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Item Open Access Adaptive measurement matrix design in compressed sensing based direction of arrival estimation(IEEE, 2021) Kılıç, Berkan; Güngör, Alper; Kalfa, Mert; Arıkan, OrhanDesign of measurement matrices is an important aspect of compressed sensing (CS) based direction of arrival (DoA) applications that enables reduction in the analog channels to be processed in sparse target environments. Here, a novel measurement matrix design methodology for CS based DoA estimation is proposed and its superior performance over alternative measurement matrix design methodologies is demonstrated. The proposed method uses prior probability distribution of the targets to improve performance. Compared to the state-of-the-art techniques, it is quantitatively demonstrated that the proposed measurement matrix design approach enables significant reduction in the number of analog channels to be processed and adapts to a priori information on the target scene.Item Open Access Combinatorial Gaussian process bandits with probabilistically triggered arms(Microtome Publishing, 2021) Demirel, İlker; Tekin, CemCombinatorial bandit models and algorithms are used in many sequential decision-making tasks ranging from item list recommendation to influence maximization. Typical algorithms proposed for combinatorial bandits, including combinatorial UCB (CUCB) and combinatorial Thompson sampling (CTS) do not exploit correlations between base arms during the learning process. Moreover, their regret is usually analyzed under independent base arm outcomes. In this paper, we use Gaussian Processes (GPs) to model correlations between base arms. In particular, we consider a combinatorial bandit model with probabilistically triggered arms, and assume that the expected base arm outcome function is a sample from a GP. We assume that the learner has access to an exact computation oracle, which returns an optimal solution given expected base arm outcomes, and analyze the regret of Combinatorial Gaussian Process Upper Confidence Bound (ComGP-UCB) algorithm for this setting. Under (triggering probability modulated) Lipschitz continuity assumption on the expected reward function, we derive (O( \sqrt{m T \log T \gamma_{T, \boldsymbol{\mu}}^{PTA}})) O(m \sqrt{\frac{T \log T}{p^*}}) upper bounds for the regret of ComGP-UCB that hold with high probability, where m denotes the number of base arms, p^* denotes the minimum non-zero triggering probability, and \gamma_{T, \boldsymbol{\mu}}^{PTA} denotes the pseudo-information gain. Finally, we show via simulations that when the correlations between base arm outcomes are strong, ComGP-UCB significantly outperforms CUCB and CTS.