What is it about?

Global traffic in mobile networks is expected to reach 160 exabytes per month by 2025. To cope with the exponential growth of network traffic and enhanced user mobility, operators have deployed small-cell eNodeBs (SC eNBs), which are smaller versions of traditional macrostations. SC eNBs provide denser cells where signal coverage is greater in order to balance the network load. A large-scale network base station (BS) can be jointly installed to form a heterogeneous network (Het Net), in which a dense group of diverse cells is defined as an ultradense network (UDN). Adedoyin and Falowo noted that more than 103 cells are deployed per square kilometer in UDN environments, where the main problem, a low user perception rate, is caused by handover (HO) and HO ping-pong (HPP) between neighboring cells. H2RDC (heuristic handover based on RCC-DTSK-C), a heuristic algorithm based on a highly interpretable deep Takagi–Sugeno–Kang fuzzy classifier, is proposed for suppressing the mobile heterogeneous networks problem of frequent handover and handover ping-pong in the multibase-station scenario. This classifier uses a stack structure between subsystems to form a deep classifier before generating a base station (BS) priority sequence during the handover process, and adaptive handover hysteresis is calculated.

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Why is it important?

Simulation results show that H2 RDC allows user equipment to switch to the best antenna at the optimal time. In high-BS density load and mobility scenarios, the proposed algorithm’s handover success rate is similar to those of classic algorithms such as best connection (BC), self tuning handover algorithm (STHA), and heuristic for handover based on AHP-TOPSIS-FUZZY (H2 ATF). Moreover, the handover rate is 83% lower under H2 RDC than under BC, whereas the handover ping-pong rate is 76% lower We proposed an algorithm to optimize HO in a heterogeneous network. A multimodule TSK fuzzy system was used to train the adaptive HO hysteresis parameter to reduce frequent HO and HPP in a UDN network. Experimental results showed that compared with algorithms such as BC, STHA, and H2 ATF, the proposed H2RDC algorithm can provide the lowest HO times and HPP rates. HO rates for H2 RDC were found to be similar to those obtained with advanced algorithms, and in a network simulation scenario, the proposed H2 RDC algorithm provided the highest user perception rate.


The present article analyzes the regularity strategy of HO in an LTE-5G heterogeneous network scenario. Because HO is the primary manifestation of user mobility, its success can be used to evaluate the mobility performance of the entire heterogeneous network. Thus, this article focuses on HO and HPP problems that occur frequently in heterogeneous networks, based on a highly interpretable depth. A heuristic algorithm is proposed based on the Takagi–Sugeno–Kang (TSK) fuzzy classifier (heuristic HO based on RCC-DTSK-C, H2RDC). The three major contributions of our work are as follows. (i) In the multibase-station (eNodeBs) scenario, neglecting to use priority-based HO schemes will lead to many HOs and affect the quality of service provided. To solve this problem, heuristic-based analysis methods, driven by heterogeneous networks and traffic models, reduce the number of HOs and the amount of HPP, thus reducing both transmission in the signaling network and unnecessary antenna HO. (ii) In an LTE or 5G heterogeneous network, the stack structure between subsystems is used to form a deep classifier, and the BS priority sequence is generated during the HO process to calculate the adaptive HO hysteresis. These characteristics allow the optimal HO time and antenna to be robustly defined. (iii) The method reduces the number of signal HOs and the amount of HPP. In cases of high-BS density loads and mobility, the HO and HPP rates are reduced by 83% and 76%, respectively, and the HO failure rate index is better constructed. Because a new type of control system is needed to increase the perception rate, we propose a heuristic algorithm based on a highly interpretable deep TSK fuzzy classifier.

Pingping Xiao
Changchun Guanghua University

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This page is a summary of: A novel heuristic for handover priority in mobile heterogeneous networks based on a multimodule Takagi–Sugeno–Kang fuzzy system, ETRI Journal, May 2022, Wiley, DOI: 10.4218/etrij.2021-0187.
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