scholarly journals Cross-Cycled Uplink Resource Allocation over NB-IoT

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7948
Author(s):  
Ya-Ju Yu ◽  
Yu-Hsiang Huang ◽  
Yuan-Yao Shih
Keyword(s):  
Resource Allocation ◽  
Low Cost ◽  
Search Space ◽  
Resource Allocation Algorithm ◽  
Search Spaces ◽  
Half Duplex ◽  
Shared Channel ◽  
Radio Resources ◽  
The Cross ◽  
Allocation Algorithms

Before each user equipment (UE) can send data using the narrowband physical uplink shared channel (NPUSCH), each UE should periodically monitor a search space in the narrowband physical downlink control channel (NPDCCH) to decode a downlink control indicator (DCI) over narrowband Internet of Things (NB-IoT). This monitoring period, called the NPDCCH period in NB-IoT, can be flexibly adjusted for UEs with different channel qualities. However, because low-cost NB-IoT UEs operate in the half-duplex mode, they cannot monitor search spaces in NPDCCHs and transmit data in the NPUSCH simultaneously. Thus, as we observed, a percentage of uplink subframes will be wasted when UEs monitor search spaces in NPDCCHs, and the wasted percentage is higher when the monitored period is shorter. In this paper, to address this issue, we formulate the cross-cycled resource allocation problem to reduce the consumed subframes while satisfying the uplink data requirement of each UE. We then propose a cross-cycled uplink resource allocation algorithm to efficiently use the originally unusable NPUSCH subframes to increase resource utilization. Compared with the two resource allocation algorithms, the simulation results verify our motivation of using the cross-cycled radio resources to achieve massive connections over NB-IoT, especially for UEs with high channel qualities. The results also showcase the efficiency of the proposed algorithm, which can be flexibly applied for more different NPDCCH periods.

scholarly journals Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds

2020 ◽  
Vol 11 (1) ◽  
pp. 149
Author(s):  
Wu-Chun Chung ◽  
Tsung-Lin Wu ◽  
Yi-Hsuan Lee ◽  
Kuo-Chan Huang ◽  
Hung-Chang Hsiao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Data Stream ◽  
Limited Attention ◽  
Heterogeneous Servers ◽  
Resource Allocation Algorithm ◽  
System Utilization ◽  
Allocation Algorithms ◽  
Considerable Resource ◽  
Heterogeneous Resources

Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing.

scholarly journals Resource Allocation Algorithms for Indoor D2D Communication

2020 ◽  
Vol 9 (5) ◽  
pp. 1292-1296
Keyword(s):  
Resource Allocation ◽  
Base Station ◽  
D2d Communication ◽  
Throughput Maximization ◽  
Resource Allocation Algorithm ◽  
Main Challenge ◽  
Device To Device ◽  
Efficient Resource ◽  
Optimal Resource ◽  
Allocation Algorithms

Device to Device (D2D) communication in cellular networks is defined as direct communication between two mobile users without traversing the data through the base station (BS). Indoor D2D communication refers to transmission between two users within a building or in a closed space. Resource allocation is a plan for using available resources efficiently and the resources are allocated for optimal functioning of the D2D network. The algorithms for optimizing D2D network is characterized by the parameters like matching network, noise, throughput maximization and few more. In this work, our aim is to develop resource allocation algorithms for indoor D2D communication. An efficient resource allocation algorithm for device to device communication and a suitable frequency allocation technique in order to avoid call blockage should be designed. The main challenge in this work is to allocate resources to D2D users without affecting cellular users efficiency. These optimal resource allocation works efficiently and also adapt to time and location variation. The process involved in each algorithm is elaborated.

Radio Resource Allocation Algorithm Based on Bargaining Game Theory for LTE System

2014 ◽  
Vol 644-650 ◽  
pp. 1527-1530
Author(s):  
Han Yin ◽  
Duo Zhang
Keyword(s):  
Game Theory ◽  
Resource Allocation ◽  
Rapid Development ◽  
Bargaining Game ◽  
Radio Resource Allocation ◽  
Radio Resource ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Radio Resources ◽  
Bargaining Game Theory

With the rapid development of wireless communication technologies, users could get many kinds of services and applications now. And as the number of users and the amount of traffic are growing, the contradiction between the infinite demand of users and the finite radio resources is getting increasingly apparent. According to this situation, this paper propose a radio resource allocation algorithm based on bargaining game theory for fourth generation long term evolution (LTE) system, with which the network could balance the situations of users in different classes and enhance the utility of users. The simulation results show that the proposed algorithm could allocate the radio resources efficiently and provide users with higher utility.

scholarly journals A Joint Modulation-Coding Scheme and Resource Allocation in LTE Uplink

2020 ◽  
Vol 26 (5) ◽  
pp. 50-58
Author(s):  
Amado Gutierrez ◽  
Victor Rangel ◽  
Javier Gomez ◽  
Robert M. Edwards ◽  
David H. Covarrubias
Keyword(s):  
Resource Allocation ◽  
Coding Scheme ◽  
Modulation Coding ◽  
Ongoing Work ◽  
Two Factors ◽  
Radio Resources ◽  
Allocation Algorithms ◽  
Resource Allocation Schemes ◽  
Lte Uplink

In Long Term Evolution (LTE) Resource Allocation Algorithms (RAAs) are an area of work where researchers are seeking to optimize the efficient use of scarce radio resources. The selection of an optimal Modulation and Coding Scheme (MCS) that allows LTE to adapt to channel conditions is a second area of ongoing work. In the wireless part of LTE, these two factors, RAA and MCS selection, are the most critical in optimization. In this paper, the performance of three resource allocation schemes is compared, and a new allocation scheme, Average MCS (AMCS) allocation, is proposed. AMCS is seen to outperform both “Minimum MCS (MMCS)” and “Average Signal to Interference and Noise Ratio MCS (SINR AMCS)” in terms of improvements to LTE Uplink (UL) performance. The three algorithms were implemented in the Vienna LTE-A Uplink Simulator v1.5.

Low-Complexity Resource Allocation Algorithm for Multicell OFDMA System

2013 ◽  
Vol E96.B (5) ◽  
pp. 1218-1221 ◽  
Author(s):  
Qingli ZHAO ◽  
Fangjiong CHEN ◽  
Sujuan XIONG ◽  
Gang WEI
Keyword(s):  
Resource Allocation ◽  
Low Complexity ◽  
Ofdma System ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm

Resource Allocation in Cloud using Multi Bidding Model with User Centric Behavior Analysis

2020 ◽  
Vol 13 (5) ◽  
pp. 1008-1019
Author(s):  
N. Vijayaraj ◽  
T. Senthil Murugan
Keyword(s):  
Resource Allocation ◽  
Behavior Analysis ◽  
User Behavior ◽  
Low Cost ◽  
Cloud Services ◽  
Online Auction ◽  
Analysis Model ◽  
Efficient Resource ◽  
User Centric ◽  
Cloud Resources

Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented & system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.

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