Joint Data Routing and Power Scheduling for Wireless Powered Communication Networks

Movahednasab, Mohammad; Omidvar, Naeimeh; Pakravan, Mohammad Reza; Svensson, Tommy

doi:10.1109/icc.2019.8761174

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…It will be shown that this vector eventually converges to the true gradient. Using this gradient estimation and the latest iterate (i.e., x k−1 l ), agent l then constructs a quadratic deterministic surrogate function, as in (5). The surrogate function is then minimised by agent l to update the control variable x k l .…”

Section: A Algorithm Descriptionmentioning

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: A Algorithm Descriptionmentioning

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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show abstract

“…The latter technique covers longer transmission ranges, requires a simpler structure for the receivers, and also better supports multiple receivers than the former technique [9]. Considering these advantages and following many previous works (e.g., see [10]- [15]), throughout this paper, we focus on RF-based WPT (RF-WPT).…”

Section: Introductionmentioning

confidence: 99%

Efficient, Fair and QoS-Aware Policies for Wirelessly Powered Communication Networks

Rezaei¹,

Omidvar²,

Movahednasab³

et al. 2019

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Wireless power transfer (WPT) is a viable source of energy for wirelessly powered communication networks (WPCNs). In this paper, we first consider WPT from an energy access point (E-AP) to multiple energy receivers (E-Rs) to obtain the optimal policy that maximizes the WPT efficiency. For this purpose, we formulate the problem of maximizing the total average received power of the E-Rs subject to the average and peak power level constraints of the E-AP. The formulated problem is a non-convex stochastic optimization problem. Using some stochastic optimization techniques, we tackle the challenges of this problem and derive a closed-form expression for the optimal solution, which requires the explicit knowledge of the distribution of channel state information (CSI) in the network. We then propose a near-optimal algorithm that does not require any explicit knowledge of the CSI distribution and prove that the proposed algorithm attains a near-optimal solution within a guaranteed gap to the optimal solution. We next consider fairness among the E-Rs and propose a quality of service (QoS) aware fair policy that maximizes a generic network utility function while guaranteeing the required QoS of each E-R. Finally, we study a practical wirelessly powered communication scenario in which the E-Rs utilize their energy harvested through WPT to transmit information to the E-AP. We optimize the received information at the E-AP under its average and peak transmission power constraints and the fairness constraints of the E-Rs. Numerical results show the significant performance of our proposed solutions compared to the state-of-the-art baselines.

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

Movahednasab

Pakravan

Makki

et al. 2021

IEEE Open J. Commun. Soc.

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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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Joint Data Routing and Power Scheduling for Wireless Powered Communication Networks

Cited by 3 publications

References 35 publications

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

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

Efficient, Fair and QoS-Aware Policies for Wirelessly Powered Communication Networks

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

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