Browsing by Subject "Cognitive radio networks"
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Item Open Access The biobjective multiarmed bandit: learning approximate lexicographic optimal allocations(TÜBİTAK, 2019) Tekin, CemWe consider a biobjective sequential decision-making problem where an allocation (arm) is called ϵ lexicographic optimal if its expected reward in the first objective is at most ϵ smaller than the highest expected reward, and its expected reward in the second objective is at least the expected reward of a lexicographic optimal arm. The goal of the learner is to select arms that are ϵ lexicographic optimal as much as possible without knowing the arm reward distributions beforehand. For this problem, we first show that the learner’s goal is equivalent to minimizing the ϵ lexicographic regret, and then, propose a learning algorithm whose ϵ lexicographic gap-dependent regret is bounded and gap-independent regret is sublinear in the number of rounds with high probability. Then, we apply the proposed model and algorithm for dynamic rate and channel selection in a cognitive radio network with imperfect channel sensing. Our results show that the proposed algorithm is able to learn the approximate lexicographic optimal rate–channel pair that simultaneously minimizes the primary user interference and maximizes the secondary user throughput.Item Open Access Contextual multi-armed bandits with structured payoffs(Bilkent University, 2020-09) Qureshi, Muhammad AnjumMulti-Armed Bandit (MAB) problems model sequential decision making under uncertainty. In traditional MAB, the learner selects an arm in each round, and then, observes a random reward from the arm’s unknown reward distribution. In the end, the goal is to maximize the cumulative reward by learning to select optimal arms as much as possible. In the contextual MAB—an extension to MAB—the learner observes a context (side-information) in the beginning of each round, selects an arm, and then, observes a random reward whose distribution depends on both the arriving context and the chosen arm. Another MAB variant, called unimodal MAB, assumes that the expected reward exhibits a unimodal structure over the arms, and tries to locate the arm with the “peak” reward by learning the direction of increase of the expected reward. In this thesis, we consider an extension to unimodal MAB called contextual unimodal MAB, and demonstrate that it is a powerful tool for designing Artificial Intelligence (AI)- enabled radios by utilizing the special structure of the dependence of the reward to contexts and arms of the wireless environment. While AI-enabled radios are expected to enhance the spectral efficiency of 5th generation (5G) millimeter wave (mmWave) networks by learning to optimize network resources, allocating resources over the mmWave band is extremely challenging due to rapidly-varying channel conditions. We consider several resource allocation problems in this thesis under various design possibilities for mmWave radio networks under unknown channel statistics and without any channel state information (CSI) feedback: i) dynamic rate selection for an energy harvesting transmitter, ii) dynamic power allocation for heterogeneous applications, and iii) distributed resource allocation in a multi-user network. All of these problems exhibit structured payoffs which are unimodal functions over partially ordered arms (transmission parameters) as well as unimodal or monotone functions over partially ordered contexts (side-information). Structure over arms helps in reducing the number of arms to be explored, while structure over contexts helps in using past information from nearby contexts to make better selections. We formalize dynamic adaptation of transmission parameters as a structured MAB, and propose frequentist and Bayesian online learning algorithms. We show that both approaches yield logarithmic in time regret. We also investigate dynamic rate and channel adaptation in a cognitive radio network serving heterogeneous applications under dynamically varying channel availability and rate constraints. We formalize the problem as a Bayesian learning problem, and propose a novel learning algorithm which considers each rate-channel pair as a two-dimensional action. The set of available actions varies dynamically over time due to variations in primary user activity and rate requirements of the applications served by the users. Additionally, we extend the work to cater to thescenario when the arms belong to a continuous interval as well as the contexts. Finally, we show via simulations that our algorithms significantly improve the performance in the aforementioned radio resource allocation problems.Item Open Access Online Bayesian learning for rate selection in millimeter wave cognitive radio networks(Institute of Electrical and Electronics Engineers, 2020) Qureshi, Muhammad Anjum; Tekin, CemWe consider the problem of dynamic rate selection in a cognitive radio network (CRN) over the millimeter wave (mmWave) spectrum. Specifically, we focus on the scenario when the transmit power is time varying as motivated by the following applications: i) an energy harvesting CRN, in which the system solely relies on the harvested energy source, and ii) an underlay CRN, in which a secondary user (SU) restricts its transmission power based on a dynamically changing interference temperature limit (ITL) such that the primary user (PU) remains unharmed. Since the channel quality fluctuates very rapidly in mmWave networks and costly channel state information (CSI) is not that useful, we consider rate adaptation over an mmWave channel as an online stochastic optimization problem, and propose a Thompson Sampling (TS) based Bayesian method. Our method utilizes the unimodality and monotonicity of the throughput with respect to rates and transmit powers and achieves logarithmic in time regret with a leading term that is independent of the number of available rates. Our regret bound holds for any sequence of transmits powers and captures the dependence of the regret on the arrival pattern. We also show via simulations that the performance of the proposed algorithm is superior than the stateof-the-art algorithms, especially when the arrivals are favorable.Item Open Access Rate and channel adaptation in cognitive radio networks under time-varying constraints(IEEE, 2020) Qureshi, Muhammad Anjum; Tekin, CemWe consider dynamic rate and channel adaptation in a cognitive radio network serving heterogeneous applications under dynamically varying channel availability and rate constraint. We formalize it as a Bayesian learning problem, and propose a novel learning algorithm, called Volatile Constrained Thompson Sampling (V-CoTS), which considers each rate-channel pair as a two-dimensional action. The set of available actions varies dynamically over time due to variations in primary user activity and rate requirements of the applications served by the users. Our algorithm learns to adapt its rate and opportunistically exploit spectrum holes when the channel conditions are unknown and channel state information is absent, by using acknowledgment only feedback. It uses the monotonicity of the transmission success probability in the transmission rate to optimally tradeoff exploration and exploitation of the actions. Numerical results demonstrate that V-CoTS achieves significant gains in throughput compared to the state-of-the-art methods.