Defining incumbent protection zones on the fly: Dynamic boundaries for spectrum sharing

Bhattarai, Sudeep; Ullah, Abid; Park, Jung‐Min; Reed, Jennifer L.; Gurney, David; Gao, Bo

doi:10.1109/dyspan.2015.7343908

Cited by 29 publications

(14 citation statements)

References 20 publications

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“…Their size is based on several factors, including transmit power, antenna gains in the direction of the interference, time variations of antenna gains, receiver susceptibility to interference, propagation effects of radio waves, mobility of earth station, etc. [10]. Moreover, transmission privileges for SU in this Coordination Zones are granted if, and only if, the proposed transmission will not interfere with the normal operations of the PU.…”

Section: The Spectrum Sharing Frameworkmentioning

confidence: 99%

“…Based on the definitions of Exclusion and Coordination zones, authors like Bhattarai and Altamimi have developed approaches to especify the characteristics of these new sharing environment. In particular, methods for creating and sizing both resticted zones [3], [9], [10]. These approaches propose a more flexible scheme that the one suggested by the FCC/NTIA.…”

Section: The Spectrum Sharing Frameworkmentioning

confidence: 99%

“…The objective is to reduce the size of both types of zones to increase the value and incentives for potential new entrants. In this paper, we will be using the notation introduced by Bhattarai et al in their definitions of the "The Multi-Tiered Incumbent Protection Zones (MIPZ)" [10]. This proposed framework for geolocation-database-driven spectrum sharing gives the option to the Primary User to adjust the size of the coordination and exclusion zones "on the fly".…”

Section: The Spectrum Sharing Frameworkmentioning

confidence: 99%

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Agent-Based Modeling Approach for Developing Enforcement Mechanisms in Spectrum Sharing Scenarios: An Application for the 1695-1710mhz Band

et al. 2018

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As radio spectrum sharing matures, one of the main challenges becomes finding adequate governance systems and the appropriate enforcement mechanisms. Historically, these processes were assigned to a central entity (in most cases a governmental agency). Nevertheless, the literature of Common Pool Resources (CPRs) shows that other governance mechanisms are possible, which include collaboration with a private, thirdparty regulator or the complete absence of central institutions, as in self-enforcement solutions. These alternatives have been developed around well-known CPRs such as fisheries, forests, etc. As argued by Weiss et al [50], and other researchers, spectrum can indeed be considered to be a CPR. In this work we study the two extremes of governance systems that could be applied to spectrum sharing scenarios. Initially, we study the classical centralized scheme of command and control, where governmental institutions are in charge of rule-definition and enforcement. Subsequently, we explore a government-less environment, i.e., a distributed enforcement approach. In this anarchy situation (i.e., lack of a formal government intervention as defined by Leeson [29]), rules and enforcement mechanisms are solely the product of repeated interactions among the intervening agents. For our analysis, we have selected the spectrum sharing framework of the 1695-1710MHz band. We also use the definitions presented by Bhattarai et al. [9], [10] as well as Altamimi [3] for managing the size of the coordination and exclusion zones. In addition, we utilize Agent-Based Modelling (ABM) to analyze the applicability of these governance mechanisms. ABM simulation allows us to explore how macro phenomena can emerge from micro-level interactions of independent agents.

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Section: The Spectrum Sharing Frameworkmentioning

confidence: 99%

Section: The Spectrum Sharing Frameworkmentioning

confidence: 99%

Section: The Spectrum Sharing Frameworkmentioning

confidence: 99%

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Agent-Based Modeling Approach for Developing Enforcement Mechanisms in Spectrum Sharing Scenarios: An Application for the 1695-1710mhz Band

et al. 2018

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“…where β(d) = P L(r 0 )d −α is the path loss, P L(r 0 ) is the path-loss at reference distance r 0 , α is the path-loss exponent (α > 2), d is the 3D distance between the BS and the radar, and N c is the number of discrete multipath components (MPCs). The Rician factor K R ≫ 1, where propagation is dominated by the line of sight component 1 . In addition, the random small-scale fading amplitude satisfies E[γ i ] = 0 and E[|γ i | 2 ] = 1.…”

Section: A Channel Modelmentioning

confidence: 99%

“…While massive MIMO enhances spectral efficiency by increasing the dimension of spatial multiplexing by an order of magnitude, spectrum sharing improves it by sharing spectrum between different wireless technologies in the spatial and temporal dimensions. Spectrum sharing is particularly attractive in the sub-6 GHz frequency bands, where spectrum is under-utilized due to conservative policies [1]. Among the various incumbents, radars are the biggest consumer of spectrum in the sub-6 GHz bands.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Worst-Case Interference in Underlay Radar-Massive MIMO Spectrum Sharing Scenarios

Rao

Dhillon

Marojevic

et al. 2019

2019 IEEE Global Communications Conference (GLOBECOM)

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In this paper, we consider an underlay radarmassive MIMO spectrum sharing scenario in which massive MIMO base stations (BSs) are allowed to operate outside a circular exclusion zone centered at the radar. Modeling the locations of the massive MIMO BSs as a homogeneous Poisson point process (PPP), we derive an analytical expression for a tight upper bound on the average interference at the radar due to cellular transmissions. The technical novelty is in bounding the worst-case elevation angle for each massive MIMO BS for which we devise a novel construction based on the circumradius distribution of a typical Poisson-Voronoi (PV) cell. While these worst-case elevation angles are correlated for neighboring BSs due to the structure of the PV tessellation, it does not explicitly appear in our analysis because of our focus on the average interference. We also provide an estimate of the nominal average interference by approximating each cell as a circle with area equal to the average area of the typical cell. Using these results, we demonstrate that the gap between the two results remains approximately constant with respect to the exclusion zone radius. Our analysis reveals useful trends in average interference power, as a function of key deployment parameters such as radar/BS antenna heights, number of antenna elements per radar/BS, BS density, and exclusion zone radius.

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Policy Enforcement in Dynamic Spectrum Sharing

2020

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Defining incumbent protection zones on the fly: Dynamic boundaries for spectrum sharing

Cited by 29 publications

References 20 publications

Agent-Based Modeling Approach for Developing Enforcement Mechanisms in Spectrum Sharing Scenarios: An Application for the 1695-1710mhz Band

Agent-Based Modeling Approach for Developing Enforcement Mechanisms in Spectrum Sharing Scenarios: An Application for the 1695-1710mhz Band

Analysis of Worst-Case Interference in Underlay Radar-Massive MIMO Spectrum Sharing Scenarios

Policy Enforcement in Dynamic Spectrum Sharing

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