Radio Dynamic Zones (RDZs) have been proposed to enable diverse spectrum-sharing scenarios where transmitters within the zone must coexist safely with incumbent spectrum users outside the zone. One important class of incumbents is highly sensitive receivers used in passive services like radio astronomy. We formulate the reactive spectrum allocation framework using a multi-armed bandit model to derive a bounded optimal allocation solution with polynomial-time exploration guarantees. To further improve performance, we incorporate predictive models that forecast spectrum usage by transmitters in the RDZ to aid in proactive spectrum allocation. We empirically show that the selected transmitter set is consistently closer to the optimal set, outperforming our theoretical 1/2-approximation bound, in less than polynomial time.

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.

To enhance the prediction accuracy and efficiency of the wireless outdoor heatmap, we propose a novel federated Gaussian Process (GP) approach combined with Bayesian Model Averaging (BMA). Traditional centralized GP models need extensive communication between distributed sensors and a central server, which leads to inefficiencies, increased computational costs, and potential privacy risks. Additionally, the GP model's log-likelihood function is optimized using the entire dataset, which makes it incompatible with standard federated aggregation techniques such as Federated Averaging (FedAvg). To overcome these challenges, our approach enables each sensor to process its data locally with GP algorithms by mapping Received Signal Strength (RSS) to corresponding locations. At the central server, BMA predicts pseudo-labels from limited global data to create a pseudo-labeled set for knowledge distillation. This allows the central server to train a global student GP model that updates parameters rather than averaging local models directly as in FedAvg. The global model is then sent back to sensors for further iterations. We evaluate our approach using real-world RSS data from the National Science Foundation (NSF) funded Platform for Open Wireless Data-driven Experimental Research (POWDER) at the University of Utah. The experiment results demonstrate that the proposed federated GP model significantly outperforms existing methods, including the federated GP schemes using FedAvg and classical Alternating Direction of Multipliers Method (cADMM) and federated Neural Network (NN)-based scheme.

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.

This paper outlines the components of a digital spectrum twin (DST) and potential application maps that can inform automated or enhanced spectrum management decisions. The DST is fundamentally a map and image database, with environmental, measurement, and prediction maps that allow parallel intelligence operations to generate useful information using aggregating rules that operate on the twin. We demonstrate several application maps generated from measured data collections and propagation modeling associated with the POWDER platform in Salt Lake City, Utah. In total, the methods of this paper provide a blueprint for generating similar DSTs in any other radio bands and regions of the world.

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.

Even as the COVID-19 pandemic drove advances in contact tracing and exposure notification systems, user privacy challenges continue to plague otherwise promising approaches to contain contagions. We propose a novel, scalable approach to address privacy in contact tracing that improves utility. We apply passive WiFi scan data using two metrics suitable for estimating contact between users. We support this with real world experimental data captured across a range of environments relevant to contact tracing. To preserve privacy, we leverage properties of truncated cryptographic hashes in an adaptation unique to contact tracing. This hash collision filter allows users to share information about potential contacts with a central server without revealing sensitive information. Using an aggressive threat model, including adversarial users and a malicious server, we share how this technique can improve utility while still providing strong security protections compared to other approaches using, for example, only Bluetooth (BT) or global navigation satellite systems (GNSSs). Finally, we discuss a capability of this approach that allows notification for asynchronous co-location from past contacts.

Transmitter localization is an important component of next-gen spectrum sharing and management systems. Recently, machine learning (ML) methods have shown promising results for localization in complex environments. However, existing ML models have significant limitations, such as the need for careful parameter tuning and the lack of accuracy on out-of-distribution (OOD) examples. Moreover, current ML models do not typically provide a "confidence" in their prediction. In this work, we propose a new training procedure based on the Earth Mover's Distance (EMD) that improves on these limitations. The method improves OOD accuracy by up to 22% while providing more interpretable results. The EMD-trained model also produces a confidence score, which can be used to identify high-error and OOD examples. We demonstrate how model confidence serves as a guide for hybrid localization models. This includes selecting the most reliable prediction from multiple models based on confidence values or resorting to a fallback path loss technique in cases of low confidence. Our work establishes the importance of model confidence in improving the accuracy of localization and as a mechanism for effective decision making in localization applications.

Radio Dynamic Zones (RDZs) are emerging as a means to enable dynamic spectrum sharing. Passive services like remote satellite sensing, radio astronomy, and earth sciences are vital candidates to share spectrum with RDZs. RDZs must protect sensitive receivers outside the zone from undesirable interference from secondary spectrum use inside the zone. We develop a scalable, novel reactive framework to minimize interference at the sensitive receivers while maximizing spectrum utilization within the zone. We utilize interference supervision at the sensitive receiver site to manage allocation decisions. We present a complete, viable, and deployable spectrum management solution and evaluate its operation both in over-the-air experiments using the POWDER wireless testbed and by simulating a real spectrum-sharing scenario with a sensitive receiver and varying sizes of RDZs at long distances. By incorporating location information, propagation characteristics, and an exponentially weighted moving average of the number of RDZ users sharing the band we achieve lower interference periods at the sensitive receiver and high spectrum utilization in the RDZ.

Future virtualized radio access network (vRAN) infrastructure providers (and today's experimental wireless testbed providers) may be simultaneously uncertain what signals are being transmitted by their base stations and legally responsible for their violations. These providers must monitor the spectrum of transmissions and external signals without access to the radio itself. In this paper, we propose FDMonitor, a full-duplex monitoring system attached between a transmitter and its antenna to achieve this goal. Measuring the signal at this point on the RF path is necessary but insufficient since the antenna is a bidirectional device. FDMonitor thus uses a bidirectional coupler, a two-channel receiver, and a new source separation algorithm to simultaneously estimate the transmitted signal and the signal incident on the antenna. Rather than requiring an offline calibration, we also adaptively estimate the linear model for the system on the fly. FDMonitor has been running on a real-world open wireless testbed, monitoring 19 SDR platforms controlled (with bare metal access) by outside experimenters over a seven month period, sending alerts whenever a violation is observed. Our experimental results show that FDMonitor accurately separates signals across a range of signal parameters. Over more than 7 months of observation, it achieves a positive predictive value of 97%, with a total of 20 false alerts.

The tremendous growth of wireless services has created an ever-increasing demand for the radio frequency spectrum. However, most of the spectrum, especially in the sub-6 GHz frequency ranges, have been allocated. Given the observation that a large part of the allocated spectrum remains unused in various locations and at different times, dynamic spectrum access technologies that allow for opportunistic use of the allocated bands when they are idle are being developed. In this paper, we study the spectrum usage in the frequency range of 700 MHz to 2.8 GHz at Salt Lake City, Utah. Our study indicates that several portions of these frequencies are under-utilized, with an average of only 19% usage. Furthermore, we observe that certain frequency bands demonstrate clear usage patterns, e.g., show higher utilization during the daytime compared to night-time, that can be exploited for opportunistic secondary usage of the spectrum. We propose a spectrum prediction system using Long Short-Term Memory (LSTM) neural networks to predict the occupancy of a channel in future time slots. We further introduce an LSTM based Window Selector to find the optimal window of future forecasts that increase the utilization of the network while minimizing the interference caused by the opportunistic user. Our experiments show that the Multivariate LSTM model can be reliably used to guide the choice of the channel for the opportunistic user. The multistep LSTM models can be used to forecast spectrum usage with approximately 96% accuracy on the frequency bands exhibiting discernible usage patterns.

We investigate the robustness of a convolutional neural network (CNN) RF transmitter localization model in the face of adversarial actors which may poison or spoof sensor data to disrupt or defeat the algorithm. We train the CNN to estimate transmitter locations based on sensor coordinates and received signal strength (RSS) measurements from a real-world dataset. We consider attacks from adversaries with varying capabilities to include naive, random attacks and omniscient, worst-case attacks. We apply countermeasures based on statistical outlier approaches and train the CNN against adversarial attacks to improve performance. Adversarial training is shown to completely neutralize some attacks and improve accuracy by up to 65% in other cases. Our evaluation of countermeasures indicates that a combination of statistical techniques and adversarial training can provide more robust defense against adversarial attacks.

Transmitter localization remains a challenging problem in large-scale outdoor environments, especially when transmitters and receivers are allowed to be mobile. We consider localization in the context of a Radio Dynamic Zone (RDZ), a proposed experimental platform where researchers can deploy experimental devices, waveforms, or wireless networks. Wireless users outside an RDZ must be protected from harmful interference coming from sources inside the RDZ. In this setting, localizing transmitters that are causing interference is critical. One notable obstacle for developing data-driven methods for localization is the lack of large-scale training datasets. As our first contribution, we present a new dataset for localization, captured at 462.7 MHz in a 4 sq. km outdoor area with 29 different receivers and over 4,500 unique transmitter locations. Receivers are both mobile and stationary, and heterogeneous in terms of hardware, placement, and gain settings. Next, we propose a new machine learning-based localization method that can handle inputs from uncalibrated, heterogeneous receivers. Finally, we leverage our new dataset to study the robustness of our technique and others against “out of distribution” (OOD) inputs that are common in most real life applications. We show that our technique, CUTL (Calibrated U-Net Transmitter Localization), is 49% more accurate on in-distribution data, and more robust than previous methods on OOD data.

We consider the problem of spectrum sharing by multiple cellular operators. We propose a novel deep Reinforcement Learning (DRL)-based distributed power allocation scheme which utilizes the multi-agent Deep Deterministic Policy Gradient (MA-DDPG) algorithm. In particular, we model the base stations (BSs) that belong to the multiple operators sharing the same band, as DRL agents that simultaneously determine the transmit powers to their scheduled user equipment (UE) in a synchronized manner. The power decision of each BS is based on its own observation of the radio environment (RF) environment, which consists of interference measurements reported from the UEs it serves, and a limited amount of information obtained from other BSs. One advantage of the proposed scheme is that it addresses the single-agent non-stationarity problem of RL in the multi-agent scenario by incorporating the actions and observations of other BSs into each BS's own critic which helps it to gain a more accurate perception of the overall RF environment. A centralized-training-distributed-execution framework is used to train the policies where the critics are trained over the joint actions and observations of all BSs while the actor of each BS only takes the local observation as input in order to produce the transmit power. Simulation with the 6 GHz Unlicensed National Information Infrastructure (U-NII)-5 band shows that the proposed power allocation scheme can achieve better throughput performance than several state-of-the-art approaches.

We consider the power allocation problem over shared spectrum for millimeter-Wave (mmWave) cellular down-link. Existing approaches usually find sub-optimal solutions by solving a non-convex optimization which leads to scalability issues due to centralized control. Therefore, distributed and adaptive approaches are desirable. Recently, model-free Deep Reinforcement Learning (DRL) has achieved success in such wireless resource management tasks. By modeling the radio environment as a Markov Decision Process (MDP) with the base stations (BSs) being the agents, power allocation can be automated at the agent level with comparable throughput performance to conventional centralized schemes. The multi-agent setting presents new challenges as the radio environment is impacted by the joint actions of the agents and is no longer stationary from any individual agent's perspective. Existing literature bypasses this non-stationarity violation by ignoring it which may cause performance degradation. To tackle this issue, we propose a distributed continuous power allocation scheme based on a modified version of multi-agent Deep Deterministic Policy Gradient (MADDPG) that is tailored for the distributed multiple-agent setting. The proposed scheme employs a centralized-training-distributed-execution framework where Q-functions are trained over subsets of BSs while each BS determines its transmit power based only on its own local observation. It admits constant per-BS communication and computation complexity and is thus scalable to large networks. Numerical evaluation shows that the proposed scheme adapts well to a wide range of interference conditions and can achieve comparable or better performance than several state-of-the-art non-learning approaches.

Software defined radios (SDRs) are often used in the experimental evaluation of next-generation wireless technologies. While crowdsourced spectrum monitoring is an important component of future spectrum-agile technologies, there is no clear way to test it in the real world, i.e., with hundreds of users each carrying an SDR while uploading data to a cloud-based controller. Current fully functional SDRs are bulky, with components connected via wires, and last at most hours on a single battery charge. To address these needs, we design and develop a compact, portable, untethered, and inexpensive SDR we call Sitara . Our SDR interfaces with a mobile device over Bluetooth 5 and can function standalone or as a client to a central command and control server. It transmits and receives common waveforms, uploads IQ samples or processed receiver data through a mobile device to a server for remote processing and performs spectrum sensing functions. We present results from a user study involving more than 100 participants to evaluate Sitara in a hypothetical large-scale crowdsourced spectrum monitoring application. We also present a comparative analysis of Sitara to related crowdsensing systems with a particular emphasis on the role of incentives and user participation.

To attain automation across different applications, nuclear power plants are beginning to leverage advancements in wireless communication technologies. A “one-size-fits-all” solution cannot be applied since wireless technologies are selected according to application needs, quality of service requirements, and economic restrictions. To balance the trade-off between technical and economic requirements, a multi-band heterogeneous wireless network architecture is needed. Numerous wireless technologies including Wi-Fi, Zigbee, and Bluetooth share the 2.4 GHz industrial, scientific, and medical band. However, due to different channel access mechanisms and transmit power levels, and very importantly, uncoordinated use, coexistence of these devices in the same vicinity can cause interference and degradation in performance. This report provides the technical basis for understanding the coexistence of these wireless technologies through an experimental evaluation of their performance. This report investigates interactions encompassing variables such as transmission power level, distance between the devices, data rates, and the utilization of co-channel or adjacent channels. The results show that the operation of both Zigbee and Bluetooth is severely compromised when coexisting with Wi-Fi within the same frequency spectrum. On the other hand, the performance of Bluetooth is not impaired by Zigbee and vice versa unless there exists any external interference from Wi-Fi.

Continual Reliability ImprovementPacifiCorp has continually and steadily improved its service reliability to customers as measured by SAIDI and SAIFI.This presentation will detail the programs and practices put in place to achieve continual improvement, as well as discuss ideas for future improvement.Jake Barker serves as director of distribution engineering and area transmission planning for PacifiCorp.He also has responsibility for customer generation engineering and power quality engineering.His responsibilities include ensuring PacifiCorp's distribution and sub-transmission grid provides adequate capacity to serve customers reliably, customers' power quality is within standards and customer generation installations comply with company interconnection policy.Previous to his current role, Barker worked in asset management developing the 10 year capital plan for major projects as well as managing customer engineering services and smart grid.