## 1 Introduction
Furthermore, the authors in [12] proposed that Hys and TTT are not independent, and provided a functional relationship between Hys, TTT, and UE velocity, which can dynamically select reasonable handover parameters without the need for any predefined handover failure or ping-pang handover thresholds. The researchers in [13] simulated the effect of different handover parameter settings on 5G network performance at different speeds and proposed that medium handover parameter settings are the best solution. However, this paper analyzed fixed handover parameter settings so the algorithm could not optimize parameters automatically. Based on joint optimization, system adaptive optimization is an important research direction to dynamically achieve the optimal performance in the actual communication process. The authors in [14] proposed a Q-learning-based algorithm in LTE network to dynamically adjust Hys and TTT. The authors in [15] constructed a parameter adaptive handover mechanism suitable for 5G in the high-speed railway dedicated LTE-R communication system, the mechanism uses the interaction of temporal-difference (TD)-learning-based reinforced agents to obtain high-speed railway handover and network performance in different combinations of speeds and handover parameters. The simulation results indicated that the suggested mechanism can find the optimal handover parameters to improve the handover performance and network performance. The researchers in [16] proposed an automatic weighting function to adjust the weights of different functions according to the SINR level of the user equipment (UE), the cell load and the speed of the UE, thereby independently adjusting the handover parameter values for the users. On the basis of [7], the authors in [17] proposed a DRQN algorithm to dynamically adjust Hys and TTT, considering ping-pong handover and handover failure. However, the above research have the following disadvantages: for the flexibility and performance of the algorithm, the protocol stack has been modified to a large extent which, for example, embeds the DQN into the existing handover process, resulting in frequent Q-network invoking every time when facing potential handover. Mobile devices that do not support software definition of communication protocols are also unable to deploy those algorithms and thus fall back to legacy handover process. This is considered to be low in efficiency, may contain poor backward compatibility, and is difficult to be implemented. The motivation of this paper is to establish a handover adaptive optimization method with strong backward compatibility. This paper regards reinforcement learning module as an independent module in relation to the handover process. Thus, the information interaction between the two needs to be reduced as much as possible. Therefore, in this paper, signal fading conditions are regarded as the basis for optimization. Furthermore, in this paper's context, the optimization module is not supposed to participate in the handover process but only gives parameter suggestions according to signal fading conditions. This paper proposes a DQN handover parameter optimization scheme based on LSTM-assisted digital twins. A DQN handover parameter selection method for different wireless signal fading conditions is established, an LSTM-assisted digital twin is utilized to enhance performance by improving system efficiency and convergence effect. The main contributions of this paper are summarized as follows:  
* Considering different conditions of signal fading, a real-time handover parameter selection method using a DQN is proposed. The method can be added to the actual communication system as an additional module without changing the handover process and has better backward compatibility. Good convergence performance is obtained through the validation of simulation platform. * An enhanced DQN method based on digital twin is proposed.