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1. Automating Spectrum Sharing from the Ground Up

   Lehr, William; Berry, Randall; Caicedo, Carlos; Kadota, Igor; Mu, Kangle; Xie, Zongyun; Tamim, Irfan (August 2024, SSRN)

   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. 

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2. Large-Scale Dynamic Spectrum Access with IEEE 1900.5.2 Spectrum Consumption Models

   https://doi.org/10.1109/WCNC55385.2023.10118670  

   Netalkar, Prasad; Zahabee, Azhaan; Caicedo Bastidas, Carlos E.; Kadota, Igor; Stojadinovic, Dragoslav; Zussman, Gil; Seskar, Ivan; Raychaudhuri, Dipankar (March 2023, in Proc. IEEE WCNC’23, Mar. 2023)

   Next generation wireless services and applications, including Augmented Reality, Internet-of-Things, and Smart- Cities, will increasingly rely on Dynamic Spectrum Access (DSA) methods that can manage spectrum resources rapidly and efficiently. Advances in regulatory policies, standardization, networking, and wireless technology are enabling DSA methods on a more granular basis in terms of time, frequency, and geographical location which are key for the operation of 5G and beyond-5G networks. In this context, this paper proposes a novel DSA algorithm that leverages IEEE 1900.5.2 Spectrum Consumption Models (SCMs) which offer a mechanism for RF devices to: (i) “announce” or “declare” their intention to use the spectrum and their needs in terms of interference protection; and (ii) determine compatibility (i.e., non-interference) with existing devices. In this paper, we develop an SCM-based DSA algorithm for spectrum deconfliction in large-scale wireless network environments and evaluate this algorithm in terms of computation time, efficiency of spectrum allocation, and number of device reconfigurations due to interference using a custom simulation platform. The results demonstrate the benefits of using SCMs and their capabilities to perform fine grained spectrum assignments in dynamic and dense communication environments. 

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3. Scalable Dynamic Spectrum Access with IEEE 1900.5.2 Spectrum Consumption Models

   https://doi.org/10.36227/techrxiv.171216553.32959940/v1  

   Netalkar, Prasad; Bastidas, Carlos_E Caicedo; Kadota, Igor; Zussman, Gil; Seskar, Ivan; Raychaudhuri, Dipankar (April 2024, Techrxiv)

   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. 

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4. A Spectrum Consumption Model-based Framework for DSA Experimentation on the COSMOS Testbed

   https://doi.org/10.1145/3477086.3480836  

   Stojadinovic, Dragoslav; Netalkar, Prasad; Bastidas, Carlos E.; Kadota, Igor; Zussman, Gil; Seskar, Ivan; Raychaudhuri, Dipankar (January 2021, in Proc. ACM MobiCom’21 Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization (WiNTECH’21))

   This paper describes a wireless experimentation framework for studying dynamic spectrum access mechanisms and an experiment that showcases its capabilities. The framework was built on COSMOS, an advanced wireless testbed designed to support real-world experimentation of next generation wireless technologies and applications. Our deployed framework supports experimentation over a large number of wireless networks, with a PUB-SUB based network interaction structure, based on the Collaborative Intelligent Radio Networks (CIRN) Interaction Language (CIL) developed by DARPA for the Spectrum Collaboration Challenge (SC2). As such, it enables interaction and message exchanges between the networks for the purposes of coordinating spectrum use. For our experiment, the message exchanges are aimed primarily for, but not limited to, Spectrum Consumption Model (SCM) messages. RF devices/systems use SCM messages which contain detailed information about their wireless transmission characteristics (i.e., spectrum mask, frequency, bandwidth, power and location) to determine their operational compatibility (non-interference) with prior transmitters and receivers, and to dynamically determine spectrum use characteristics for their own transmissions. 

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5. Greedy Channel Selection for Dynamic Spectrum Access Radios

   https://doi.org/10.1109/ISCAS45731.2020.9180882  

   Lackpour, Alex; Rey, Xaime Rivas; Mainland, Geoffrey; Dandekar, Kapil R. (October 2020, 2020 IEEE International Symposium on Circuits and Systems)

   Dynamic Spectrum Access (DSA) radios typically select their radio channels according to their data networking goals, a defined DSA spectrum operating policy, and the state of the RF spectrum. RF spectrum sensing can be used to collect information about the state of the RF spectrum and prioritize which channels should be assigned for DSA radio waveform transmission and reception. This paper describes a Greedy Channel Ranking Algorithm (GCRA) used to calculate and rank RF interference metrics for observed DSA radio channels. The channel rankings can then be used to select and/or avoid channels in order to attain a desired DSA radio performance level. Experimental measurements are collected using our custom software-defined radio (SDR) system to quantify the performance of using GCRA for a DSA radio application. Analysis of these results show that both pre and post-detection average interference power metrics are the most accurate metrics for selecting groups of radio channels to solve constrained channel assignment problems in occupied gray space spectrum. 

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