi-disciplinary approach to DSA. In particular, we focus on the use of standards-based Spectrum Consumption Models (SCMs), and review on-going research to incorporate SCMs in an automated management framework based on incentive-compatible, technically-sound spectrum access contracts, or Spectrum Access Agreements (SAAs). This work is being undertaken as part of the NSF National Radio Dynamic Zone (NRDZ) research initiative and this paper provides an introduction to the core concepts of the SCM/SAA framework, project goals, and preliminary insights into how the SCM/SAA can help improve spectrum management and advance R&D efforts to enable the transition to a shared spectrum future. The SCM/SAA research represents a bottom-up effort to develop the techno-economic tools to facilitate market-based experimentation and development of spectrum sharing markets, business models, and applications to complement and render more economically viable and relevant emerging DSA technologies and top-down regulatory reforms aimed at lowering spectrum sharing barriers. 

Command and control (C2) data links over cellular networks is envisioned to be a reliable communications modality for various types of missions for Unmanned Aircraft System (UAS). The planning of UAS traffic and the provision of cellular communication resources are cross-coupled decisions that should be analyzed together to understand the quality of service such a modality can provide that meets business needs. The key to effective planning is the accurate estimation of communication link quality and the resource usage for a given air traffic requirement. In this work, a simulation and modelling framework is developed that integrates two open-source simulation platforms, Repast Simphony and ns-3, to generate UAS missions over different geographical areas and simulates the provision of 4G/5G cellular network connectivity to support their C2 and mission data links. To the best of our knowledge, this is the first simulator that co-simulates air traffic and cellular network communications for UAS while leveraging standardized 3GPP propagation models and incorporating detailed management of communication channels (i.e., resource blocks) at the cellular base station level. Three experiments were executed to demonstrate how the integrated simulation platform can be used to provide guidelines in communication resource allocation, air traffic management, and mission safety management in beyond visual line of sight (BVLOS) operations. 

Small Unmanned Aircraft Systems (sUAS) will be an important component of the smart city and intelligent transportation environments of the near future. The demand for sUAS related applications, such as commercial delivery and land surveying, is expected to grow rapidly in next few years. In general, sUAS traffic routing and management functions are needed to coordinate the launching of sUAS from different launch sites and determine their trajectories to avoid conflict while considering several other constraints such as expected arrival time, minimum flight energy, and availability of communication resources. However, as the airborne sUAS density grows in a certain area, it is difficult to foresee the potential airspace and communications resource conflicts and make immediate decisions to avoid them. To address this challenge, we present a temporal and spatial routing algorithm and simulation platform for sUAS trajectory management in a high density urban area that plans sUAS movements in a spatial and temporal maze taking into account obstacles that are either static or dynamic in time. The routing allows the sUAS to avoid static no-fly areas (i.e. static obstacles) or other in-flight sUAS and areas that have congested communication resources (i.e. dynamic obstacles). The algorithm is evaluated using an agent-based simulation platform. The simulation results show that the proposed algorithm outperforms other route management algorithms in many areas, especially in processing speed and memory efficiency. Detailed comparisons are provided for the sUAS flight time, the overall throughput, conflict rate and communication resource utilization. The results demonstrate that our proposed algorithm can be used to address the airspace and communication resource utilization needs for a next generation smart city and smart transportation.