Radio Dynamic Zones (RDZs) enable diverse spectrum-sharing scenarios, including advancing coexistence between active and passive spectrum users. We consider RDZs where radio astronomy coexists with active transmitters and introduce the Adaptive Spectrum Tuning and Reactive Allocation (ASTRA) framework, used by a Zone Management System to manage spectrum access to transmitters. We develop and deploy the first RDZ testbed at a radio astronomy facility, the Hat Creek Radio Observatory, to demonstrate a practical solution for managing interference from active transmitters to radio telescopes in over-the-air experiments. To comprehensively evaluate our approach, we simulate RDZs of varying sizes and demonstrate that ASTRA maintains interference at the radio telescopes below acceptable thresholds while maximizing spectrum access to transmitters in the zone.

Radio Dynamic Zones (RDZs) are being explored by the research community as an approach to safely test and evaluate spectrum sharing mechanisms and technologies. There is general consensus in the research community regarding the conceptual architecture of an RDZ. In this paper, we present our work on the Powder-RDZ, a prototype RDZ developed and built on the Powder platform. We present a practical RDZ architecture and explore a number of end-to-end use-cases. We present the design and implementation of OpenZMS, our prototype RDZ Zone Management System, and evaluate it in the Powder platform.

The University of Colorado, Boulder (CU), has been collaborating with the Hat Creek Radio Observatory (HCRO) and UC Berkeley (UCB) to prototype a National Radio Dynamic Zone (NRDZ) which facilitates spectrum sharing between Radio Astronomy (RA), and active users of the electromagnetic spectrum.

Radio Dynamic Zones (RDZs) are being explored by the research community as an approach to safely test and evaluate spectrum sharing mechanisms and technologies. There is general consensus in the research community regarding the conceptual architecture of an RDZ. In this paper, we present our work on the Powder-RDZ, a prototype RDZ developed and built on the POWDER platform. We present a practical RDZ architecture and explore a number of end-to-end use-cases. We present the design and implementation of OPENZMS, our prototype RDZ Zone Management System, and evaluate it in the POWDER platform.

The complex and invisible nature of dynamic radio usage poses a significant challenge, even for the most skilled engineers, in visualizing and effectively utilizing radio frequency data. This paper proposes a method to quantify and visualize what is known about spectrum occupancy in a region from spectrum monitoring data recorded at a few fixed locations. The average observed signal strengths at the monitors are extrapolated throughout the region through likelihood estimation of transmitter location(s) and a simple log-distance path loss model. New georeferenced spectrum occupancy visualizations that combine estimates of occupancy power with duty cycle, and of signal variation with confidence level are introduced offering insights into planning for future allocations, interference, and broadcast coverage analysis. The spectrum consumption trends over different times of day and seasons are analyzed and interpreted.

This work proposes a quick, accurate means to generate correction factors for established propagation models such as the terrain-integrated rough earth model (TIREM). Minimum Mean-Squared Error (MMSE) techniques are applied, extending the applicability of this already-accurate and flexible propagation model and improving the standard deviation error between measurement and predicted by as much as 6.9 dB in some regions of a signal-strength measurement campaign at the University of Utah campus.

POWDER is a highly flexible, deeply programmable, and city-scale scientific instrument that enables cutting-edge research in wireless technologies. Researchers interact with the POWDER platform via the Internet to conduct their experiments, with zero penalty for remote access. In this two-part demonstration, the POWDER implementers show how to use the platform. First, they present the workflow that researchers follow to conduct experiments. Second, they highlight some of the hardware and software building blocks available through POWDER, including components related to over-the-air wireless and mobile networking, 5G, and massive MIMO.

Real world testbeds, like the POWDER platform (Platform for Open Wireless Data-driven Experimental Research), enable a broad range of mobile and wireless research. Given the flexibility of this platform, a key concern for platform users is selecting a set of wireless resources that will satisfy the requirements of their experiments. In this paper we present the design and implementation of WiMatch a wireless resource matchmaking system. We illustrate the utility of our approach by evaluating it in the POWDER platform.

The need to find more efficient ways to share and use wireless spectrum has resulted in renewed interest in radio frequency (RF) propagation modeling. The open and programmable nature of the POWDER (Platform for Open Wireless Datadriven Experimental Research) mobile and wireless platform offers a unique environment in which to test and validate RF propagation modeling approaches. In this paper we present our work illustrating how POWDER based RF measurements can be performed, as a form of "ground truth", and compared with predicted RF signal strength based on a propagation model. We make use of the Shout RF measurement framework available on POWDER to perform a series of RF measurements. We compare these measurements with predicted power levels using the open source RF Signal Propagation, Loss, And Terrain (SPLAT!) analysis tool. We present our results and a brief terrain analysis to provide real-world context for it. Our work is "packaged" as a POWDER profile to allow others to repeat our analysis and to serve as a starting point for further RF measurement and propagation related research.

This repository contains our raw datasets from channel measurements performed at the University of Utah campus. In addition, we have included a document that explains the setup and methodology used to collect this data, as well as a very brief discussion of results. File organization: * documentation/ - Contains a .docx with the description of the setup and evaluation. * data/ - HDF5 files containing both metadata and raw IQ samples for each location at which data was collected. Notice we collected data at 14 different client locations. See map in the attached docx (skipped locations 12 and 16). We deployed 5 different receivers at 5 different rooftops. Due to resource constraints, one set of files contains data from 4 different locations whereas another set contains information from the single remaining location. We have developed a set of python scripts that allow us to parse and analyze the data. Although not included here, they can be found in our public repository: https://github.com/renew-wireless/RENEWLab You can find the top script here. For more information on the POWDER-RENEW project please visit the POWDER website. The RENEW part of the project focuses on the deployment of an open-source massive MIMO system. Please visit our website for more information.

This paper provides an overview of the Platform for Open Wireless Data-driven Experimental Research (Powder). Powder is a city-scale, remotely accessible, end-to-end software defined platform to support mobile and wireless research. Compared to other mobile and wireless testbeds Powder provides advances in scale, realism, diversity, flexibility, and access.

Given the highly empirical nature of research in cloud computing, networked systems, and related fields, testbeds play an important role in the research ecosystem. In this paper, we cover one such facility, CloudLab, which supports systems research by providing raw access to programmable hardware, enabling research at large scales, and creating a shared platform for repeatable research. We present our experiences designing CloudLab and operating it for four years, serving nearly 4,000 users who have run over 79,000 experiments on 2,250 servers, switches, and other pieces of datacenter equipment. From this experience, we draw lessons organized around two themes. The first set comes from analysis of data regarding the use of CloudLab: how users interact with it, what they use it for, and the implications for facility design and operation. Our second set of lessons comes from looking at the ways that algorithms used "under the hood," such as resource allocation, have importantand sometimes unexpected-effects on user experience and behavior. These lessons can be of value to the designers and operators of IaaS facilities in general, systems testbeds in particular, and users who have a stake in understanding how these systems are built.

Fail-slow hardware is an under-studied failure mode. We present a study of 114 reports of fail-slow hardware incidents, collected from large-scale cluster deployments in 14 institutions. We show that all hardware types such as disk, SSD, CPU, memory, and network components can exhibit performance faults. We made several important observations such as faults convert from one form to another, the cascading root causes and impacts can be long, and fail-slow faults can have varying symptoms. From this study, we make suggestions to vendors, operators, and systems designers.

We will demonstrate features and capabilities of the PhantomNet testbed. PhantomNet is a mobile testbed, at the University of Utah, aimed at enabling a broad range of mobile networking related research. PhantomNet is remotely accessible and open to the mobile networking research community.

The PhantomNet facility allows experimenters to combine mobile networking, cloud computing and software-defined networking in a single environment. It is an end-to-end testbed , meaning that it supports experiments not just with mobile end-user devices but also with a cellular core network that can be configured and extended with new technologies. This article introduces PhantomNet and presents a road map for its future development. The current PhantomNet prototype is available now at no cost to researchers and educational users.

We present MobiScud, an evolutionary mobile network architecture that integrates cloud and SDN technologies into a standard mobile network in backwards compatible fashion. MobiScud enables personalized virtual machines to seamlessly "follow" mobile network users as they move around.

Repeating research in computer science requires more than just code and data: it requires an appropriate environment in which to run experiments. In some cases, this environment appears fairly straightforward: it consists of a particular operating system and set of required libraries. In many cases, however, it is considerably more complex: the execution environment may be an entire network, may involve complex and fragile configuration of the dependencies, or may require large amounts of resources in terms of computation cycles, network bandwidth, or storage. Even the "straightforward" case turns out to be surprisingly intricate: there may be explicit or hidden dependencies on compilers, kernel quirks, details of the ISA, etc. The result is that when one tries to repeat published results, creating an environment sufficiently similar to one in which the experiment was originally run can be troublesome; this problem only gets worse as time passes. What the computer science community needs, then, are environments that have the explicit goal of enabling repeatable research. This paper outlines the problem of repeatable research environments, presents a set of requirements for such environments, and describes one facility that attempts to address them.

We present our Software-defined network Mobile Offloading aRchitecturE (SMORE). SMORE realizes traffic offloading in mobile networks without requiring any changes to the functionality of existing mobile network nodes. At the same time, it is fully aware of mobile network functionality, including mobility.