Stochastic Power Control Policies for Battery-Operated Wireless Power Transfer

Rezaei, Roohollah; Movahednasab, Mohammad; Omidvar, Naeimeh; Pakravan, Mohammad Reza

doi:10.1109/pimrc.2018.8580910

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…Since according to Assumption 1-b, ∇ l f i (x) , ∀i are Lipschitz continuous over the domain X , it follows that there exists some 0 ≤ G ≤ ∞ such that ∇ l f i (x) , ∀i, ∀x ∈ X . Therefore, using (11), we have…”

Section: Discussionmentioning

confidence: 99%

Parallel Stochastic Optimization Framework for Large-Scale Non-Convex Stochastic Problems

Omidvar,

Liu,

Lau

et al. 2019

Preprint

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In this paper, we consider the problem of stochastic optimization, where the objective function is in terms of the expectation of a (possibly non-convex) cost function that is parametrized by a random variable. While the convergence speed is critical for many emerging applications, most existing stochastic optimization methods suffer from slow convergence. Furthermore, the emerging technology of parallel computing has motivated an increasing demand for designing new stochastic optimization schemes that can handle parallel optimization for implementation in distributed systems.We propose a fast parallel stochastic optimization framework that can solve a large class of possibly non-convex stochastic optimization problems that may arise in applications with multi-agent systems. In the proposed method, each agent updates its control variable in parallel, by solving a convex quadratic subproblem independently. The convergence of the proposed method to the optimal solution for convex problems and to a stationary point for general non-convex problems is established.The proposed algorithm can be applied to solve a large class of optimization problems arising in important applications from various fields, such as machine learning and wireless networks. As a representative application of our proposed stochastic optimization framework, we focus on large-scale support vector machines and demonstrate how our algorithm can efficiently solve this problem, especially in modern applications with huge datasets. Using popular real-world datasets, we present experimental results to demonstrate the merits of our proposed framework by comparing its performance to the state-of-the-art in the literature. Numerical results show that the proposed method can significantly outperform the state-of-the-art methods in terms of the convergence speed while having the same or lower complexity and storage requirement.

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Section: Discussionmentioning

confidence: 99%

Parallel Stochastic Optimization Framework for Large-Scale Non-Convex Stochastic Problems

Omidvar,

Liu,

Lau

et al. 2019

Preprint

Self Cite

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“…Finally, (14) in Lemma 1 is proved if we subtract V E U(t)|Q(t) from both sides of (47). 16), we always have D n (t) = 0 if Q n (t) ≥ V .…”

Section: Appendix a Proof Of Lemmamentioning

confidence: 97%

On Energy Allocation and Data Scheduling in Backscatter Networks with Multi-antenna Readers

Movahednasab¹,

Pakravan²,

Makki³

et al. 2021

Preprint

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In this paper, we study the throughput utility functions in buffer-equipped monostatic backscatter communication networks with multi-antenna Readers. In the considered model, the backscatter nodes (BNs) store the data in their buffers before transmission to the Reader. We investigate three utility functions, namely, the sum, the proportional and the common throughput. We design online admission policies, corresponding to each utility function, to determine how much data can be admitted in the buffers. Moreover, we propose an online data link control policy for jointly controlling the transmit and receive beamforming vectors as well as the reflection coefficients of the BNs. The proposed policies for data admission and data link control jointly optimize the throughput utility, while stabilizing the buffers.We adopt the min-drift-plus-penalty (MDPP) method in designing the control policies. Following the MDPP method, we cast the optimal data link control and the data admission policies as solutions of two independent optimization problems which should be solved in each time slot. The optimization problem corresponding to the data link control is non-convex and does not have a trivial solution. Using Lagrangian dual and quadratic transforms, we find a closed-form iterative solution. Finally, we use the results on the achievable rates of finite blocklength codes to study the system performance in the cases with short packets. As demonstrated, the proposed policies achieve optimal utility and stabilize the data buffers in the BNs.

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“…Then, (49) implies that ∆ 1 u ≥ ∆ 0 u , and hence, a data admission vector with D l > 0 is not optimal. Accordingly, we conclude that Q n (t) will never exceed V + D max .…”

Section: Appendix a Proof Of Lemmamentioning

confidence: 99%

On Energy Allocation and Data Scheduling in Backscatter Networks With Multi-Antenna Readers

Movahednasab

Pakravan

Makki

et al. 2021

IEEE Open J. Commun. Soc.

Self Cite

View full text Add to dashboard Cite

In this paper, we study the throughput utility functions in buffer-equipped monostatic backscatter communication networks with multi-antenna Readers. In the considered model, the backscatter nodes (BNs) store the data in their buffers before transmission to the Reader. We investigate three utility functions, namely, the sum, the proportional and the common throughput. We design online admission policies, corresponding to each utility function, to determine how much data can be admitted in the buffers. Moreover, we propose an online data link control policy for jointly controlling the transmit and receive beamforming vectors as well as the reflection coefficients of the BNs. The proposed policies for data admission and data link control jointly optimize the throughput utility, while stabilizing the buffers. We adopt the min-drift-plus-penalty (MDPP) method in designing the control policies. Following the MDPP method, we cast the optimal data link control and the data admission policies as solutions of two independent optimization problems which should be solved in each time slot. The optimization problem corresponding to the data link control is non-convex and does not have a trivial solution. Using Lagrangian dual and quadratic transforms, we find a closed-form iterative solution. Finally, we use the results on the achievable rates of finite blocklength codes to study the system performance in the cases with short packets. As demonstrated, the proposed policies achieve optimal utility and stabilize the data buffers in the BNs.

show abstract

Stochastic Power Control Policies for Battery-Operated Wireless Power Transfer

Cited by 8 publications

References 13 publications

Parallel Stochastic Optimization Framework for Large-Scale Non-Convex Stochastic Problems

Parallel Stochastic Optimization Framework for Large-Scale Non-Convex Stochastic Problems

On Energy Allocation and Data Scheduling in Backscatter Networks with Multi-antenna Readers

On Energy Allocation and Data Scheduling in Backscatter Networks With Multi-Antenna Readers

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