Fairness-Aware Throughput Maximization Over Cognitive Heterogeneous NOMA Networks for Industrial Cognitive IoT

Xu, Lei; Yin, Weixin; Zhang, Xiaoling; Yang, Yuwang

doi:10.1109/tcomm.2020.2992720

Cited by 37 publications

(23 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Joint sub-channel and power management for downlink heterogeneous NOMA networks were investigated in [28][29][30]. In addition, the problems of resources in cognitive NOMA networks to increase the spectral efficiency in NOMA were investigated recently in [31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Sum Rate Maximization-based Fair Power Allocation in Downlink NOMA Networks

Abd‐Elnaby¹

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Non-orthogonal multiple access (NOMA) has been seen as a promising technology for 5G communication. The performance optimization of NOMA systems depends on both power allocation (PA) and user pairing (UP). Most existing researches provide sub-optimal solutions with high computational complexity for PA problem and mainly focuses on maximizing the sum rate (capacity) without considering the fairness performance. Also, the joint optimization of PA and UP needs an exhaustive search. The main contribution of this paper is the proposing of a novel capacity maximizationbased fair power allocation (CMFPA) with low-complexity in downlink NOMA. Extensive investigation and analysis of the joint impact of signal to noise ratio (SNR) per subcarrier and the channel gains of the paired users on the performance of NOMA in terms of the capacity and the user fairness is presented. Next, a closed-form equation for the power allocation coefficient of CMFPA as a function of SNR, and the channel gains of the paired users is provided. In addition, to jointly optimize UP and PA in NOMA systems an efficient low-complexity UP (ELCUP) method is proposed to be incorporated with the proposed CMFPA to compromise the proposed joint resource allocation (JRA). Simulation results demonstrate that the proposed CMFPA can improve the capacity and fairness performance of existing UP methods, such as conventional UP, and random UP methods. Furthermore, the simulation results show that the proposed JRA significantly outperforms the existing schemes and gives a near-optimal performance.

show abstract

Section: Related Workmentioning

confidence: 99%

Sum Rate Maximization-based Fair Power Allocation in Downlink NOMA Networks

Abd‐Elnaby¹

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Note that (26) provides a tight lower bound as Proposition 1. Furthermore, it converges to KKT point of the original problem using an iterative algorithm since it provides a sequence of non-decreasing objective function value [27], [28]. Therefore, we focus on the lower bound of original problem.…”

Section: B Subchannel Allocationmentioning

confidence: 99%

Joint Association and Resource Allocation for Multi-Hop Integrated Access and Backhaul (IAB) Network

Lim¹,

Lee²,

Kwon³

et al. 2021

Preprint

View full text Add to dashboard Cite

Integrated access and backhaul (IAB) network is envisioned as a novel network architecture for increasing the network capacity and coverage. To facilitate the IAB network, the appropriate methods of wireless link association and resource management are required. In this paper, we investigate the joint optimization problem of association and resource allocation in terms of subchannel and power for IAB network. In particular, we handle the association and resource allocation problems for wireless backhaul and access links considering multi-hop backhauling. Since the optimization problem for IAB network is formulated as a mixed integer non-linear programming (MINLP), we divide it into three subproblems for association, subchannel allocation, and power allocation, respectively, and these subproblems are solved alternatively to obtain a local optimal solution. For the association problem, we adopt the Lagrangian duality approach to configure the backhaul and access links and successive convex approximation (SCA) approach is used to solve the subchannel and power allocation problems efficiently. Simulation results demonstrate that the proposed algorithm achieves better performance than single-hop backhauling based network and enhances the capacity and coverage by configuring the multi-hop backhauling.

show abstract

“…This section 268 | P a g e www.ijacsa.thesai.org specifically discusses the issues about interoperability in IoT. The recent work carried out by Xu et al [14] has presented a technique for improving throughput performance, specifically concerning industrial IoT systems using cognitive networks. The study has used a convex optimization process in order to address resource allocation issues in industrial IoT.…”

Section: Existing Approachesmentioning

confidence: 99%

ICS: Interoperable Communication System for Inter-Domain Routing in Internet-of-Things

Bhavana¹,

N²

2021

IJACSA

View full text Add to dashboard Cite

The Internet-of-Things consists of heterogeneous smart appliances connected by global network with selfconfiguring capabilities requiring interoperable communication schemes while performing inter-domain routing. A review of existing interoperable approaches shows that there is still a large scope of improving IoT interoperability. The proposed system introduces Interoperable Communication System (ICS) by developing a novel inter-domain routing in IoT using two schemes. Preemptive and Non-Preemptive Communication scheme targets mainly emergency-based routing, which demands faster transmission, and dedicated transmission, demanding accountability in communication. A simulation study carried out for the proposed system shows that it offers approximately 90% reduced delay, 57% increased packet delivery ratio, and 98% faster processing time when compared with existing approaches to accomplish interoperability in IoT.

show abstract

Fairness-Aware Throughput Maximization Over Cognitive Heterogeneous NOMA Networks for Industrial Cognitive IoT

Cited by 37 publications

References 42 publications

Sum Rate Maximization-based Fair Power Allocation in Downlink NOMA Networks

Sum Rate Maximization-based Fair Power Allocation in Downlink NOMA Networks

Joint Association and Resource Allocation for Multi-Hop Integrated Access and Backhaul (IAB) Network

ICS: Interoperable Communication System for Inter-Domain Routing in Internet-of-Things

Contact Info

Product

Resources

About