School of Mechanical, Electrical and Computer Engineering, Federal University of Goiás, Goiânia, Goiás, Brazil.
PLoS One. 2019 Nov 13;14(11):e0224883. doi: 10.1371/journal.pone.0224883. eCollection 2019.
In this paper, we propose a predictive Generalized OBF (Orthonormal Basis Functions)-Fuzzy flow control scheme for the 5G downlink by deriving an expression for the optimal control rate of the traffic sources considering minimization of data delay and a minimum traffic rate to the users. The adaptive GOBF-Fuzzy model is applied to predict queueing behavior in initial 5G systems. To this end, we propose to obtain orthonormal basis functions related to the real traffic flows via multifractal modeling, inserting these functions into the fuzzy model trained with the LMS (Least Mean Square) adaptive algorithm. Simulations of a F-OFDM (Filtered Orthogonal Frequency Division Multiplexing) based 5G Downlink are carried out to validate the proposed flow control algorithm. Comparisons with other predictive control schemes in the literature prove the efficiency of the adaptive GOBF-fuzzy based control in enhancing the performance of the system downlink as well as guaranteeing some QoS (Quality of Service) parameters.
在本文中，我们通过推导考虑最小化数据延迟和用户最小流量的最优控制率，为 5G 下行链路提出了一种预测广义 OBF（正交基函数）-模糊流量控制方案。自适应 GOBF-模糊模型用于预测初始 5G 系统中的排队行为。为此，我们建议通过分形建模获得与实际流量相关的正交基函数，然后将这些函数插入到使用 LMS（最小均方）自适应算法训练的模糊模型中。基于 F-OFDM（滤波正交频分复用）的 5G 下行链路的仿真验证了所提出的流量控制算法。与文献中的其他预测控制方案的比较证明了自适应 GOBF-模糊控制在提高系统下行链路性能以及保证某些 QoS（服务质量）参数方面的有效性。
