Abstract
With the increasing demand for higher bandwidth and data rate of the mobile user. There are massive Base Stations (BS) will be deployed in the future wireless environment. Several issues could be raised dues to dense deployment of BSs, i.e. handover (HO) ping-pong effect, unnecessary HO and frequent HO. To avoid these effects, the handover decision-making strategies become extremely important to select the optimal BS among all detected BS and ensure QoS for each mobile user. In this paper, the author develops a fuzzy-TOPSIS based HO algorithm to minimise the ping-pong effect and number of HO. The proposed algorithm integrates both advantages of fuzzy logic and TOPSIS. The Received Signal Strength Intensity (RSSI) and Signal to Noise Ratio (SNR) are considered as HO criteria in this approach. For the simulation result, the proposed HO algorithm can reduce ping-pong rate and a number of HO effectivity by comparing to conventional RSSI-based HO approach and classical Multi-Attribute Decision Making (MADM) HO method, i.e. simple additive weighting (SAW) and TOPSIS.
Highlights
To cope up with the demand of the mobile users in mobile data and the Internet of Things (IoT), the fifthgeneration of mobile communications (5G) system has been proposed and developed and expected to be commercialised in 2020
This proposal HO algorithm combines both advantages of fuzzy logic and TOPSIS, which incorporates more than one criteria as the input of HO, and process uncertain input data and weight value to obtain the optimal decision
The simulation results show that the proposed approach can significantly reduce the reversal phenomenon, the ping-pong effect and number of HO failures
Summary
Fuzzy logic is a reliable mathematical tool to trigger an HO as discussed in[6]–[10] The basic structure of fuzzy logic consists of fuzzification, fuzzy inference system (FIS) and defuzzification. Paper [6] proposed a fuzzy logic based HO algorithm to trigger HO under A2 event. After obtaining the normalised matrix, the weight value for each HO criteria can be calculated by the coefficient of standard deviation weighting techniques as (4) (5): Vj σj Z̅j Based on the weight for each HO criteria, the normalised decision matrix and weight value will be transformed from non-crispy values to crispy value by mapping into a triangular fuzzy membership function as shown in (6) and Fig.. Based on the weight for each HO criteria, the normalised decision matrix and weight value will be transformed from non-crispy values to crispy value by mapping into a triangular fuzzy membership function as shown in (6) and Fig.1 Carrier frequency: Duration of simulation Mobility model Number of BSs The distance between each BS Number of UE UE speed Handover threshold Propagation model: 1.5 ~ 2 GHz 36000 s Random direction 16 1800 m Single UE 120 km/h -100.5 dBm Cost-Hata model
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