## 5 Challenges and Future Research Directions  
Many studies already have been conducted to address one of the biggest challenges of future wireless networks such as HO management in 5G small cell. Emerging technologies i.e., D2D, M2M, MIMO, EC, SC, BF, convergence of WiFi and cellular, SDN, NFV and CC and upcoming use cases and services such as mMTC, eMBB, and uRLLC introduces new challenges. Also high speed mobility, high data rate applications and limited resources in 5G UDSC networks faces numerous challenges. Still there are some significant challenges needs to be addressed in optimized way using advance machine learning algorithms. In this section, we will briefly discuss the upcoming challenges for HO management in small cell networks and future research direction. 1. QoS/QoE for multimedia traffic; The requirements for quality of service and serving capability of multimedia traffic are different from the data and voice traffic. HO techniques deliver different QoS/QoE in different use cases to perform various types of multimedia traffic [116]. Providing the best machine learning solution while considering the QoS/QoE in HO management, is an active research area for beyond 5G wireless small cell networks where huge data will be drive with low latency and best connectivity. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Algorithm** & **Model** & **Description** \\ \hline [PERSON], et al. [108] & Deep Learning & Model free & Suggest a mobility model to simplify the user’s trajectory using recurrent neural network variations and eliminating the irrelevant data. \\ \hline [PERSON], et al. [109] & Deep Learning & Model Free & Suggest a mobility model of existing relations from all existing trajectories for future path prediction using a similarity metric. \\ \hline [PERSON], et al. [110] & Deep Learning & Model Free & Proposed an analytical model to determine the holistic cost of HO i.e., latency, signaling overhead, and call dropping. \\ \hline [PERSON]. [PERSON], et al. [111] & Deep Learning & Model Free & Suggest a technique to predict obstruction and mmWave beam for mobility management considering sub-6 GHz channels. \\ \hline [PERSON], et al. [112] & Deep Learning & Model Free & Suggest a policy to predict the upcoming cell for proactive conditional HO using deep neural network in mmWave networks. \\ \hline [PERSON]. [PERSON], et al. [113] & Deep Learning & Model Free & For low latency mobile networks, hidden Markov process implemented for learning the optimal HO controllers and to prediction the next connected access point. \\ \hline [PERSON], et al. [114] & Deep Learning & Model Free & Proposed a proactive HO method based on novel data-driven intelligent radio access network. The technique decreases the number of service interruptions and the impact of ping-pong effect. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Controlling Communication Overhead; Existing HO solutions required complicated and frequent collaboration between all nodes available for communication i.e., Macrocoll, small cells and the UEs. This phenomenon required large number of network resources to exchange the necessary information [117]. Providing the best machine learning solution for controlling communication overhead while considering is an active research area for beyond 5G wireless small cell networks. 3. Network Performance in Outdoor Use Cases; Primarily, huge data traffic broadcast in indoor scenarios where wired and wireless connections are the best available option [118]. while providing the best machine learning solution for outdoor scenarios should be consider carefully is an active research area for beyond 5G wireless networks. 4. Battery Life in Smartphone; Advance antenna, applications and optimized use case scenarios required huge processing and this killing behavior consuming the battery life of smart phones and wireless connected drones [119].