Evolutionary Multi-Objective Optimization Algorithm for Resource Allocation Using Deep Neural Network in 5G Multi-User Massive MIMO

Purushothaman, K. E.; Nagarajan, V.

doi:10.1080/00207217.2020.1843715

Cited by 13 publications

(10 citation statements)

References 28 publications

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“…In [ 144 ], resource allocation for multi-users in a 5G massive-MIMO (mMIMO) was executed through a deep neural network (DNN). In the first phase, the unbiased functions were enhanced through the Multi-objective Sine Cosine algorithm (MOSCA).…”

Section: Discussionmentioning

confidence: 99%

Resource Allocation Schemes for 5G Network: A Systematic Review

Kamal

Raza

Alam

et al. 2021

Sensors

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Fifth-generation (5G) communication technology is intended to offer higher data rates, outstanding user exposure, lower power consumption, and extremely short latency. Such cellular networks will implement a diverse multi-layer model comprising device-to-device networks, macro-cells, and different categories of small cells to assist customers with desired quality-of-service (QoS). This multi-layer model affects several studies that confront utilizing interference management and resource allocation in 5G networks. With the growing need for cellular service and the limited resources to provide it, capably handling network traffic and operation has become a problem of resource distribution. One of the utmost serious problems is to alleviate the jamming in the network in support of having a better QoS. However, although a limited number of review papers have been written on resource distribution, no review papers have been written specifically on 5G resource allocation. Hence, this article analyzes the issue of resource allocation by classifying the various resource allocation schemes in 5G that have been reported in the literature and assessing their ability to enhance service quality. This survey bases its discussion on the metrics that are used to evaluate network performance. After consideration of the current evidence on resource allocation methods in 5G, the review hopes to empower scholars by suggesting future research areas on which to focus.

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Section: Discussionmentioning

confidence: 99%

Resource Allocation Schemes for 5G Network: A Systematic Review

Kamal

Raza

Alam

et al. 2021

Sensors

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“…5) Fairness in resource allocation: Resource allocation problems (RAPs) [156] are ubiquitous in many areas, such as task scheduling [157], [158], emergency service allocation [159], [160] and Cloud service allocation [44], [43]. In recent decades, equitable allocation of resources that ensures equitable distribution of resources to users [39], [40], [41], has become a popular research topic.…”

Section: Fairness In Multi-objective Optimizationmentioning

confidence: 99%

“…By contrast, the fairness considered in optimization is to ensure the equity among the individuals (or groups) and there is no concern with the sensitive attributes. Therefore, fairness in optimization may lie in all aspects influencing the optimization process, such as the fair-sampling [145], [146], the design of fairness-aware objective [36], fairness between the preferences from DMs [150], [154], and fair-outcomes [158], [160], [177].…”

Section: Differences Between Fairness In Optimization and Machine Lea...mentioning

confidence: 99%

Towards Fairness-Aware Multi-Objective Optimization

Yu¹,

Ma²,

Du³

et al. 2022

Preprint

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Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in real life, such as fair resource allocation problems and datadriven multi-objective optimization problems. This paper aims to illuminate and broaden our understanding of multi-objective optimization from the perspective of fairness. To this end, we start with a discussion of user preferences in multi-objective optimization and then explore its relationship to fairness in machine learning and multi-objective optimization. Following the above discussions, representative cases of fairness-aware multiobjective optimization are presented, further elaborating the importance of fairness in traditional multi-objective optimization, data-driven optimization and federated optimization. Finally, challenges and opportunities in fairness-aware multi-objective optimization are addressed. We hope that this article makes a small step forward towards understanding fairness in the context of optimization and promote research interest in fairness-aware multi-objective optimization.

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“…In the face of the multiobjective sinusoidal algorithm (MOSCA), the objective function is optimized [ 1 ]. The indexes of the optimized objective function are data rate, signal-to-interference-noise ratio (SINR), power consumption, and energy efficiency, and then the optimized objective function is allocated to a neural network for resource allocation [ 2 ]. It expounds a new deep neural network convolution layer-variable convolution (vConv) layer, which learns the kernel length of data adaptively by its own cycle to realize the motif recognition of data sets with high throughput [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Planning of Sports Competition Based on Deep Neural Network

Jin

2022

Computational Intelligence and Neuroscience

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Physical education curriculum has been paid more and more attention by teachers and parents, and having a healthy body is the foundation. School sports competition is also more and more concerned by major researchers, and scholars have produced in-depth research and analysis of sports competition results prediction because prediction results can better let teachers carry out appropriate sports training for students, so as to achieve the best learning effect. The construction of the prediction model and whether the performance and universality of the model after construction are suitable for predicting sports competitions have also become a major research point. Deep neural network is a complex network method to analyze the structure of the human brain, which plays a core role in the field of sports planning and performance prediction and can know the future performance of athletes or students in advance in sports competitions. This paper establishes the autoregressive summation model prediction model, the complex neural network prediction model, and the improved complex neural network prediction model. It is concluded that only the improved BP neural network model has a remarkable effect on performance prediction, and the prediction value obtained by this prediction model can reduce the systematic error of prediction, so that it can better infer the performance prediction of sports competitions in China and plan which sports events are suitable for which prediction model.

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Evolutionary Multi-Objective Optimization Algorithm for Resource Allocation Using Deep Neural Network in 5G Multi-User Massive MIMO

Cited by 13 publications

References 28 publications

Resource Allocation Schemes for 5G Network: A Systematic Review

Resource Allocation Schemes for 5G Network: A Systematic Review

Towards Fairness-Aware Multi-Objective Optimization

Prediction and Planning of Sports Competition Based on Deep Neural Network

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