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Ayass et al. Intell Robot 2022;2(1):20-36 https://dx.doi.org/10.20517/ir.2021.07 Page 26

[10]
Hu et al. proposed an intelligent handover control method for UAVs in cellular networks. They
introduced a deep learning model to predict the user’s trajectory to provide mobility management. The
handover decision is conceived by calculating the received signal power based on the predicted location and
the characteristics of the air-ground channel, for accurate decision making. The simulation results
demonstrate that the proposal’s handover success rate was 8% higher than the traditional handover method.

[11]
Lee et al. emphasized that the traditional handover decision is not suitable for drones that move and
communicate in 3D space. The drone’s characteristics are considered as input parameters, namely the speed
limit and coverage area, which are used as input in a fuzzy system for decision making on the handover.
Thus, the calculation of the number of handover decisions showed that considering the parameters related
to the terminal (drone) and the parameters related to the network has a positive effect on the handover
decision.

Madelkhanova et al. developed a new algorithm based on Q-learning to manage the handover between
[12]
airbase stations and static BSs, to maximize the total capacity of the UEs served by the air BSs. The Q-
learning agent states are described in terms of the load of the ground bases and the reward function is
defined in terms of the capacity of the UEs served by the air BSs. The results show an increase in the
capacity of the UEs by up to 18% and 20% in the level of satisfaction with the solution. They also
demonstrated that the Q-learning process converges quickly and only dozens of handovers are needed to
achieve a significant gain.

Park et al. presented an efficient handover mechanism for aerial networks in three-dimensional space,
[13]
which differs considerably from conventional two-dimensional schemes. The proposed scheme adjusts the
height of a drone and the distance between drones. For this purpose, the probability of successful handover
without interruption and the false probability of starting the handover were considered to evaluate the ideal
coverage decision algorithm. The proposed method was the first attempt to offer a handover scheme for
drones in three-dimensional space.

Bai et al. pointed out that the support of drones in cellular networks has allowed a wide range of new
[14]
applications for next-generation wireless systems. However, they discussed that these networks were
traditionally designed to serve terrestrial users, which contributes to the emergence of challenges to support
wireless communication by drones. As these devices experience increased interference and channel
fluctuation, they must perform handover more frequently and are more susceptible to failure rate and ping-
pong during movement.

Faced with these challenges, the authors proposed an improved mobility management algorithm for drones,
exploring pre-configured flight path information and their air channel properties. That is the proposal of a
route-aware handover decision algorithm to minimize the failure and reduce the number of unnecessary
handovers. The simulation results also demonstrate that the algorithm can reduce the ping-pong effect in
certain cases.

[15]
Dong et al. proposed a scheme that dynamically adjusts the HO trigger parameters (handover) to reduce
the number of unnecessary transfers. The scheme specifically considers the UAV sailing at a certain altitude
and taking off. Experiments showed that the presented solution can significantly reduce the number of
unnecessary HOs and improve network performance. They also showed that the channel quality between
the UAV and the BS is very different from that on the ground, and therefore selecting the most appropriate
target BS is also important.