scholarly journals THz Transmission meets Untrusted UAV-Relaying; Trajectory and Communication Co-design for Secrecy Energy Efficiency Maximization

2021 ◽  
Author(s):  
Milad Tatar Mamaghani ◽  
Yi Hong
Keyword(s):  
Energy Efficiency ◽  
Optimization Problem ◽  
Mixed Integer ◽  
Security Level ◽  
Secrecy Rate ◽  
Two Phase ◽  
Cooperative Jamming ◽  
Transmission Strategy ◽  
Average System ◽  
Phase Transmission

Unmanned aerial vehicles (UAVs) and Terahertz (THz) technology are envisioned to play paramount roles in next-generation wireless communications. Hence, this paper presents a novel secure UAV-assisted mobile relaying system operating at THz bands for data acquisition from multiple ground user equipments towards a destination. We assume that the UAV-mounted relay may act, besides providing relaying services, as a potential adversary called the untrusted UAV relay. To safeguard end-to-end communications, we present a secure two-phase transmission strategy with cooperative jamming. Then, we formulate an optimization problem in terms of a new measure – secrecy energy efficiency (SEE), defined as the ratio of achievable average secrecy rate to average system power consumption, which enables us to obtain the best possible security level while taking UAV's inherent flight power limitation into account. This optimization problem leads to a joint design of key system parameters, including UAV's trajectory and velocity, communication scheduling, and power allocations. Since the formulated problem is a mixed-integer nonconvex optimization and computationally intractable, we propose alternative algorithms to solve it efficiently via greedy/sequential block coordinated descent, successive convex approximation, and non-linear fractional programming techniques. Numerical results demonstrate significant SEE performance improvement of our designs when compared to other known benchmarks.

scholarly journals THz Transmission meets Untrusted UAV-Relaying; Trajectory and Communication Co-design for Secrecy Energy Efficiency Maximization

2021 ◽  
Author(s):  
Milad Tatar Mamaghani ◽  
Yi Hong
Keyword(s):  
Energy Efficiency ◽  
Optimization Problem ◽  
Mixed Integer ◽  
Security Level ◽  
Secrecy Rate ◽  
Two Phase ◽  
Cooperative Jamming ◽  
Transmission Strategy ◽  
Average System ◽  
Phase Transmission

Unmanned aerial vehicles (UAVs) and Terahertz (THz) technology are envisioned to play paramount roles in next-generation wireless communications. Hence, this paper presents a novel secure UAV-assisted mobile relaying system operating at THz bands for data acquisition from multiple ground user equipments towards a destination. We assume that the UAV-mounted relay may act, besides providing relaying services, as a potential adversary called the untrusted UAV relay. To safeguard end-to-end communications, we present a secure two-phase transmission strategy with cooperative jamming. Then, we formulate an optimization problem in terms of a new measure – secrecy energy efficiency (SEE), defined as the ratio of achievable average secrecy rate to average system power consumption, which enables us to obtain the best possible security level while taking UAV's inherent flight power limitation into account. This optimization problem leads to a joint design of key system parameters, including UAV's trajectory and velocity, communication scheduling, and power allocations. Since the formulated problem is a mixed-integer nonconvex optimization and computationally intractable, we propose alternative algorithms to solve it efficiently via greedy/sequential block coordinated descent, successive convex approximation, and non-linear fractional programming techniques. Numerical results demonstrate significant SEE performance improvement of our designs when compared to other known benchmarks.

scholarly journals Energy Efficiency Design for Secure MISO Cognitive Radio Network Based on a Nonlinear EH Model

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lei Ni ◽  
Xinyu Da ◽  
Hang Hu ◽  
Miao Zhang
Keyword(s):  
Energy Efficiency ◽  
Cognitive Radio ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Primary User ◽  
Open Architecture ◽  
Maximization Problem ◽  
Transmit Beamforming ◽  
Secrecy Rate ◽  
Single Output

In this work, we investigate the secrecy energy efficiency (SEE) optimization problem for a multiple-input single-output (MISO) cognitive radio (CR) network based on a practical nonlinear energy-harvesting (EH) model. In particular, the energy receiver (ER) is assumed to be a potential eavesdropper due to the open architecture of a CR network with simultaneous wireless information and power transfer (SWIPT), such that the confidential message is prone to be intercepted in wireless communications. The aim of this work is to provide a secure transmit beamforming design while satisfying the minimum secrecy rate target, the minimum EH requirement, and the maximum interference leakage power to primary user (PU). In addition, we consider that all the channel state information (CSI) is perfectly known at the secondary transmitter (ST). We formulate this beamforming design as a SEE maximization problem; however, the original optimization problem is not convex due to the nonlinear fractional objective function. To solve it, a novel iterative algorithm is proposed to obtain the globally optimal solution of the primal problem by using the nonlinear fractional programming and sequential programming. Finally, numerical simulation results are presented to validate the performance of the proposed scheme.

scholarly journals Joint Trajectory and Power Allocation Design for Secure Artificial Noise aided UAV Communications

2021 ◽  
Author(s):  
Milad Tatar Mamaghani ◽  
Yi Hong
Keyword(s):  
Power Allocation ◽  
Maximization Problem ◽  
Artificial Noise ◽  
Transmission Protocol ◽  
Secrecy Rate ◽  
Two Phase ◽  
Convex Problem ◽  
Aerial Vehicle ◽  
Network Transmission ◽  
Phase Transmission

This paper investigates an average secrecy rate (ASR) maximization problem for an unmanned aerial vehicle (UAV) enabled wireless communication system, wherein a UAV is employed to deliver confidential information to a ground destination in the presence of a terrestrial passive eavesdropper. By employing an artificial noise (AN) injection based secure two-phase transmission protocol, we aim at jointly optimizing the UAV’s trajectory, network transmission power, and AN power allocation over a given time horizon to enhance the ASR performance. Specifically, we divide the original non-convex problem into four subproblems, and propose a successive convex approximation based efficient iterative algorithm to solve it suboptimally with guaranteed convergence. Simulation results demonstrate significant security advantages of our designed scheme over other known benchmarks, particularly for stringent flight durations.

scholarly journals Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2550
Author(s):  
Saidiwaerdi Maimaiti ◽  
Gang Chuai ◽  
Weidong Gao ◽  
Jinxi Zhang
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Optimization Problem ◽  
Power Optimization ◽  
Mimo System ◽  
Optimization Technique ◽  
Mixed Integer ◽  
Optimization Scheme ◽  
Quadratic Transformation ◽  
Convex Problem

This paper studies beam allocation and power optimization scheme to decrease the hardware cost and downlink power consumption of a multiuser millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. Our target is to improve energy efficiency (EE) and decrease power consumption without obvious system performance loss. To this end, we propose a beam allocation and power optimization scheme. First, the problem of beam allocation and power optimization is formulated as a multivariate mixed-integer non-linear programming problem. Second, due to the non-convexity of this problem, we decompose it into two sub-problems which are beam allocation and power optimization. Finally, the beam allocation problem is solved by using a convex optimization technique. We solve the power optimization problem in two steps. First, the non-convex problem is converted into a convex problem by using a quadratic transformation scheme. The second step implements Lagrange dual and sub-gradient methods to solve the optimization problem. Performance analysis and simulation results show that the proposed algorithm performs almost identical to the exhaustive search (ES) method, while the greedy beam allocation and suboptimal beam allocation methods are far from the ES. Furthermore, experiment results demonstrated that our proposed algorithm outperforms the compared the greedy beam allocation method and the suboptimal beam allocation scheme in terms of average service ratio.

scholarly journals Joint Trajectory and Power Allocation Design for Secure Artificial Noise aided UAV Communications

2021 ◽  
Author(s):  
Milad Tatar Mamaghani ◽  
Yi Hong
Keyword(s):  
Power Allocation ◽  
Maximization Problem ◽  
Artificial Noise ◽  
Transmission Protocol ◽  
Secrecy Rate ◽  
Two Phase ◽  
Convex Problem ◽  
Aerial Vehicle ◽  
Network Transmission ◽  
Phase Transmission

This paper investigates an average secrecy rate (ASR) maximization problem for an unmanned aerial vehicle (UAV) enabled wireless communication system, wherein a UAV is employed to deliver confidential information to a ground destination in the presence of a terrestrial passive eavesdropper. By employing an artificial noise (AN) injection based secure two-phase transmission protocol, we aim at jointly optimizing the UAV’s trajectory, network transmission power, and AN power allocation over a given time horizon to enhance the ASR performance. Specifically, we divide the original non-convex problem into four subproblems, and propose a successive convex approximation based efficient iterative algorithm to solve it suboptimally with guaranteed convergence. Simulation results demonstrate significant security advantages of our designed scheme over other known benchmarks, particularly for stringent flight durations.

scholarly journals Online joint power control for cooperative jamming systems with energy harvesting

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988811
Author(s):  
Weijia Lei ◽  
Ziwei Wang ◽  
Hongjiang Lei
Keyword(s):  
Energy Harvesting ◽  
Power Control ◽  
Optimization Problem ◽  
Source Node ◽  
Artificial Noise ◽  
Secrecy Rate ◽  
Cooperative Jamming ◽  
Optimization Framework ◽  
Lyapunov Optimization

To maximize the long-term time-averaged secrecy rate of an energy harvesting wireless communication system, an online power control algorithm based on the Lyapunov optimization framework is proposed. The system is composed of a source node, a cooperative jamming node, and two destination nodes. The source node and the jamming node are powered by the energy harvesting device. Information sent to the two destination nodes is mutually confidential. Using the Lyapunov optimization framework, the original stochastic optimization problem is transformed into a per-time-slot optimization problem, and the power of the signal and that of the artificial noise are determined based on the current system state such as the power level of the batteries and channel coefficients. The fairness between the two destination nodes is considered too. Simulation results demonstrate that the proposed algorithm can effectively utilize the harvested energy and significantly improve the long-term averaged secrecy rate.

On the Optimal Precoder Design for Energy-Efficient and Secure MIMO Systems

2017 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Tung T. Vu ◽  
Ha Hoang Kha
Keyword(s):  
Energy Efficiency ◽  
Mimo Systems ◽  
Search Algorithm ◽  
Multiple Input Multiple Output ◽  
Research Work ◽  
Computational Cost ◽  
Optimal Solution ◽  
Secrecy Rate ◽  
Highly Nonlinear ◽  
Precoder Design

In this research work, we investigate precoder designs to maximize the energy efficiency (EE) of secure multiple-input multiple-output (MIMO) systems in the presence of an eavesdropper. In general, the secure energy efficiency maximization (SEEM) problem is highly nonlinear and nonconvex and hard to be solved directly. To overcome this difficulty, we employ a branch-and-reduce-and-bound (BRB) approach to obtain the globally optimal solution. Since it is observed that the BRB algorithm suffers from highly computational cost, its globally optimal solution is importantly served as a benchmark for the performance evaluation of the suboptimal algorithms. Additionally, we also develop a low-complexity approach using the well-known zero-forcing (ZF) technique to cancel the wiretapped signal, making the design problem more amenable. Using the ZF based method, we transform the SEEM problem to a concave-convex fractional one which can be solved by applying the combination of the Dinkelbach and bisection search algorithm. Simulation results show that the ZF-based method can converge fast and obtain a sub-optimal EE performance which is closed to the optimal EE performance of the BRB method. The ZF based scheme also shows its advantages in terms of the energy efficiency in comparison with the conventional secrecy rate maximization precoder design.

scholarly journals Joint optimization of computing ratio and access points’ density for mixed mobile edge/cloud computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Tianqi Jing ◽  
Shiwen He ◽  
Fei Yu ◽  
Yongming Huang ◽  
Luxi Yang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Coverage Probability ◽  
Optimization Problem ◽  
Mobile Cloud Computing ◽  
Low Complexity ◽  
Mixed Integer ◽  
Joint Optimization ◽  
Mobile Cloud ◽  
Convex Problem

AbstractCooperation between the mobile edge computing (MEC) and the mobile cloud computing (MCC) in offloading computing could improve quality of service (QoS) of user equipments (UEs) with computation-intensive tasks. In this paper, in order to minimize the expect charge, we focus on the problem of how to offload the computation-intensive task from the resource-scarce UE to access point’s (AP) and the cloud, and the density allocation of APs’ at mobile edge. We consider three offloading computing modes and focus on the coverage probability of each mode and corresponding ergodic rates. The resulting optimization problem is a mixed-integer and non-convex problem in the objective function and constraints. We propose a low-complexity suboptimal algorithm called Iteration of Convex Optimization and Nonlinear Programming (ICONP) to solve it. Numerical results verify the better performance of our proposed algorithm. Optimal computing ratios and APs’ density allocation contribute to the charge saving.

Subcarrier and Power Allocation Algorithm for Spectral Efficiency Maximization in Superposition Coding OFDMA Systems

2015 ◽  
Vol 24 (05) ◽  
pp. 1550061
Author(s):  
Mateus de Paula Marques ◽  
Taufik Abrão
Keyword(s):  
Power Allocation ◽  
Spectral Efficiency ◽  
Time Complexity ◽  
Multiple Access ◽  
Optimization Problem ◽  
Frequency Division ◽  
Superposition Coding ◽  
Ofdma System ◽  
Transmission Strategy ◽  
Throughput Gain

This paper addresses the optimization problem on subcarrier and power allocation of orthogonal frequency division multiple access (OFDMA) system under spectral efficiency (SE) metric when deploying superposition coding (SC) transmission strategy. An algorithm with polynomial time complexity, of the order of (UN log 2(N)) has been proposed for sub-optimal SE maximization. Results indicate that the system SE increases with the use of SC technique. Besides, the throughput gain with SC adoption increases when the number of users (U) approaches the number of subcarriers (N) available in the system.

Production Process Modeling for Demand Side Management

2017 ◽  
Vol 871 ◽  
pp. 77-86
Author(s):  
Stefanie Kabelitz ◽  
Sergii Kolomiichuk
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Supply And Demand ◽  
Demand Side Management ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Business Practices ◽  
Demand Side ◽  
Manufacturing Companies ◽  
Consumption Data

The supply of electricity is growing increasingly dependent on the weather as the share of renewable energies increases. Different measures can nevertheless maintain grid reliability and quality. These include the use of storage technologies, upgrades of the grid and options for responsiveness to supply and demand. This paper focuses on demand side management and the use of flexibility in production processes. First, the framework of Germany’s energy policy is presented and direct and indirect incentives for businesses to seek as well as to provide flexibility capabilities are highlighted. Converting this framework into a mixed integer program leads to multi-objective optimization. The challenge inherent to this method is realistically mapping the different objectives that affect business practices directly and indirectly in a variety of laws. An example is introduced to demonstrate the complexity of the model and examine the energy flexibility. Second, manufacturing companies’ energy efficiency is assessed under the frequently occurring conditions of heavily aggregated energy consumption data and of information with insufficient depth of detail to perform certain analyses, formulate actions or optimize processes. The findings obtained from the energy assessment and energy consumption projections are used to model the production system’s energy efficiency and thus facilitate optimization. Methods of data mining and machine learning are employed to project energy consumption. Aggregated energy consumption data and different production and environmental parameters are used to assess indirectly measured consumers and link projections of energy consumption with the production schedule.

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