Browsing International Telemetering Conference Proceedings, Volume 55 (2019) by Authors
CLASSIFICATION STYLE REGRESSION FOR SPECTRAL OPENING PMF ESTIMATIONFosdick, Garrett; Marefat, Michael; Bose, Tamal; Univ Arizona, Dept Electrical and Computer Engineering (International Foundation for Telemetering, 2019-10)Dynamic spectrum allocation (DSA) permits unlicensed users to access spectrum owned by a licensed user given they do so without interference to the primary user. To avoid interference with other users, the unlicensed user needs to be aware of channel availability. Spectrum sensing allows a radio to find spectrum holes, but costs energy and time. Predictive methods can be used to decrease the amount of spectrum sensing needed to find an available channel. We designed a novel neural network architecture for spectrum hole prediction. This neural network is capable of creating probability mass functions (PMF) estimates of the length of channel openings with no assumptions of the initial probability distribution or prior knowledge about the traffic. This architecture is shown to work through a mathematical proof, and its performance is measured through simulation.
REINFORCEMENT LEARNING FOR HYBRID BEAMFORMING IN MILLIMETER WAVE SYSTEMSPeken, Ture; Tandon, Ravi; Bose, Tamal; Univ Arizona, Dept Electrical and Computer Engineering (International Foundation for Telemetering, 2019-10)The use of millimeter waves (mmWave) for next-generation cellular systems is promising due to the large bandwidth available in this band. Beamforming will likely be divided into RF and baseband domains, which is called hybrid beamforming. Precoders can be designed by using a predefined codebook or by choosing beamforming vectors arbitrarily in hybrid beamforming. The computational complexity of finding optimal precoders grows exponentially with the number of RF chains. In this paper, we develop a Q-learning (a form of reinforcement learning) based algorithm to find the precoders jointly. We analyze the complexity of the algorithm as a function of the number of iterations used in the training phase. We compare the spectral efficiency achieved with unconstrained precoding, exhaustive search, and another state-of-art algorithm. Results show that our algorithm provides better spectral efficiency than the state-of-art algorithm and has performance close to that of exhaustive search.
The Good, The Bad, and The Non-Circular SignalsBose, Tamal; Tsang, Stephanie D.; Samuel, Al; Univ Arizona, Dept Electrical and Computer Engineering (International Foundation for Telemetering, 2019-10)Second-order (SO) non-circularity is a statistical property that is used to classify signals. Signals with SO non-circularity are extensively used in communication and radar systems. The SO non-circularity property is generally useful in the application of array processing techniques for extending antenna apertures. Exploiting this non-circularity property for a multi-faceted set of communication-type and radar-type signals is the objective of this study. For a given type of signal, the circularity quotient and its properties are tested and evaluated in terms of parameters such as the modulus of its phase, complex covariance, pseudo-variance, the angle orientation of the ellipse, its eccentricity, and other relevant properties are calculated. A geometrical interpretation for the circularity quotient and the correlation coeficient is used to derive the bounds for circularity.