Clustered-NOMA Based Resource Allocation in Wireless Powered Communication Networks

Na, Zhenyu; Liu, Yue; Wang, Jun; Guan, Mingxiang; Gao, Zihe

doi:10.1007/s11036-020-01585-5

Cited by 15 publications

(3 citation statements)

References 41 publications

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“…We assume that the length of a frame is

T

. Then, energy harvested at

S_{n}

is given by 16

E_{n} goodbreak= μ_{1} \frac{T}{2} italicηα P_{N} {|h_{n}|}^{2},

where

italicηϵ ((), 0, 1)

denotes the energy conversion efficiency.

α

is the power splitting coefficient, and

0 < α < 1

.…”

Section: System Modelmentioning

confidence: 99%

“…However, the number of users in a network or cluster size is one of the important factors required for appropriate decoding at the receiver end. The performance in a NOMA cluster can be improved by taking users with larger channel differences 15,16 . Xu et al 15 proposed a clustering method based on the K‐means algorithm in which the SUs were grouped based on their channel conditions.…”

Section: Introductionmentioning

confidence: 99%

“…The performance in a NOMA cluster can be improved by taking users with larger channel differences. 15,16 Xu et al 15 proposed a clustering method based on the K-means algorithm in which the SUs were grouped based on their channel conditions. The larger the channel gain difference in a cluster, the more the throughput.…”

mentioning

confidence: 99%

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A matching game framework for users clustering and resource allocation with wireless power transfer in a CR‐NOMA network

Das¹,

Khadanga

Rout

2022

Int J Communication

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This paper investigates the resource allocation in a massively deployed user cognitive radio enabled non-orthogonal multiple access (CR-NOMA) network considering the downlink scenario. The system performance deteriorates with the number of users who are experiencing similar channel characteristics from the base station (BS) in NOMA. To address this challenge, we propose a framework for maximizing the system throughput that is based on one-to-one matching game theory integrated with the machine learning technique. The proposed approach is decomposed to solve users clustering and power allocation subproblems. The selection of optimal cluster heads (CHs) and their associated cluster members is based on Gale-Shapley matching game theoretical model with the application of Hungarian method. The CHs can harvest energy from the BS and transfer their surplus power to the primary user (PU) through wireless power transfer. In return, they are allowed to access the licensed band for secondary transmission. The power allocation to the users intended for power conservation at CHs is formulated as a probabilistic constraint, which is then solved by employing the support vector machine (SVM) algorithm. The simulation results demonstrate the efficacy of our proposed schemes that enable the CHs to transfer the residual power while ensuring maximum system throughput. The effects of different parameters on the performance are also studied.

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“…We assume that the length of a frame is