一种改进的LTE-advanced网络中H2H和M2M通信中时延敏感设备下行分组调度算法

Q3 Business, Management and Accounting
S. Radhakrishnan, S. Neduncheliyan, K. Thyagharajan
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引用次数: 0

摘要

在当今的无线环境中,对提高实时数据业务的数据速率和QoS的需求不断增加。文献中可用的调度方案在eNodeB处产生大量调度开销。因此,这项工作推荐了一种在eNodeB处具有降低的调度复杂性的节能、QoS感知的调度器,用于延迟敏感数据的传输。调度问题由传输数据的信道所需的各种资源的所有可能组合的加权传输速率的增益组成。已经开发了一种在eNodeB处的改进的贪婪算法来动态地将资源分配给用户设备(UE)以用于实时数据的传输。使用离散小波变换对算法的输入视频帧进行压缩。研究结果表明,所提出的调度算法大大提高了小区边缘用户的覆盖率。将这种贪婪调度器的性能与文献中其他两种著名的调度器,即LOG规则和EXP规则进行了比较。该调度算法在实时数据传输的QoS参数方面优于其他方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved downlink packet scheduling algorithm for delay sensitive devices in both H2H and M2M communications in LTE-advanced networks
The demand for increased data rate with improved QoS for real-time data traffic is ever increasing in the present day wireless environment. The scheduling schemes available in the literature incur lot of scheduling overhead at the eNodeB. Therefore, this work recommends an energy efficient, QoS-aware scheduler with reduced scheduling complexity at the eNodeB, for transmission of delay sensitive data. The scheduling problem is composed as a gain of weighted transmission rates of all possible combinations of various resources required by the channel for transmitting data. An improved greedy algorithm at the eNodeB, has been developed to allocate the resources dynamically to the user equipments (UEs) for the transmission of real-time data. The input video frames to the algorithm are compressed using discrete wavelet transform. The results of this research work show that the proposed scheduling algorithm greatly improves the coverage of the cell edge users. The performance of this greedy scheduler is compared with other two notable schedulers in the literature namely LOG rule and EXP-rule. This scheduling algorithm outperforms the other schemes in terms of QoS parameters for real-time data transmission.
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来源期刊
International Journal of Enterprise Network Management
International Journal of Enterprise Network Management Business, Management and Accounting-Management of Technology and Innovation
CiteScore
0.90
自引率
0.00%
发文量
28
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