Optimal power allocation for NOMA-enabled D2D communication with imperfect SIC decoding

Yu, Shanshan; Khan, Wali Ullah; Zhang, Xiaoqing; Liu, Ju

doi:10.1016/j.phycom.2021.101296

Cited by 36 publications

(20 citation statements)

References 29 publications

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“…erefore, this technology has become the focus of any artificial intelligence company, and it is also very necessary for the governments of many countries/regions in the world [18]. In addition, researchers are also focusing on the modernization of other scientific fields such as artificial intelligence and information technology [19][20][21][22][23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

Ahmad¹,

Ullah

Khan

et al. 2021

Journal of Healthcare Engineering

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Object detection plays a vital role in the fields of computer vision, machine learning, and artificial intelligence applications (such as FUSE-AI (E-healthcare MRI scan), face detection, people counting, and vehicle detection) to identify good and defective food products. In the field of artificial intelligence, target detection has been at its peak, but when it comes to detecting multiple targets in a single image or video file, there are indeed challenges. This article focuses on the improved K-nearest neighbor (MK-NN) algorithm for electronic medical care to realize intelligent medical services and applications. We introduced modifications to improve the efficiency of MK-NN, and a comparative analysis was performed to determine the best fuse target detection algorithm based on robustness, accuracy, and computational time. The comparative analysis is performed using four algorithms, namely, MK-NN, traditional K-NN, convolutional neural network, and backpropagation. Experimental results show that the improved K-NN algorithm is the best model in terms of robustness, accuracy, and computational time.

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Section: Introductionmentioning

confidence: 99%

Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

Ahmad¹,

Ullah

Khan

et al. 2021

Journal of Healthcare Engineering

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“…This section discusses the state-of-the-art works in the field of resource allocation in D2D communication. Various researchers 2,5,8,[16][17][18][19][20][21][22] across the globe have given different algorithmic solutions to achieve efficient resource allocation in D2D communication. Chen et al 18 proposed a resource allocation scheme for D2D communication with OFDMA as an access scheme in two-tier heterogeneous networks.…”

Section: Related Workmentioning

confidence: 99%

SARAS: Secure resource allocation scheme for NOMA‐based device‐to‐device communication using coalition game and cognitive radio

Gupta

Tanwar

2022

Int J Communication

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Summary Device‐to‐device (D2D) is a fifth‐generation (5G) network's key technology which allows devices in proximity to communicate one‐to‐one without a base station (BS). Moreover, it ameliorates channel gain, communication latency, energy efficiency, and spectral efficiency. However, mitigating interference (D2D and cellular mobile users [CMUs]) and eavesdropper effect (at D2D links) is a prime concern in D2D communication. These issues can be resolved with an efficient allocation of radio resources. Cognitive radio (CR) and non‐orthogonal multiple access (NOMA) are viable solutions to resolve the aforementioned issues. Motivated by the aforementioned discussion, in this paper, we propose a joint CR and NOMA‐based scheme, that is, SARAS for secure resource allocation in D2D communication. It aims to maximize the overall sum rate and secrecy capacity of D2D users with assured quality of service (QoS) and quality of experience (QoE). CR is used for pairing between strong and weak D2D users (i.e., D2D pair) and NOMA's successive interference cancelation (SIC) technique helps to reduce the interference effect between cellular and D2D users. We used a coalition game for efficient resource utilization, which transfers D2D users from one coalition to another based on the preference order and channel conditions. This landed D2D users in the best coalition having favorable channel conditions. We have executed the proposed SARAS scheme over the MATLAB simulation tool, considering a varying number of D2D and CMUs. Simulation results show that the proposed scheme outperforms the traditional OFDMA, random, and nearest approaches concerning parameters such as sum rate and secrecy capacity.

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“…We assume that all the devices in the network is equipped with omni-directional antenna and the channel state information is also available [36]. We also assume that the channels are independent and identical Raleigh fading [37], [38]. The ST transmits data to both IoT users in the presence of user-k which is the primary receiver (PR).…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Fair Power Allocation in Cooperative Cognitive Systems under NOMA Transmission for Future IoT Networks

Ali¹,

Khan²,

Sidhu³

et al. 2021

Preprint

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This work aims to achieve fairness among secondary users (SUs) in IoT cooperative NOMA-based CR transmission. We design a power allocation algorithm for secondary IoT network that ensures protection of primary system. Further, an independent battery constraint at each node is considered, and power gap among transmissions of two NOMA users is applied for successive interference cancellation. Our extensive simulation results show that the proposed framework provides excellent performance and for sufficient available transmission power perfect fairness is achieved in the rate of both the users.

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Optimal power allocation for NOMA-enabled D2D communication with imperfect SIC decoding

Cited by 36 publications

References 29 publications

Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

SARAS: Secure resource allocation scheme for NOMA‐based device‐to‐device communication using coalition game and cognitive radio

Fair Power Allocation in Cooperative Cognitive Systems under NOMA Transmission for Future IoT Networks

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