Our hunger for data has grown tremendously over the years which has led to a demand for the increase in the available radio spectrum for communications. The Federal Communications Commission in the United States allowed for the sharing of the CBRS band (3550-3700 MHz) a few years ago. Since then, research has been done by both industry and academia to identify similar opportunities in other radio bands as well. This research is, however, being hampered due to a lack of experimental frameworks where the various aspects of spectrum sharing can be studied. To this end, we propose to develop an open-source spectrum access system that incorporates context awareness and multi-band operational support and serves as an RandD tool for the research community. We have developed a novel Prioritization Framework that takes the current operational context of each user into account to determine their relative priority, within or outside their user class/group, for transmission in the network. We also introduce a Policy Engine for the configuration and management of dynamic policies (or rules) for defining the relationships between the various forms of context information and their relative impact on a user's overall priority. We have performed several experiments to show how context awareness impacts the spectrum sharing efficiency and quality of service. Due to its modular and extensible nature, we expect that this tool will be used by researchers and policy-makers to implement their own policies and algorithms and test their efficacy in a simulated radio environment. / Master of Science / Over the years, the advancements in the internet and communication technology have made us more and more data-hungry. Consequently, the electromagnetic spectrum on which data is transmitted has become a sparse resource. Governments worldwide are working together with academia and industry to find the most efficient utilization of this resource. If the current users of protected spectrum could share their bands with other licensed or opportunistic users, then a tremendous amount of spectrum could be freed up for public and private use. To facilitate rapid research and development in this field, this thesis proposes the development of an open-source, modular, and extensible Context-Aware Dynamic Spectrum Access System. In this system, we explore the usage of several traditional and novel context information in spectrum allocation, which in turn helps us improve the efficiency and resiliency of spectrum management while ensuring that incumbent users are not adversely affected by other licensed or unlicensed users. We develop cognitive modules for context-based prioritization of users for allocation through a novel Prioritization Framework and for enabling the use of dynamic policies or rules (governing spectrum allocation) instead of static policies that most systems use today. We simulate several operational scenarios and depict our tool's performance in them. Through our experiments and discussions, we highlight the significance of this tool for researchers, policy-makers, and regulators for studying spectrum sharing in general, and context-aware, dynamic policy-based spectrum sharing in particular.

In 2015, Federal Communications Commission (FCC) enabled shared commercial use of the 3.550-3.700 GHz band. A framework was developed to enable this spectrum-sharing capa- bility which included an automated frequency coordinator called Spectrum Access System (SAS). This work extends the open source SAS based on the aforementioned FCC SAS framework developed by researchers at Virginia Tech Wireless group, with real-time envi- ronment sensing capability along with intelligent incumbent detection using Software-defined Radios (SDRs) and a real-time graphical user interface. This extended version is called the OpenSAS. Furthermore, the SAS client and OpenSAS are extended to be compliant with the Wireless Innovation Forum (WINNF) specifications by testing the SAS-CBRS Base Station Device (CBSD) interface with the Google SAS Test Environment. The Environment Sensing Capability (ESC) functionality is evaluated and tested in our xG Testbed to verify its ability to detect the presence of users in the CBRS band. An ML-based feedforward neural net- work model is employed and trained using simulated radar waveforms as incumbent signals and captured 5G New Radio (NR) signals as a non-incumbent signal to predict whether the detected user is a radar incumbent or an unknown user. If the presence of incumbent radar is detected with an 85% or above certainty, incumbent protection is activated, terminating CBSD grants causing damaging interference to the detected incumbent. A 5G NR signal is used as a non-incumbent user and added to the training dataset to better the ability of the model to reject non-incumbent signals. The model achieves a maximum validation accuracy of 95.83% for signals in the 40-50 dB Signal-to-Noise Ratio (SNR) range. It achieves an 85.35% accuracy for Over the air (OTA) real-time tests. The non-incumbent 5G NR signal rejection accuracy is 91.30% for a calculated SNR range of 10-20 dB. In conclusion, this work advances state of the art in spectrum sharing systems by presenting an enhanced open source SAS and evaluating the newly added functionalities. / Master of Science / In 2015, Federal Communications Commission (FCC) enabled shared commercial use of the 3.550-3.700 GHz band. A framework was developed to enable this spectrum-sharing capability which included an automated frequency coordinator called Spectrum Access System (SAS). The task of the SAS is to make sure no two users use the same spectrum in the same location causing damaging interference to each other. The SAS is also responsible for prioritizing the higher tier users and protecting them from interference from lower tier users. This work extends the open source SAS based on the aforementioned FCC SAS framework developed by researchers at Virginia Tech Wireless group, with real-time environment sensing capability along with intelligent incumbent detection using Software-defined Radios (SDRs) and a real-time graphical user interface. This extended version is called the OpenSAS. Furthermore, the SAS client and OpenSAS are extended to be compliant with the Wireless Innovation Forum (WINNF) specifications by testing the SAS-CBRS Base Station Device (CBSD) interface with the Google SAS Test Environment. The Environment Sensing Capability (ESC) functionality is evaluated and tested in our xG Testbed to verify its ability to detect the presence of users in the CBRS band. The ESC is used to detect incumbent users (the highest tier) that do not inform the SAS about their use of the spectrum. An ML-based feedforward neural net- work model is employed and trained using simulated radar waveforms as incumbent signals and captured 5G New Radio (NR) signals as a non-incumbent signal to predict whether the detected user is a radar incumbent or an unknown user. If the presence of incumbent radar is detected with an 85% or above certainty, incumbent protection is activated, terminating CBSD grants causing damaging interference to the detected incumbent. A 5G NR signal is used as a non-incumbent user and added to the training dataset to better the ability of the model to reject non-incumbent signals. The model achieves a maximum validation accuracy of 95.83% for signals in the 40-50 dB Signal to-Noise Ratio (SNR) range. It achieves an 85.35% accuracy for Over the air (OTA) real-time tests. The non-incumbent 5G NR signal rejection accuracy is 91.30% for a calculated SNR range of 10-20 dB. In conclusion, this work advances state of the art in spectrum sharing systems by presenting an enhanced open source SAS and evaluating the newly added functionalities.