Parameter aware utility proportional fairness scheduling technique in a communication network

Q4 Mathematics
Neeraj Kumar, Anwar Ahmad, Shashank Awasthi, K. Sharma
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引用次数: 4

Abstract

Conventionally, real-time (inelastic) users and non-real-time (elastic) users are scheduled separately. Further, some users may be left without getting any radio resources. In real scenario, such as in high network traffic, less network coverage, resource allocation for all types of users are needed. In the proposed technique, a utility function is defined for each elastic traffic and inelastic traffic type users. Then, it maximises the total utilities by network utility maximisation approach with considering scheduling parameters such as queue length, head of the line delay and experienced channel condition. Rate allocation based on queue awareness improves fairness among elastic traffic users, delay awareness improves QoS performance of inelastic traffic users and channel condition awareness improves throughput of all users. Further, the network utility maximisation approach provides minimum throughput to all users.
通信网络中参数感知实用比例公平调度技术
传统上,实时(非弹性)用户和非实时(弹性)用户是分开调度的。此外,一些用户可能得不到任何无线电资源。在实际场景中,例如在高网络流量、较少网络覆盖的情况下,需要为所有类型的用户分配资源。在所提出的技术中,为每个弹性流量和非弹性流量类型的用户定义了效用函数。然后,它通过网络效用最大化方法,考虑队列长度、线路头延迟和经历的信道条件等调度参数,使总效用最大化。基于队列感知的速率分配提高了弹性流量用户之间的公平性,延迟感知提高了非弹性流量用户的QoS性能,信道条件感知提高了所有用户的吞吐量。此外,网络效用最大化方法为所有用户提供最小吞吐量。
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
23
期刊介绍: IJICA proposes and fosters discussion on all new computing paradigms and corresponding applications to solve real-world problems. It will cover all aspects related to evolutionary computation, quantum-inspired computing, swarm-based computing, neuro-computing, DNA computing and fuzzy computing, as well as other new computing paradigms
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