# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 4 Handover Decision Techniques
### FLC-Based HO Decision Approaches
Whereas in stage three, the selected target cell will be checked for the radio resources availability. The simulation results were compared with several TOPSIS and simple fuzzy schemes on the basis of PLR, average throughput and number of handovers. However, in the article [88], the authors used RSRP, RSRQ and UE velocity as inputs to a fuzzy logic system to optimize HOM as well as TTT values. But the results were only compared on the basis of HOPP and HOP for different UE velocities. The majority of the available literature for fuzzy logic-based handovers has used numerous input parameters to obtain more appropriate optimization values. However, the authors of this article [96] only used SINR as an input parameter, together with changes in SINR value, to forecast the HOM. They defended this technique by claiming that when a UE's SINR value drops, the UE is likely to move away from the BS and disconnect soon. As a result, they proposed that when the SINR is low, the HOM value be set low to initiate the handover process early. They simply employed nine fuzzy rules to calculate HOM and compared simulation results using RLF and HOPP. Basic FL approaches employed in mobile communications performed well. They act as a reliable and robust approach for optimizing HCPs based on uncertain data however, these basic approaches have some flaws. For example, they cannot produce a reliable and robust decision if the number of input decision criterion increases. Similarly, designing of membership function also needs some automation instead of human experiences as, due to dynamically changing environments these approaches may not work in all scenarios. A fuzzy logic TOPSIS approach was presented in [89] that used RSRP, RSRQ, SINR and UE velocity as input parameters. From these input parameters, fuzzy logic utilized three inputs named RSRP, RSRQ and UE velocity for the fuzzification process that will help in the handover decision process. The basic purpose was to optimize the HOM by considering multiple input parameters. They used TOPSIS to prioritize the selected target cells from the available list and compared the results based on the number of HOs, HOF, HOPP and data rate. Using SINR and UE velocity helps to improve HOM optimization according to the user requirements. Similarly, the article [94] proposed a fuzzy logic and TOPSIS-based approach. They utilized fuzzy logic to manage dynamic input control parameters and TOPSIS to select the best BS. In order to improve the decision criteria, they used the concept of subtracting cluster scheme to generate membership functions based on historical data. They utilized SINR, RSRP, jitter, and packet loss as input parameters and compared simulation results using HOPP, the number of HOs, and the mean option score, which is a performance indicator for algorithms. Instead of using TOPSIS, the authors in [90] used a deep-Takagi-Sugeno-Kang fuzzy classifier (DTSK-C) for HetNets scenarios. They named it H2 RDC (heuristic handover based on RCC-DTSK-C) with the purpose to reduce the number of HOPP. The DTSK-C classifier used a deep stack structure between different subsystems to classify and prioritize the target cell for handovers. They considered SINR, RSRP, RSRQ and UE velocity as input parameters for the fuzzification process to construct the input dataset for handovers. The simulation results were compared with the state-of-the-art approach [89] along with two other basic approaches by considering HOPP, HOF, number of HOs, CDR and time required for handovers. Table 7 presents the studies discussed in this section based on FLC. Fuzzy logic has been widely used in mobile communication applications as a reliable and robust approach for optimizing handover control parameters based on uncertain data. Despite its advantages, it still has some limitations, such as its inability to produce a reliable decision when the number of input decision criteria increases.