Parisa Mansourifard

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We propose a new scheme for increasing the throughput of video files in cellular communications systems. This scheme exploits (i) the redundancy of user requests as well as (ii) the considerable storage capacity of smartphones and tablets. Users cache popular video files and-after receiving requests from other users-serve these requests via device-to-device(More)
—Cellular networks can be operated more energy-efficiently if operators agree to share base-stations during off-peak hours. We apply a micro-economic analysis for a single-cell two-operator scenario to investigate the conditions under which self-interested operators would agree to share resources in this manner. Our analysis yields a comprehensive treatment(More)
—In this paper, we propose a novel cognitive cooperative relaying scheme using multi-carrier transmission in which cognitive users assist primary users by relaying their information using Decode and Forward (DF) strategy. The best cognitive user is selected as a relay for a Primary User (PU). Outage probability of the PU is investigated. Our analyses show(More)
—We study the problem of power allocation over two identical Gilbert-Elliot communication channels. Our goal is to maximize the expected discounted number of bits transmitted over an infinite time horizon. This is achieved by choosing among three possible strategies: (1) betting on channel 1 by allocating all the power to this channel, which results in high(More)
— We formulate a general class of restless multi-armed bandits with n independent and stochastically identical arms. Each arm is in a real-valued state s ∈ [s0, smax]. Selecting an arm with state s yields an immediate reward with expectation R(s). The state of the arm that is selected stochastically jumps from its current value s to either smax or s0 with(More)
We consider the problem of automatically learning the optimal thermal control in a room in order to maximize the expected average satisfaction among occupants providing stochastic feedback on their comfort through a participatory sensing application. Not assuming any prior knowledge or modeling of user comfort, we first apply the classic UCB1 online(More)
— We study a state-tracking problem in which the background random process is Markovian with unknown real-valued states and known transition probability densities. At each time step the decision-maker chooses a state as an action and accumulates some reward based on the selected state and the actual state. If the selected state is higher than the actual(More)
—We formulate a Bayesian congestion control problem in which a source must select the transmission rate over a network whose available bandwidth is modeled as a time-homogeneous finite-state Markov Chain. The decision to transmit at a rate below the instantaneous available bandwidth results in an under-utilization of the resource while transmission at rates(More)
We study the state-tracking of a Markovian random process with a known transition matrix and a finite ordered state set. The decision-maker must select a state as an action at each time step in order to minimize the total expected (discounted) cost. The decision-maker is faced with asymmetries both in cost and observation: in case the selected state is less(More)
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