# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 4 Handover Decision Techniques
### Spatial Information Aware HO Decision Approaches  
As mobile networks are proliferating, fewer communication delays, higher data rates and higher capacities are expected in these networks. However, these factors can be compromised for high-speed scenarios in HetNets and small cells as they will observe frequent handovers and high signalling overheads. Most of these handovers are unwanted or can be avoided if the UE performs better handover decisions. Basic and conventional handover approaches only required signal strength information to perform handovers; however, these approaches can severely affect the handover performance in HetNet scenarios. For example, the conventional systems choose a cell with the highest signal strength as the handover target cell even if its link would be lost within a second after the handover process. Instead, another cell with a lower signal strength but a longer stable link could have prevented the system from frequent handovers. So the use of proactive and conditional handover schemes as shown in Figure 11 can be combined with location-based decision-making procedures to predict more accurate estimation for handovers. In this section, we present a summary of studies that utilize location and trajectory information for handover decision-making. The findings from these studies are presented in tabular form in Table 10.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**Ref. No Year** & **Criteria Parameter Selection** & **of HCP’s** & **KPIs** & **Simulator** & **Achievement** & **Algorithm** \\ \hline
2022 & RSRP, RSRQ, Cell Informations, Channel Conditions, Achannel Width & & & & & & Deep-Mobility in the best beneficial for understanding & Deep-Mobility in the best beneficial for understanding & Mobility in the best beneficial for understanding \\ \hline
2021 & RSRP, SINR CIO & HMM, TTT, CIO & RLF, HOPP, HOP, RLF & NS-3 & & & Adaption time and user satisfaction rates are only 4.17 \% and 416.7\% at 5 Km/hr, and 33.33\% and 187.8\% at 30 Km/hr respectively as compared to baseline algorithm & & & TL, RL \\ \hline  
In the article [116,121], a movement, direction and distance-based multi-criteria handover decision algorithm was proposed for femto cells and dense macro cells. The proposed algorithm employed UE speed, distance, UE trajectory, UE location, Cell location and RSRP as input parameters for different handover scenarios. The simulation results were evaluated for packet delay and packet loss in [121] and packet delay, packet loss, throughput and number of HOs in [116] on different cell radius proximities. Too close and too distant from the BS induce HOPP and HOF, respectively, and they related this in the form of packet loss rate. Similarly, in [99], the authors used spatio-temporal information for the handover decision process. For target small cell selection, they considered a number of quality indicators such as packet loss rate, HOPP, call drop and block rate, etc. This approach was intended to reduce unnecessary handovers and HOPP in 5G macro and small Cell mobile networks. They employed Kringing Interpolator with Semivariogram analysis for spatial estimation of the collected values at the first stage. They used a k-order autoregressive model for temporal estimation to get temporal values against each target cell. This temporal estimation aimed to get more stable target cells for the mobility management process. The simulation results were compared with the traditional handover process by considering HOPP, HOF, CDR, CBR and PLR. The authors of [100] proposed a multi-attribute decision making QoS aware handover technique for HetNets. They considered RSRP, co-channel interference and outage probability on the basis of UE location for better target cell selection. They divided the cell coverage area into three regions.