Data Rate Maximization in RIS-Assisted D2D Communication with Transceiver Hardware Impairments

We maximize the transmit rate of device-to-device (D2D) in a reconfigurable intelligent surface (RIS) assisted D2D communication system by satisfying the unit-modulus constraints of reflectin elements, the transmit power limit of base station (BS) and the transmitter in a D2D pair. Since it is a non-convex optimization problem, the block coordinate descent (BCD) technique is adopted to decouple this problem into three subproblems. Then, the non-convex subproblems are approximated into convex problems by using successive convex approximation (SCA) and penalty convex-concave procedure (CCP) techniques. Finally, the optimal solution of original problem is obtained by iteratively optimizing the subproblems. Simulation results reveal the validity of the algorithm that we proposed to solve the optimization problem and illustrate the effectiveness of RIS to improve the transmit rate of the D2D pair even with hardware impairments.

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Robust Beamforming Design for Secure V2X Downlink System with Wireless Information and Power Transfer under a Nonlinear Energy Harvesting Model

Sensors ◽

10.3390/s18103294 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3294 ◽

Cited By ~ 5

Author(s):

Shidang Li ◽

Chunguo Li ◽

Weiqiang Tan ◽

Baofeng Ji ◽

Luxi Yang

Keyword(s):

Energy Harvesting ◽

Traffic Safety ◽

Optimization Problem ◽

Optimal Solution ◽

Base Station ◽

Power Splitting ◽

Power Transfer ◽

Vehicular Communication ◽

Downlink System ◽

Nonlinear Energy

Vehicle to everything (V2X) has been deemed a promising technology due to its potential to achieve traffic safety and efficiency. This paper considers a V2X downlink system with a simultaneous wireless information and power transfer (SWIPT) system where the base station not only conveys data and energy to two types of wireless vehicular receivers, such as one hybrid power-splitting vehicular receiver, and multiple energy vehicular receivers, but also prevents information from being intercepted by the potential eavesdroppers (idle energy vehicular receivers). Both the base station and the energy vehicular receivers are equipped with multiple antennas, whereas the information vehicular receiver is equipped with a single antenna. In particular, the imperfect channel state information (CSI) and the practical nonlinear energy harvesting (EH) model are taken into account. The non-convex optimization problem is formulated to maximize the minimum harvested energy power among the energy vehicular receivers satisfying the lowest harvested energy power threshold at the information vehicular receiver and secure vehicular communication requirements. In light of the intractability of the optimization problem, the semidefinite relaxation (SDR) technique and variable substitutions are applied, and the optimal solution is proven to be tight. A number of results demonstrate that the proposed robust secure beamforming scheme has better performance than other schemes.

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Design of Low Order Controllers for Decoupled MIMO Systems With Time Response Specifications

Advances in System Dynamics and Control - Advances in Systems Analysis, Software Engineering, and High Performance Computing ◽

10.4018/978-1-5225-4077-9.ch004 ◽

2018 ◽

pp. 90-128

Author(s):

Maher Ben Hariz ◽

Wassila Chagra ◽

Faouzi Bouani

Keyword(s):

Convex Optimization ◽

Optimization Problem ◽

Closed Loop ◽

Mimo Systems ◽

Design Method ◽

Optimal Solution ◽

Time Response ◽

Convex Optimization Problem ◽

Tito Systems ◽

Low Order

The design of a low order controller for decoupled MIMO systems is proposed. The main objective of this controller is to guarantee some closed loop time response performances such as the settling time and the overshoot. The controller parameters are obtained by resolving a non-convex optimization problem. In order to obtain an optimal solution, the use of a global optimization method is suggested. In this chapter, the proposed solution is the GGP method. The principle of this method consists of transforming a non-convex optimization problem to a convex one by some mathematical transformations. So as to accomplish the fixed goal, it is imperative to decouple the coupled MIMO systems. To approve the controllers' design method, the synthesis of fixed low order controller for decoupled TITO systems is presented firstly. Then, this design method is generalized in the case of MIMO systems. Simulation results and a comparison study between the presented approach and a PI controller are given in order to show the efficiency of the proposed controller. It is remarkable that the obtained solution meets the desired closed loop time specifications for each system output. It is also noted that by considering the proposed approach the user can fix the desired closed loop performances for each output independently.

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Minimizing Power Consumption of an Experimental HVAC System Based on Parallel Grid Searching

Energies ◽

10.3390/en13082083 ◽

2020 ◽

Vol 13 (8) ◽

pp. 2083 ◽

Cited By ~ 1

Author(s):

Wangqi Xiong ◽

Jiandong Wang

Keyword(s):

Power Consumption ◽

Graphics Processing Units ◽

Optimization Problem ◽

Search Algorithm ◽

Experimental Studies ◽

Optimal Solution ◽

Hvac System ◽

Convex Optimization Problem ◽

The One ◽

Graphics Processing

This paper proposes a parallel grid search algorithm to find an optimal operating point for minimizing the power consumption of an experimental heating, ventilating and air conditioning (HVAC) system. First, a multidimensional, nonlinear and non-convex optimization problem subject to constraints is formulated based on a semi-physical model of the experimental HVAC system. Second, the optimization problem is parallelized based on Graphics Processing Units to simultaneously compute optimization loss functions for different solutions in a searching grid, and to find the optimal solution as the one having the minimum loss function. The proposed algorithm has an advantage that the optimal solution is known with evidence as to the best one subject to current resolutions of the searching grid. Experimental studies are provided to support the proposed algorithm.

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Ambient BackCom in Beyond 5G NOMA Networks: A Multi-Cell Resource Allocation Framework

10.36227/techrxiv.17152730.v1 ◽

2021 ◽

Author(s):

Wali Ullah Khan ◽

Kapal Dev ◽

Muhammad Awais Javed ◽

Dinh-Thuan Do ◽

Nawab Muhammad Faseeh Qureshi ◽

...

Keyword(s):

Resource Allocation ◽

Multiple Access ◽

Optimization Problem ◽

Original Problem ◽

Base Station ◽

Joint Optimization ◽

Theory Approach ◽

Closed Form Solutions ◽

Cell Network ◽

Sum Rate

This article proposes a new resource allocation framework that uses the dual theory approach. Specifically, the sum-rate of the multi-cell network having backscatter tags and NOMA user equipments is maximized by formulating a joint optimization problem. To find the efficient base station transmit power and backscatter reflection coefficient in each cell, the original problem is first divided into two subproblems and then derived the closed-form solutions. A comparison with the orthogonal multiple access (OMA) ambient BackCom and pure NOMA transmission has been provided.

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GS-OPT: A new fast stochastic algorithm for solving the non-convex optimization problem

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v9.i2.pp183-192 ◽

2020 ◽

Vol 9 (2) ◽

pp. 183

Author(s):

Xuan Bui ◽

Nhung Duong ◽

Trung Hoang

Keyword(s):

Convex Optimization ◽

Optimization Problem ◽

Latent Dirichlet Allocation ◽

Stochastic Algorithm ◽

Inference Problem ◽

Theoretical Understanding ◽

Convex Optimization Problem ◽

Discrete Probability ◽

Convex Problems ◽

Posterior Inference

<p>Non-convex optimization has an important role in machine learning. However, the theoretical understanding of non-convex optimization remained rather limited. Studying efficient algorithms for non-convex optimization has attracted a great deal of attention from many researchers around the world but these problems are usually NP-hard to solve. In this paper, we have proposed a new algorithm namely GS-OPT (General Stochastic OPTimization) which is effective for solving the non-convex problems. Our idea is to combine two stochastic bounds of the objective function where they are made by a commonly discrete probability distribution namely Bernoulli. We consider GS-OPT carefully on both the theoretical and experimental aspects. We also apply GS-OPT for solving the posterior inference problem in the latent Dirichlet allocation. Empirical results show that our approach is often more efficient than previous ones.</p>

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An Exact Algorithm for Bilevel 0-1 Knapsack Problems

Mathematical Problems in Engineering ◽

10.1155/2012/504713 ◽

2012 ◽

Vol 2012 ◽

pp. 1-23 ◽

Cited By ~ 9

Author(s):

Raid Mansi ◽

Cláudio Alves ◽

J. M. Valério de Carvalho ◽

Saïd Hanafi

Keyword(s):

Optimization Problem ◽

Original Problem ◽

Parametric Optimization ◽

Feasible Solution ◽

Optimal Solution ◽

Exact Algorithm ◽

Decision Makers ◽

Knapsack Problems ◽

Hierarchical Decision ◽

Parametric Optimization Problem

We propose a new exact method for solving bilevel 0-1 knapsack problems. A bilevel problem models a hierarchical decision process that involves two decision makers called the leader and the follower. In these processes, the leader takes his decision by considering explicitly the reaction of the follower. From an optimization standpoint, these are problems in which a subset of the variables must be the optimal solution of another (parametric) optimization problem. These problems have various applications in the field of transportation and revenue management, for example. Our approach relies on different components. We describe a polynomial time procedure to solve the linear relaxation of the bilevel 0-1 knapsack problem. Using the information provided by the solutions generated by this procedure, we compute a feasible solution (and hence a lower bound) for the problem. This bound is used together with an upper bound to reduce the size of the original problem. The optimal integer solution of the original problem is computed using dynamic programming. We report on computational experiments which are compared with the results achieved with other state-of-the-art approaches. The results attest the performance of our approach.

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Price-Based Resource Allocation in Wireless Power Transfer-Enabled Massive MIMO Networks

Sensors ◽

10.3390/s19153298 ◽

2019 ◽

Vol 19 (15) ◽

pp. 3298 ◽

Cited By ~ 1

Author(s):

Zhengqiang Wang ◽

Kunhao Huang ◽

Xiaona Yang ◽

Xiaoyu Wan ◽

Zifu Fan ◽

...

Keyword(s):

Resource Allocation ◽

Convex Optimization ◽

Optimization Problem ◽

Wireless Power Transfer ◽

Base Station ◽

Power Transfer ◽

Wireless Power ◽

Convex Optimization Problem ◽

Harvesting Time ◽

Mimo Networks

This paper considers the price-based resource allocation problem for wireless power transfer (WPT)-enabled massive multiple-input multiple-output (MIMO) networks. The power beacon (PB) can transmit energy to the sensor nodes (SNs) by pricing their harvested energy. Then, the SNs transmit their data to the base station (BS) with large scale antennas by the harvesting energy. The interaction between PB and SNs is modeled as a Stackelberg game. The revenue maximization problem of the PB is transformed into the non-convex optimization problem of the transmit power and the harvesting time of the PB by backward induction. Based on the equivalent convex optimization problem, an optimal resource allocation algorithm is proposed to find the optimal price, energy harvesting time, and power allocation for the PB to maximize its revenue. Finally, simulation results show the effectiveness of the proposed algorithm.

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Real-time Bidding for Time Constrained Impression Contracts in First and Second Price Auctions - Theory and Algorithms

Proceedings of the ACM on Measurement and Analysis of Computing Systems ◽

10.1145/3491049 ◽

2021 ◽

Vol 5 (3) ◽

pp. 1-37

Author(s):

Ryan J. Kinnear ◽

Ravi R. Mazumdar ◽

Peter Marbach

Keyword(s):

Real Time ◽

Optimization Problem ◽

Optimal Solution ◽

Piecewise Constant Function ◽

Convex Optimization Problem ◽

Piecewise Constant ◽

Infinite Dimensional ◽

Optimal Behavior ◽

Price Auction ◽

Second Price Auctions

We study the optimal bids and allocations in a real-time auction for heterogeneous items subject to the requirement that specified collections of items of given types be acquired within given time constraints. The problem is cast as a continuous time optimization problem that can, under certain weak assumptions, be reduced to a convex optimization problem. Focusing on the standard first and second price auctions, we first show, using convex duality, that the optimal (infinite dimensional) bidding policy can be represented by a single finite vector of so-called ''pseudo-bids''. Using this result we are able to show that the optimal solution in the second price case turns out to be a very simple piecewise constant function of time. This contrasts with the first price case that is more complicated. Despite the fact that the optimal solution for the first price auction is genuinely dynamic, we show that there remains a close connection between the two cases and that, empirically, there is almost no difference between optimal behavior in either setting. This suggests that it is adequate to bid in a first price auction as if it were in fact second price. Finally, we detail methods for implementing our bidding policies in practice with further numerical simulations illustrating the performance.

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Throughput Fairness in Cognitive Backscatter Networks With Residual Hardware Impairments and a Nonlinear EH Model

10.21203/rs.3.rs-1031589/v1 ◽

2021 ◽

Author(s):

Xiaona Gao ◽

Liqin Shi ◽

Guangyue Lu

Keyword(s):

Iterative Algorithm ◽

Optimization Problem ◽

Optimal Solution ◽

Primary User ◽

Energy Harvesters ◽

Hardware Impairments ◽

Convex Problem ◽

Resource Allocation Scheme ◽

Nonlinear Energy

Abstract This paper is to design a throughput fairness-aware resource allocation scheme for a cognitive backscatter network (CBN), where multiple backscatter devices (BDs) take turns to modulate information on the primary signals and backscatter the modulated signals to a cooperative receiver (C-Rx), while harvesting energy to sustain their operations. The nonlinear energy harvesting (EH) circuits at the BDs and the residual hardware impairments (HWIs) at the transceivers are considered to better reflect the properties of the practical energy harvesters and transceivers, respectively. To ensure the throughput fairness among BDs, we formulate an optimization problem to maximize the minimum throughput of BDs by jointly optimizing the transmit power of the primary transmitter, the backscattering time and reflection coefficient for each BD, subject to the primary user’s quality of service (QoS) and BDs’ energy-causality constraints. We introduce the variable slack and decoupling methods to transform the formulated non-convex problem, and propose an iterative algorithm based on block coordinate descent (BCD) technique to solve the transformation problem. We also investigate a special CBN with a single BD and derive the optimal solution in the closed form to maximize the BD's throughput. Numerical results validate the quick convergence of the proposed iterative algorithm and that the proposed scheme ensures much fairness than the existing schemes.

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