Low-complexity particle swarm optimisation-based adaptive user clustering for downlink non-orthogonal multiple access deployed for 5G systems

2022 ◽  
Vol 18 (1) ◽  
pp. 7
Author(s):  
S. Prabha Kumaresan ◽  
Chee Keong Tan ◽  
Ching Kwang Lee ◽  
Yin Hoe Ng
Keyword(s):  
Multiple Access ◽  
Particle Swarm ◽  
Low Complexity ◽  
Particle Swarm Optimisation ◽  
User Clustering ◽  
5G Systems

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.

Frequencies Wave Sound Particle Swarm Optimisation (FPSO)

2021 ◽  
pp. 1-32
Author(s):  
Ahmad K. Al Hwaitat ◽  
Rizik M. H. Al-Sayyed ◽  
Imad K. M. Salah ◽  
Saher Manaseer ◽  
Hamed S. Al-Bdour ◽  
...  
Keyword(s):  
Particle Swarm ◽  
Particle Swarm Optimisation

scholarly journals User Clustering and Power Allocation for Energy Efficiency Maximization in Downlink Non-Orthogonal Multiple Access Systems

2021 ◽  
Vol 11 (2) ◽  
pp. 716
Author(s):  
Ruibiao Chen ◽  
Fangxing Shu ◽  
Kai Lei ◽  
Jianping Wang ◽  
Liangjie Zhang
Keyword(s):  
Energy Efficiency ◽  
Power Allocation ◽  
Multiple Access ◽  
Spectrum Efficiency ◽  
Lagrangian Multiplier ◽  
Efficiency Gain ◽  
Optimal Power Allocation ◽  
Optimal Power ◽  
User Clustering ◽  
Cluster Dynamic

Non-orthogonal multiple access (NOMA) has been considered a promising technique for the fifth generation (5G) mobile communication networks because of its high spectrum efficiency. In NOMA, by using successive interference cancellation (SIC) techniques at the receivers, multiple users with different channel gain can be multiplexed together in the same subchannel for concurrent transmission in the same spectrum. The simultaneously multiple transmission achieves high system throughput in NOMA. However, it also leads to more energy consumption, limiting its application in many energy-constrained scenarios. As a result, the enhancement of energy efficiency becomes a critical issue in NOMA systems. This paper focuses on efficient user clustering strategy and power allocation design of downlink NOMA systems. The energy efficiency maximization of downlink NOMA systems is formulated as an NP-hard optimization problem under maximum transmission power, minimum data transmission rate requirement, and SIC requirement. For the approximate solution with much lower complexity, we first exploit a quick suboptimal clustering method to assign each user to a subchannel. Given the user clustering result, the optimal power allocation problem is solved in two steps. By employing the Lagrangian multiplier method with Karush–Kuhn–Tucker optimality conditions, the optimal power allocation is calculated for each subchannel. In addition, then, an inter-cluster dynamic programming model is further developed to achieve the overall maximum energy efficiency. The theoretical analysis and simulations show that the proposed schemes achieve a significant energy efficiency gain compared with existing methods.

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