基于比例公平调度算法的认知无线电系统性能分析
Performance Analysis of Cognitive Radio System Based on Proportional Fair Scheduling Algorithm
DOI: 10.12677/CSA.2018.84049, PDF,    国家自然科学基金支持
作者: 崔 盼*:兰州理工大学计算机与通信学院,甘肃 兰州;黎锁平:兰州理工大学理学院,甘肃 兰州
关键词: 认知无线电比例公平算法时延吞吐量Cognitive Radio Proportional Fairness Algorithm Delay Throughput
摘要: 本文为进一步提高频谱资源的利用率,解决LTE系统中下行传输过程中比例公平算法下未被调度的用户问题,提出将认知技术融入比例公平调度算法中,因此,这些未被调度的用户就具有发现“频谱空洞”并合理利用的能力,从而有效的利用频谱资源,同时提高系统的吞吐量。其次,由于认知用户(secondary user, SU)不仅要具有认知能力还要具有可重构性这一特性,即当主用户(primary user, PU)占用频谱时认知用户该做出怎样的反应,继续等待还是寻找新的子载波,针对这一问题,本文讨论了两种情况下的时延并给出判决依据。
Abstract: In order to further improve the utilization of spectrum resources and solve the problem of unscheduled users under the proportional fairness algorithm in the downlink transmission process of LTE systems, this paper proposes to integrate cognitive technologies into the proportional fair scheduling algorithm. Therefore, these unscheduled users have the ability to discover “spectral voids” and make reasonable use of them, thereby effectively utilizing spectrum resources and improving system throughput. Second, because the secondary user (SU) must not only have the cognitive ability but also the reconstruction character, that is how the secondary user should respond when the primary user (PU) occupies the spectrum, continuing to wait or finding a new carrier. In response to this problem, this paper discusses the delay in two cases and gives the decision basis.
文章引用:崔盼, 黎锁平. 基于比例公平调度算法的认知无线电系统性能分析[J]. 计算机科学与应用, 2018, 8(4): 448-454. https://doi.org/10.12677/CSA.2018.84049

参考文献

[1] Issariyakul, T., Pillutla, L.S. and Krishnamurthy, V. (2009) Tuning Radio Resource in an Overlay Cognitive Radio Network for TCP: Greed Isn’t Good. IEEE Communications Magazine, 47, 57-63. [Google Scholar] [CrossRef
[2] Srinivasa, S. and Jafar, S.A. (2006) The Throughput Potential of Cognitive Radio: A Theoretical Perspective. IEEE Fortieth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 29 October-1 November 2006, 221-225.
[3] Shalaby, M., Shokair, M. and Abdo, Y.S.E. (2014) Enhancement of Geometry and Throughput in LTE Femtocells Cognitive Radio Networks. Wireless Personal Communications, 77, 649-659. [Google Scholar] [CrossRef
[4] Costa, M. and Ephremides, A. (2016) Energy Efficiency versus Performance in Cognitive Wireless Networks. IEEE Journal on Selected Areas in Communications, 34, 1336-1347. [Google Scholar] [CrossRef
[5] Esmaeelzadeh, V., Hosseini, E.S., Berangi, R., et al. (2016) Modeling of Rate-Based Congestion Control Schemes in Cognitive Radio Sensor Networks. Ad Hoc Networks, 36, 177-188. [Google Scholar] [CrossRef
[6] Nsiri, B., Mallouki, N., Mhatli, S., et al. (2015) Modeling and Performance Evaluation of Novel Scheduling Algorithm for Downlink LTE Cellular Network. Wireless Personal Communications, 83, 2303-2316. [Google Scholar] [CrossRef
[7] Zhong, X., Qin, Y. and Li, L. (2014) Capacity Analysis in Multi-Radio Mul-ti-Channel Cognitive Radio Networks: A Small World Perspective. Wireless Personal Communications, 79, 2209-2225. [Google Scholar] [CrossRef