scholarly journals Joint Power Loading and Phase Shifting on Signal Constellation for Transmit Power Saving on OFDM/OFDMA Systems

2018 ◽  
Vol 8 (5) ◽  
pp. 2039 ◽  
Author(s):  
Budi Prasetya ◽  
Adit Kurniawan ◽  
Iskandar ◽  
Arfianto Fahmi
Keyword(s):  
Power Saving ◽  
Phase Shifting ◽  
Joint Power ◽  
Transmit Power ◽  
Signal Constellation ◽  
Power Loading

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.

Optimized Backlight Power Saving Algorithm Using Joint Power-PSNR Characteristics Among Multiple Frames

2016 ◽  
Vol 12 (12) ◽  
pp. 1506-1513
Author(s):  
Ting-Lan Lin ◽  
Kun-Hsien Tung ◽  
Guan-Jie Fang ◽  
Po-Yi Wu
Keyword(s):  
Power Saving ◽  
Joint Power ◽  
Multiple Frames ◽  
Saving Algorithm

Joint power control and beamforming in MIMO relays

2016 ◽  
Vol 35 (1) ◽  
pp. 102-116 ◽  
Author(s):  
Shahriar Shirvani Moghaddam ◽  
Maryam Alibeigi
Keyword(s):  
Power Consumption ◽  
Optimization Problem ◽  
Performance Comparison ◽  
Constant Factor ◽  
Amplify And Forward ◽  
Efficiency Criterion ◽  
Joint Power ◽  
Transmit Power ◽  
Content Type ◽  
Sum Rate

Purpose – In this paper, a wireless network consisting of multiple source-destination pairs, which communicate peer-to-peer with the help of multiple Amplify-and-Forward relays is considered. The purpose of this paper is to minimize the total relay transmit power subject to a sum rate threshold. Design/methodology/approach – It is shown that finding a beamforming matrix which satisfies the investigation’s goal is a non-convex optimization problem. Therefore, a semidefinite relaxation technique is used to turn it into a semidefinite programming problem. Thus, it can be effectively solved by interior point methods. Findings – Simulation results show that the total relay transmit power decreases with the number of relays. In addition, it is shown that relays’ power consumption decreases when the quality of uplink and/or downlink channels improves. In order to compare the proposed optimization algorithm to two conventional approaches, energy efficiency criterion used for performance comparison and complexity order for evaluating computational complexity. Although the proposed algorithm does not change the order of complexity, it needs a few more iterations to find the best result which leads to a double the processing time compared to existing approaches. Originality/value – In the present optimization problem, coefficient ß is used instead of a constant factor 2 in the original optimization problem. It is shown that by changing parameter ß between 1 and 2, a lower power consumption for a target sum rate can be achieved.

Intelligent Multiple Relay Selection and Transmit Power-Saving with ABC Optimization for Underlay Relay-assisted CRNs

2018 ◽  
pp. 377-384 ◽  
Author(s):  
Kiran Sultan ◽  
◽  
Atta-ur-Rahman Atta-ur-Rahman ◽  
Bassam A. Zafar ◽  
Nahier Aldhafferi ◽  
...  
Keyword(s):  
Relay Selection ◽  
Power Saving ◽  
Transmit Power ◽  
Multiple Relay

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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