Energy-Efficient Resource and Power Allocation for Underlay Multicast Device-to-Device Transmission

Jiang, Fan; Wang, Honglin; Ren, Hao; Xu, Shuai

doi:10.3390/fi9040084

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…Hu et al use [16] the EERA in time-sharing multi -user system with hybrid EH transmitter, the traditional object is transformed into optimization problems in network. Jiang et al [17] present a EERA for underlay multi-cast D2D transmission, they also build the optimization for network in EERA. In 2021, Chen and Liu [18] propose a energy-efficient task offloading and resource allocation in MEN, while the DRL verifies a good performance in a HPC environment for the network of affective computing in routers and switches.…”

Section: Related Workmentioning

confidence: 99%

“…Table 1 is the notations in this work. Most existing ABCI perform networking EERA only and get a good performance on linear iterative prediction in EERA [13,[17][18]. However, they may fail on non-linear NLIPS in EERA of MMP, where informative nodes in the network tend to be out of the emotion state monitoring and affective computing is needed.…”

Section: Related Workmentioning

confidence: 99%

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The Energy-Efficient Resource Allocation of Multi-Modal Perception for Affective Brain-Computer Interactions Based on Non-Linear Iterative Prediction Scheme

Zhou¹,

Zhou²,

Chen³

et al. 2023

Journal of Internet Technology

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<p>For the whole environmental settings in this research, the conventional affective brain-computer interactions can not build a good performance on energy-efficient resource of network’s forwarding ports and routing paths due to its poor allocation function of cognitive radio networks, based on the novel interactive networking architecture, the model of non-linear iterative prediction scheme in interaction was successively proposed. This research proposes a modified LSTM algorithm with a structure of non-linear iterative in complexity prediction, joins the multiple k modes selection and multi-agent systems, maximizes EERA of forwarding and routing while maintaining the communication quality. Firstly, considering whether this affective brain-computer interactions need the networking communication in system. Secondly, adjusting the forwarding and routing factors of energy-efficient resource allocation by selecting the best optimal energy-efficient resource for the links through the non-linear iterative prediction in a multi-modal perception. The simulation results show that compared with the other models and algorithms, the proposed scheme for affective brain-computer interactions, which has a nice performance on a higher EERA and channel utilization of a networking architecture of brain-computer interactions.</p> <p> </p>

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

The Energy-Efficient Resource Allocation of Multi-Modal Perception for Affective Brain-Computer Interactions Based on Non-Linear Iterative Prediction Scheme

Zhou¹,

Zhou²,

Chen³

et al. 2023

Journal of Internet Technology

View full text Add to dashboard Cite

show abstract

“…The UE energy consumption is minimized by optimizing the task assignment with a graph matching policy, which can achieve good D2D task assignments. However, the energy-efficiency of the D2D clusters is not considered in the study [140], since it mainly deals with the D2D crowd task assignment problem [160]. In addition, in order to make the proposed framework practical, scenarios with changing D2D connections need to be considered in future research.…”

Section: Energy Consumption Minimizationmentioning

confidence: 99%

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

et al. 2019

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Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute-and data-intensive services with low latency. Growing service demands by the end devices may overwhelm MEC installations, while cost constraints limit the increases of the installed MEC computing and data storage capacities. At the same time, the ever increasing computation capabilities and storage capacities of mobile end devices are valuable resources that can be utilized to enhance the MEC. This article comprehensively surveys the topic area of device-enhanced MEC, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices. We classify the device-enhanced MEC mechanisms into mechanisms for computation offloading and mechanisms for caching. We further subclassify the offloading and caching mechanisms according to the targeted performance goals, which include throughput maximization, latency minimization, energy conservation, utility maximization, and enhanced security. We identify the main limitations of the existing device-enhanced MEC mechanisms and outline future research directions. INDEX TERMS Caching, computation offloading, device-to-device (D2D) communication, mobile edge computing (MEC). I. INTRODUCTION A. MOTIVATION

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“…However, it was observed in [23] that the used method, Genetic Algorithm (GA), was unable to reduce the reuse frequency, even though channel allocation was carried out. By studying common allocation systems in mobile networks [24], reference [25] developed a unique centralized channel allocation which could effectively use scarce resource bandwidth. This model contains the so-called geographical model, which is essentially a group of contiguous, non-overlapping cells, each of which has a hexagonal form and which together form a parallelogram.…”

Section: Related Workmentioning

confidence: 99%

Sustainable Communication Systems: A Graph-Labeling Approach for Cellular Frequency Allocation in Densely-Populated Areas

2019

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The need for smart and sustainable communication systems has led to the development of mobile communication networks. In turn, the vast functionalities of the global system of mobile communication (GSM) have resulted in a growing number of subscribers. As the number of users increases, the need for efficient and effective planning of the “limited” frequency spectrum of the GSM is inevitable, particularly in densely-populated areas. As such, there are ongoing discussions about frequency (channel) allocation methods to resolve the challenges of channel allocation, which is a complete NP (Nondeterministic Polynomial time) problem. In this paper, we propose an algorithm for channel allocation which takes into account soft constraints (co-channel interference and adjacent channel interference). By using the Manhattan distance concept, this study shows that the formulation of the algorithm is correct and in line with results in the literature. Hence, the Manhattan distance concept may be useful in other scheduling and optimization problems. Furthermore, this unique concept makes it possible to develop a more sustainable telecommunication system with ease of connectivity among users, even when several subscribers are on a common frequency.

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Energy-Efficient Resource and Power Allocation for Underlay Multicast Device-to-Device Transmission

Cited by 8 publications

References 17 publications

The Energy-Efficient Resource Allocation of Multi-Modal Perception for Affective Brain-Computer Interactions Based on Non-Linear Iterative Prediction Scheme

The Energy-Efficient Resource Allocation of Multi-Modal Perception for Affective Brain-Computer Interactions Based on Non-Linear Iterative Prediction Scheme

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

Sustainable Communication Systems: A Graph-Labeling Approach for Cellular Frequency Allocation in Densely-Populated Areas

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