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1. Optimal Wireless Scheduling for Remote Sensing through Brownian Approximation

   https://doi.org/10.1109/INFOCOM42981.2021.9488785  

   Guo, Daojing; Hsieh, Ping-Chun; Hou, I-Hong (May 2021, IEEE Infocom 2021)

   null (Ed.)

   This paper studies a remote sensing system where multiple wireless sensors generate possibly noisy information updates of various surveillance fields and delivering these updates to a control center over a wireless network. The control center needs a sufficient number of recently generated information updates to have an accurate estimate of the current system status, which is critical for the control center to make appropriate control decisions. The goal of this work is then to design the optimal policy for scheduling the transmissions of information updates. Through Brownian approximation, we demonstrate that the control center’s ability to make accurate real-time estimates depends on the averages and temporal variances of the delivery processes. We then formulate a constrained optimization problem to find the optimal means and variances. We also develop a simple online scheduling policy that employs the optimal means and variances to achieve the optimal system-wide performance. Simulation results show that our scheduling policy enjoys fast convergence speed and better performance when compared to other state-of-the-art policies. 

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2. Rate-Optimal Budget Allocation for the Probability of Good Selection

   https://doi.org/10.1109/WSC63780.2024.10838732  

   Kim, Taeho; Eckman, David J (December 2024, IEEE)

   Lam, H; Azar, E; Batur, D; Gao, S; Xie, W; Hunter, S R; Rossetti, M D (Ed.)

   This paper studies the allocation of simulation effort in a ranking-and-selection (R&S) problem with the goal of selecting a system whose performance is within a given tolerance of the best. We apply large-deviations theory to derive an optimal allocation for maximizing the rate at which the so-called probability of good selection (PGS) asymptotically approaches one, assuming that systems’ output distributions are known. An interesting property of the optimal allocation is that some good systems may receive a sampling ratio of zero. We demonstrate through numerical experiments that this property leads to serious practical consequences, specifically when designing adaptive R&S algorithms. In particular, we observe that the convergence and even consistency of a simple plug-in algorithm designed for the PGS goal can be negatively impacted. We offer empirical evidence of these challenges and a preliminary exploration of a potential correction. 

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3. Bonferroni-Free and Indifference-Zone-Flexible Sequential Elimination Procedures for Ranking and Selection

   https://doi.org/10.1287/opre.2023.2447  

   Wang, Wenyu; Wan, Hong; Chen, Xi (January 2023, Operations Research)

   This paper proposes two fully sequential procedures for selecting the best system with a guaranteed probability of correct selection (PCS). The main features of the proposed procedures include the following: (1) adopting a Bonferroni-free model that overcomes the conservativeness of the Bonferroni correction and delivers the exact probabilistic guarantee without overshooting; (2) conducting always valid and fully sequential hypothesis tests that enable continuous monitoring of each candidate system and control the type I error rate (or equivalently, PCS) at a prescribed level; and (3) assuming an indifference-zone-flexible formulation, which means that the indifference-zone parameter is not indispensable but could be helpful if provided. We establish statistical validity and asymptotic efficiency for the proposed procedures under normality settings with and without the knowledge of true variances. Numerical studies conducted under various configurations corroborate the theoretical findings and demonstrate the superiority of the proposed procedures. Funding: W. Wang and H. Wan were supported in part by CollinStar Capital Pty Ltd. X. Chen was supported in part by the National Science Foundation [Grant IIS-1849300 and CAREER CMMI-1846663]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2447 . 

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4. Stochastic Resource Allocation via Dual Tail Waterfilling

   https://doi.org/10.1109/CISS59072.2024.10480205  

   Yaylali, Gokberk; Kalogerias, Dionysis (March 2024, IEEE)

   Optimal resource allocation in wireless systems still stands as a rather challenging task due to the inherent statistical characteristics of channel fading. On the one hand, minimax/outage-optimal policies are often overconservative and analytically intractable, despite advertising maximally reliable system performance. On the other hand, ergodic-optimal resource allocation policies are often susceptible to the statistical dispersion of heavy-tailed fading channels, leading to relatively frequent drastic performance drops. We investigate a new risk-aware formulation of the classical stochastic resource allocation problem for point-to-point power-constrained communication networks over fading channels with no cross-interference, by leveraging the Conditional Value-at-Risk (CV@R) as a coherent measure of risk. We rigorously derive closed-form expressions for the CV@R-optimal risk-aware resource allocation policy, as well as the optimal associated quantiles of the corresponding user rate functions by capitalizing on the underlying fading distribution, parameterized by dual variables. We then develop a purely dual tail waterfilling scheme, achieving significantly more rapid and assured convergence of dual variables, as compared with the primal-dual tail waterfilling algorithm, recently proposed in the literature. The effectiveness of the proposed scheme is also readily confirmed via detailed numerical simulations. 

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5. Online Allocation and Pricing: Constant Regret via Bellman Inequalities

   https://doi.org/10.1287/opre.2020.2061  

   Vera, Alberto; Banerjee, Siddhartha; Gurvich, Itai (July 2021, Operations Research)

   null (Ed.)

   We develop a framework for designing simple and efficient policies for a family of online allocation and pricing problems that includes online packing, budget-constrained probing, dynamic pricing, and online contextual bandits with knapsacks. In each case, we evaluate the performance of our policies in terms of their regret (i.e., additive gap) relative to an offline controller that is endowed with more information than the online controller. Our framework is based on Bellman inequalities, which decompose the loss of an algorithm into two distinct sources of error: (1) arising from computational tractability issues, and (2) arising from estimation/prediction of random trajectories. Balancing these errors guides the choice of benchmarks, and leads to policies that are both tractable and have strong performance guarantees. In particular, in all our examples, we demonstrate constant-regret policies that only require resolving a linear program in each period, followed by a simple greedy action-selection rule; thus, our policies are practical as well as provably near optimal. 

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