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 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.

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 Optimization of Cognitive UAV-Assisted Edge Communication for Semantic Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yilong Gu ◽  
Yangchao Huang ◽  
Hang Hu ◽  
Weiting Gao ◽  
Yu Pan
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Energy Efficient ◽  
Optimization Problem ◽  
Optimization Scheme ◽  
Battery Capacity ◽  
Aerial Vehicle ◽  
Efficient Alternative ◽  
The Internet Of Things

With the consolidation of the Internet of Things (IoT), the unmanned aerial vehicle- (UAV-) based IoT has attracted much attention in recent years. In the IoT, cognitive UAV can not only overcome the problem of spectrum scarcity but also improve the communication quality of the edge nodes. However, due to the generation of massive and redundant IoT data, it is difficult to realize the mutual understanding between UAV and ground nodes. At the same time, the performance of the UAV is severely limited by its battery capacity. In order to form an autonomous and energy-efficient IoT system, we investigate semantically driven cognitive UAV networks to maximize the energy efficiency (EE). The semantic device model for cognitive UAV-assisted IoT communication is constructed. And the sensing time, the flight speed of UAV, and the coverage range of UAV communication are jointly optimized to maximize the EE. Then, an efficient alternative algorithm is proposed to solve the optimization problem. Finally, we provide computer simulations to validate the proposed algorithm. The performance of the joint optimization scheme based on the proposed algorithm is compared to some benchmark schemes. And the simulation results show that the proposed scheme can obtain the optimal system parameters and can significantly improve the EE.

scholarly journals Parameter Study on Weight Minimization of Network Arch Bridges

2017 ◽  
Author(s):  
Rimantas Belevicius ◽  
Algirdas Juozapaitis ◽  
Dainius Rusakevičius
Keyword(s):  
Optimization Problem ◽  
Minimum Weight ◽  
Optimization Technique ◽  
Mixed Integer ◽  
Design Parameters ◽  
Parameter Study ◽  
Large Set ◽  
Topological Parameters ◽  
Arch Bridges ◽  
Arch Shape

The article concerns optimization of network arch bridges. This is challenging optimization problem involving even for conventional scheme of network arch bridge the identification of some topological parameters as well as shape configurations and all sizing parameters of structural members, seeking the minimum weight. Optimal bridge scheme is sought tuning a large set of design parameters of diverse character: the type of hanger arrangement, the number of hangers, their inclination angles and placement distances, the arch shape and rise, etc. Mathematically, the optimization of the bridge scheme is a mixed-integer constrained global optimization problem solved employing stochastic evolutionary algorithm. Plane heavy/moderate/and light-deck bridges of 18, 30, 42 and 54 m spans were optimized using proposed optimization technique. The decisive design parameters and their promising ranges were revealed. Also, the influence of some simplifications is shown: changing the arch shape from elliptical to circular, placing the hangers at equal distances, etc.

scholarly journals Caching deployment based on energy efficiency in device-to-device cooperative networks

2020 ◽  
Vol 16 (12) ◽  
pp. 155014772098465
Author(s):  
Weiguang Wang ◽  
Hui Li ◽  
Yang Liu ◽  
Wei Cheng ◽  
Haoyang Qin
Keyword(s):  
Energy Efficiency ◽  
Communication Networks ◽  
Optimization Problem ◽  
Transformation Method ◽  
Cooperative Networks ◽  
Closed Form Expression ◽  
Maximization Problem ◽  
Quadratic Transformation ◽  
Traffic Demand ◽  
Device To Device

The rapid growth of mobile data traffic demand will cause congestion to the future communication network. The cache-enabled device-to-device communication has been proven to effectively enhance the performance of wireless communication networks. This article investigates the caching deployment problem from the energy efficiency in the cache-enabled device-to-device networks. According to the random geometry theory modeling, the closed form expression of energy efficiency is derived, which measures the average number of successful transmitted file bits per unit time and per unit power consumption. And then we establish an optimization problem to maximize energy efficiency. As the formulated optimization problem is a multiple-ratio fractional programming problem that cannot be solved conveniently, we propose a quadratic transformation method to nest in the energy efficiency maximization problem. To tackle this problem, an iterative optimization algorithm is proposed to optimize the caching policy and network energy efficiency. The simulation results demonstrate that the proposed policy can achieve higher energy efficiency and hit probability in the cache-enabled device-to-device network.

scholarly journals Energy Efficiency Maximization for UAV-Assisted Emergency Communication Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Haibin Niu ◽  
Xinyu Zhao ◽  
Liming Hou ◽  
Dongjun Ma
Keyword(s):  
Energy Efficiency ◽  
Communication Networks ◽  
Nonconvex Optimization ◽  
Bandwidth Allocation ◽  
Optimization Problem ◽  
Energy Savings ◽  
Low Cost ◽  
Optimization Technique ◽  
Base Station ◽  
Effective Capacity

Using unmanned aerial vehicles (UAVs) in emergency communications is a promising technology because of their flexible deployment, low cost, and high mobility. However, due to the limited energy of the onboard battery, the service duration of the UAV is greatly limited. In this paper, we study an emerging energy-efficient UAV emergency network, where a UAV works as an aerial base station to serve a group of users with different statistical quality-of-service (QoS) constraints in the downlink. In particular, the energy efficiency of the UAV is defined as the sum effective capacity of the downlink users divided by the energy consumption of the UAV, which includes the energy consumed by communication and the energy consumed by hovering. Then, we formulate an optimization problem to maximize the energy efficiency of the UAV by jointly optimizing the UAV’s altitude, downlink transmit power, and bandwidth allocation while meeting a statistical delay QoS requirement for each user. The formulated optimization problem is a nonlinear nonconvex optimization problem of fractional programming, which is difficult to solve. In order to deal with the nonconvex optimization problem, the following two steps are used. First, we transform the fractional objective function into a tractable subtractive function. Second, we decompose the original optimization problem into three subproblems, and then, we propose an efficient iterative algorithm to obtain the energy efficiency maximization value by using the Dinkelbach method, the block coordinate descent, and the successive convex optimization technique. Extensive simulation results show that our proposed algorithm has significant energy savings compared with a benchmark scheme.

scholarly journals Energy Efficiency Optimization in Adaptive Massive MIMO Networks for 5G Applications Using Genetic Algorithm

2021 ◽  
Author(s):  
Ibrahim Salah ◽  
M. Mourad Mabrook ◽  
Kamel Hussein Rahouma ◽  
Aziza I. Hussein
Keyword(s):  
Genetic Algorithm ◽  
Energy Efficiency ◽  
Spectral Efficiency ◽  
Massive Mimo ◽  
Mimo System ◽  
Optimization Technique ◽  
Base Station ◽  
Maximum Efficiency ◽  
Adaptive Optimization ◽  
Trade Offs

Abstract Given that the exponential pace of growth in wireless traffic has continued for more than a century, wireless communication is one of the most influential innovations in recent years. Massive Multiple-Input Multiple-Output (M-MIMO) is a promising technology for meeting the world's exponential growth in mobile data traffic, particularly in 5G networks. The most critical metrics in the massive MIMO scheme are Spectral Efficiency (SE) and Energy Efficiency (EE). For single-cell MMIMO uplink transmission, energy and spectral-efficiency trade-offs have to be estimated by optimizing the number of base station antennas versus the number of active users. This paper proposes an adaptive optimization technique focusing on maximizing Energy Efficiency at full spectral efficiency using a Genetic Algorithm (GA) optimizer. The number of active antennas is estimated according to the change in the number of active users based on the proposed GA scheme that optimizes the EE in the M-MIMO system. Simulation results show that the GA optimization technique achieved the maximum energy efficiency of the 5G M-MIMO platform and the maximum efficiency in the trade-off process.

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.

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