A mutual-information based power allocation algorithm for multi-layer rate splitting scheme in tri-sectored wireless networks

2012 ◽  
Author(s):  
Guangxia Zhou ◽  
Wen Xu ◽  
Gerhard Bauch
Keyword(s):  
Wireless Networks ◽  
Mutual Information ◽  
Power Allocation ◽  
Splitting Scheme ◽  
Allocation Algorithm ◽  
Rate Splitting

scholarly journals Proportional Fairness-based Power Allocation Algorithm for Downlink NOMA 5G Wireless Networks

2020 ◽  
Vol 65 (2) ◽  
pp. 1571-1590
Author(s):  
Li Jianzhong ◽  
Mei Dexiang ◽  
Deng Dong ◽  
Imran Khan ◽  
Peerapong Uthansakul
Keyword(s):  
Wireless Networks ◽  
Power Allocation ◽  
Proportional Fairness ◽  
Allocation Algorithm

Power allocation algorithm with QoS-requirements in multi-user relay networks

2011 ◽  
Vol 31 (3) ◽  
pp. 606-608
Author(s):  
Jing-lin YAN ◽  
Lun TANG ◽  
Qian-bin CHEN ◽  
Bo CHEN
Keyword(s):  
Power Allocation ◽  
Relay Networks ◽  
Allocation Algorithm

Joint user clustering and salp based particle swarm optimization algorithm for power allocation in MIMO-NOMA

2021 ◽  
Vol 40 (5) ◽  
pp. 9007-9019
Author(s):  
Jyotirmayee Subudhi ◽  
P. Indumathi
Keyword(s):  
Energy Efficiency ◽  
Particle Swarm Optimization ◽  
Power Allocation ◽  
Multiple Access ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Allocation Algorithm ◽  
User Clustering ◽  
Sum Rate

Non-Orthogonal Multiple Access (NOMA) provides a positive solution for multiple access issues and meets the criteria of fifth-generation (5G) networks by improving service quality that includes vast convergence and energy efficiency. The problem is formulated for maximizing the sum rate of MIMO-NOMA by assigning power to multiple layers of users. In order to overcome these problems, two distinct evolutionary algorithms are applied. In particular, the recently implemented Salp Swarm Algorithm (SSA) and the prominent Optimization of Particle Swarm (PSO) are utilized in this process. The MIMO-NOMA model optimizes the power allocation by layered transmission using the proposed Joint User Clustering and Salp Particle Swarm Optimization (PPSO) power allocation algorithm. Also, the closed-form expression is extracted from the current Channel State Information (CSI) on the transmitter side for the achievable sum rate. The efficiency of the proposed optimal power allocation algorithm is evaluated by the spectral efficiency, achievable rate, and energy efficiency of 120.8134bits/s/Hz, 98Mbps, and 22.35bits/Joule/Hz respectively. Numerical results have shown that the proposed PSO algorithm has improved performance than the state of art techniques in optimization. The outcomes on the numeric values indicate that the proposed PSO algorithm is capable of accurately improving the initial random solutions and converging to the optimum.

Sign in / Sign up

Export Citation Format

Share Document