scholarly journals Multi-Objective Optimization of Green Small Cell Allocation for IoT Applications in Smart City

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 101903-101914 ◽  
Author(s):  
Hao Ran Chi ◽  
Ayman Radwan
Keyword(s):  
Smart City ◽  
Small Cell ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Iot Applications

scholarly journals NOMA-enabled Optimization Framework for Next-generation Small-cell IoV Networks under Imperfect SIC Decoding

2021 ◽  
Author(s):  
Wali Ullah Khan ◽  
Xingwang Li ◽  
Asim Ihsan ◽  
Mohammad Ayoub Khan ◽  
Varun G Menon ◽  
...  
Keyword(s):  
Interference Cancellation ◽  
Average Power ◽  
Small Cell ◽  
System Capacity ◽  
Multi Objective Optimization ◽  
Joint Power ◽  
Transmit Power ◽  
Multi Objective ◽  
Optimization Framework ◽  
Weighted Sum Method

To meet the demands of massive connections, diverse quality of services (QoS), ultra-reliable and low latency in the future sixth-generation (6G) Internet-of-vehicle (IoV) communications, we propose non-orthogonal multiple access (NOMA)-enabled small-cell IoV network (SVNet). We aim to investigate the trade-off between system capacity and energy efficiency through a joint power optimization framework. In particular, we formulate a nonlinear multi-objective optimization problem under imperfect successive interference cancellation (SIC) detecting. Thus, the objective is to simultaneously maximize the sum-capacity and minimize the total transmit power of NOMA-enabled SVNet subject to individual IoV QoS, maximum transmit power and efficient signal detecting. To solve the nonlinear problem, we first exploit a weighted-sum method to handle the multi-objective optimization and then adopt a new iterative Sequential Quadratic Programming (SQP)-based approach to obtain the optimal solution. The proposed optimization framework is compared with Karush-Kuhn-Tucker (KKT)-based NOMA framework, average power NOMA framework, and conventional OMA framework. Monte Carlo simulation results unveil the validness of our derivations.

scholarly journals A Generalized SDP Multi-Objective Optimization Method for EM-Based Microwave Device Design

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3065 ◽  
Author(s):  
Ying Liu ◽  
Qingsha S. Cheng ◽  
Slawomir Koziel
Keyword(s):  
Computational Cost ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Uwb Antenna ◽  
Nsga Ii ◽  
Multi Objective ◽  
Iot Applications ◽  
Comparison Results ◽  
Pareto Fronts ◽  
Better Than

In this article, a generalized sequential domain patching (GSDP) method for efficient multi-objective optimization based on electromagnetics (EM) simulation is proposed. The GSDP method allowing fast searching for Pareto fronts for two and three objectives is elaborated in detail in this paper. The GSDP method is compared with the NSGA-II method using multi-objective problems in the DTLZ series, and the results show the GSDP method saved computational cost by more than 85% compared to NSGA-II method. A diversity comparison indicator (DCI) is used to evaluate approximate Pareto fronts. The comparison results show the diversity performance of GSDP is better than that of NSGA-II in most cases. We demonstrate the proposed GSDP method using a practical multi-objective design example of EM-based UWB antenna for IoT applications.

scholarly journals NOMA-enabled Optimization Framework for Next-generation Small-cell IoV Networks under Imperfect SIC Decoding

2021 ◽  
Author(s):  
Wali Ullah Khan ◽  
Xingwang Li ◽  
Asim Ihsan ◽  
Mohammad Ayoub Khan ◽  
Varun G Menon ◽  
...  
Keyword(s):  
Interference Cancellation ◽  
Average Power ◽  
Small Cell ◽  
System Capacity ◽  
Multi Objective Optimization ◽  
Joint Power ◽  
Transmit Power ◽  
Multi Objective ◽  
Optimization Framework ◽  
Weighted Sum Method

To meet the demands of massive connections, diverse quality of services (QoS), ultra-reliable and low latency in the future sixth-generation (6G) Internet-of-vehicle (IoV) communications, we propose non-orthogonal multiple access (NOMA)-enabled small-cell IoV network (SVNet). We aim to investigate the trade-off between system capacity and energy efficiency through a joint power optimization framework. In particular, we formulate a nonlinear multi-objective optimization problem under imperfect successive interference cancellation (SIC) detecting. Thus, the objective is to simultaneously maximize the sum-capacity and minimize the total transmit power of NOMA-enabled SVNet subject to individual IoV QoS, maximum transmit power and efficient signal detecting. To solve the nonlinear problem, we first exploit a weighted-sum method to handle the multi-objective optimization and then adopt a new iterative Sequential Quadratic Programming (SQP)-based approach to obtain the optimal solution. The proposed optimization framework is compared with Karush-Kuhn-Tucker (KKT)-based NOMA framework, average power NOMA framework, and conventional OMA framework. Monte Carlo simulation results unveil the validness of our derivations.

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