Energy Efficient Power Allocation Using Salp Particle Swarm Optimization Model in MIMO–NOMA Systems

Khaleelahmed, Shaik; Venkateswararao, Nandhanavanam

doi:10.1007/s11277-019-06911-z

Cited by 18 publications

(10 citation statements)

References 21 publications

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“…Tables 2 and 3 give the critical state values of the node height and the flow of the connecting pipes. Negative flow in pipe 5 indicates that the power flow is from node 6 to node 5[ 16 , 17 ].…”

Section: State Estimation Model Of the Regional Power Gas-integrated ...mentioning

confidence: 99%

Economic Operation of the Regional Integrated Energy System Based on Particle Swarm Optimization

Chen

2022

Computational Intelligence and Neuroscience

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Aiming at the problems of single planning technology and relatively few resource types in the process of regional comprehensive energy system planning at the current stage, this paper proposes a method based on the construction of regional comprehensive energy system planning and the operation model. Through the integration of the particle swarm optimization algorithm, this method can better realize the optimization and economic operation of the regional comprehensive energy system and build a system optimization mode based on two stages of planning and operation to pursue the optimal configuration of system equipment. Through the simulation algorithm, it is found that the solution time of the traditional basic particle swarm optimization algorithm is 10.49s, while the average solution time of the particle swarm optimization system proposed in this study is 7.93s; the efficiency is increased by 24.4%, and the system operation efficiency is significantly improved, providing theoretical and technical guidance for the economic operation of the regional comprehensive energy system.

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Section: State Estimation Model Of the Regional Power Gas-integrated ...mentioning

confidence: 99%

Economic Operation of the Regional Integrated Energy System Based on Particle Swarm Optimization

Chen

2022

Computational Intelligence and Neuroscience

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“…However, this work was considered only two users; it failed to process multiple users. Khaleel Ahmed and Venkateswara Rao [21] developed the salp particle swarm optimization for power allocation (SPPA) based power among the users of the NOMA. The bit error rate (BER) of SPPA was reduced along with the increment in users.…”

Section: Related Workmentioning

confidence: 99%

Power Allocation Using Multi-Objective Sum Rate Based Butterfly Optimization Algorithm for NOMA Network

2022

IJIES

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In recent times, non-orthogonal multiple access (NOMA) is a growing technique for the future 5G networks to address the incredible evolution of mobile data traffic and radio access issues based on concurrent communication between multiple users. However, the performance of the NOMA is affected due to an inappropriate power allocation to the users, which affects the system's sum rate. In this research, the multi-objective sum rate-based butterfly optimization algorithm (M-SRBOA) is proposed to address the power allocation challenges of the NOMA network. The M-SRBOA is optimized by considering distinct objective functions like sum rate and Rayleigh fading coefficients. Therefore, the developed M-SRBOA method allocates an appropriate power for all users of the NOMA, which increases higher sum rate. The performance of the M-SRBOA method is analyzed using achievable sum rate and outage probability. The existing researches such as equal power allocation (EPA), optimal power allocation (OPA) factors and difference of convex optimization (DCO) are used to justify the efficiency of the M-SRBOA method. The achievable sum rate of M-SRBOA for the SNR of 15 dB with noise variance of 2 is 13.09 bps/Hz, which is high compared to the EPA, OPA and DCO.

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Section: Introductionmentioning

confidence: 99%

“…DPA based metaheuristic optimization methods are iterative methods that increase implementation complexity as well as conventional DPA numerical search methods. For instance, DPA based DE 49 has been compared with GRPA, DPA based PSO 50 has been compared with NGDPA and GRPA, while DPA based GA has been compared with FTPA 51 and GRPA. 52 As can be seen from the related works given above, no comprehensive and realistic comparison of various DPA strategic design methods has yet been published, taking into account the BER, sum-rate capacity, fairness, and distance between the users.…”

Section: Introductionmentioning

confidence: 99%

On The Selection of Power Allocation Strategy in Power Domain Non‐Orthogonal Multiple Access (PD‐NOMA) for 6G and Beyond

Mounir

Mashade

Aboshosha

2021

Trans Emerging Tel Tech

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Non-orthogonal multiple access (NOMA) is an attractive candidate for 6G networks to support ultra-massive machine-type communications (umMTC). Power domain NOMA (PD-NOMA) is the simplest type of NOMA, which assigns a different power level to each user. Power allocation in PD-NOMA can be classified into fixed/dynamic power allocation (FPA/DPA). FPA is simple, but DPA is more suitable for the mobile environment than FPA. However, finding optimum power per each user in DPA is extremely complex. Fortunately, many DPA strategic design methods were introduced in literature as simple suboptimal solutions of DPA. Therefore, DPA strategic design methods and FPA techniques are simple approaches to implement PD-NOMA in 6G and beyond. In literature, no previous work had compared the performances of all DPA strategic design methods, nor FPA techniques, to ease the selection of a simple strategy for PD-NOMA in 6G. Motivated by that, this work compares performances of all DPA strategic design methods as well as FPA techniques, in terms of sum-rate capacity, fairness, and bit error rate (BER). Results showed that the best DPA strategic design method and the best FPA technique have comparable performance.

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Energy Efficient Power Allocation Using Salp Particle Swarm Optimization Model in MIMO–NOMA Systems

Cited by 18 publications

References 21 publications

Economic Operation of the Regional Integrated Energy System Based on Particle Swarm Optimization

Economic Operation of the Regional Integrated Energy System Based on Particle Swarm Optimization

Power Allocation Using Multi-Objective Sum Rate Based Butterfly Optimization Algorithm for NOMA Network

On The Selection of Power Allocation Strategy in Power Domain Non‐Orthogonal Multiple Access (PD‐NOMA) for 6G and Beyond

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