Browsing by Author "Martone, Anthony F."

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    On the Value of Online Learning for Cognitive Radar Waveform Selection
    Thornton III, Charles Ethridge (Virginia Tech, 2023-05-16)
    Modern radar systems must operate in a wide variety of time-varying conditions. These include various types of interference from neighboring systems, self-interference or clutter, and targets with fluctuating responses. It has been well-established that the quality and nature of radar measurements depend heavily on the choice of signal transmitted by the radar. In this dissertation, we discuss techniques which may be used to adapt the radar's waveform on-the-fly while making very few a priori assumptions about the physical environment. By employing tools from reinforcement learning and online learning, we present a variety of algorithms which handle practical issues of the waveform selection problem that have been left open by previous works. In general, we focus on two key challenges inherent to the waveform selection problem, sample-efficiency and universality. Sample-efficiency corresponds to the number of experiences a learning algorithm requires to achieve desirable performance. Universality refers to the learning algorithm's ability to achieve desirable performance across a wide range of physical environments. Specifically, we develop a contextual bandit-based approach to vastly improve the sample-efficiency of learning compared to previous works. We then improve the generalization performance of this model by developing a Bayesian meta-learning technique. To handle the problem of universality, we develop a learning algorithm which is asymptotically optimal in any Markov environment having finite memory length. Finally, we compare the performance of learning-based waveform selection to fixed rule-based waveform selection strategies for the scenarios of dynamic spectrum access and multiple-target tracking. We draw conclusions as to when learning-based approaches are expected to significantly outperform rule-based strategies, as well as the converse.
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    A Stochastic Optimization Approach for Spectrum Sharing of Radar and LTE Systems
    Labib, Mina; Martone, Anthony F.; Marojevic, Vuk; Reed, Jeffrey H.; Zaghloul, Amir I. (IEEE, 2019)
    This paper proposes a chance-constrained stochastic optimization technique that enables effective coexistence between LTE-Unlicensed base stations and radars in a shared spectrum. The optimization problem is formulated to guarantee the minimum performance criteria for radar operation, and at the same time, allows the LTE-Unlicensed base station to control its transmit power to maximize the performance for the serving LTE-Unlicensed device. The proposed power control mechanism results in significant reduction of the required protection distance (3.9% of the one imposed by regulations) between the radar and the LTE-Unlicensed network for the two to effectively coexist in a shared spectrum.