## 5 Challenges and Future Research Directions
So, providing the best machine learning solution for limited energy supply is another critical challenge is an active research area for beyond 5G wireless networks. 5. Wireless Back haul Spectrum Efficiency; In beyond 5G wireless networks, cell BSs requires wireless back haul network with massive capability to handle the large number of wireless connections and flexible deployment [120]. Hence, providing the best machine learning solution for spectrum resource management, networking complexity, and infrastructure cost to handle the large number of cells in beyond 5G wireless networks is an active research area. 6. Advanced Techniques Integration; In 5G small cell networks, mmWave, massive MIMO, and mMTC are the key enablers to improve the network capacity up to 100 times. And massive signaling overhead of these advance technologies produce dense communication and processing [121]. Therefore, providing the resource efficiency, cost efficiency, and interference mitigation using machine learning in beyond 5G wireless networks is also an active research area. 7. Security and Privacy Concerns; The most critical and crucial challenges in HO management for UD 5G small cell networks are security and privacy concerns since the high densification of the cells and UEs. Number of new functions and applications dealing with communication data pose new challenges for security compromise and privacy concern [122]. Hence, efficient counterstep using machine learning in beyond 5G small cell wireless networks also an active research direction. 8. HO in Drone Mobility; According to 3 GPP, unmanned airel vehicles possibly experience weak signal-to-interference-plus-noise ratio (SINR) than terrestrial UEs. Because of obstacles occurring between the wireless signals and possibility of HOs increases so it is inevitable to improve these issues. As mentioned previously, small cell technology and cell densification also bring challenges for mobility and HO management. In UD networks, the coverage range of cells is limited and overlaid. As a result, UEs, covering mobility functions, need to move from one cell to another or face frequent HOs. Mobility management is a key feature of 5G infrastructure as it improves user experience and use cases that will be coming in future. Therefore, HO functions and operations must be completed without interference and interruption instances to perform the requirements of 5G mobility management. In this paper, we discuss challenges of mobility and HO management in 5G UD cellular network and scrutinize multiple ways to overcome these challenges. In drone communication, telepresence, dominance in line of sight (LoS), coordinated multi-point transmission, air-borne-base station required more efficient solutions to conduct different services [123; 124]. Therefore, cost effective machine learning based solution in UD networks also an active research direction. Load Imbalance; Despite all the advantages of HetNets and cell densifications, small cell technology comes with hurdles that must be solved first. Load imbalance, for instance, occurs due to variation in transmitted power and coverage area from unliketiers of cells. Henceforth, small cells will not serve substantial purpose by using traditional user association rules which revolve around only received power. Cell range expansion (CRE) or biasing is a persuasive technique to fight against this challenge [125]. * Inter-cell Interference; Inter-cell interference is yet another issue present in cell densification that can be solved through eICIC, an abbreviation used for cell interference Coordination. This mitigation technique exploits Almost blank subframes (ABS) to eradicate noises or interference from Macrocell BSs. ABSs are integrated in HetNets to optimize interference of high power nodes. However, low power nodes know the interference pattern, allowing the CRE to be embedded over the low power nodes and can serve large number of UEs without getting interference from high power nodes [126]. * Radio Resource Control (RRC).