# Resource Allocation Schemes for 5G Network: A Systematic Review
## 5 Discussion
### Q5 Which Open Issues and Research Trends Were Unaddressed in Resource Allocation in 5G?  
Because of the evolutionary enhancement in IoT and data requirements, the entire wireless communication system has been completely altered, such as in M2M communication or V2V networks. RA is still facing enormous challenges at each level. Therefore, many challenges such as communication security, network infrastructure, spectral efficiency, and energy efficiency need to be addressed by researchers in the near future. For efficient next-generation communication in the future, it is observed that some main challenges such as lifetime operation networks and the benefits of green communication for the goal of saving energy will be a challenging task. For example, the issues related to resource allocation for achieving energy harvesting networks and green communication networks will receive substantial attention in the near future.  
It is observed that spectral resource (SR) is a limited resource and precious for wireless communication. Therefore, there is a need to develop some useful methods for enhancing the SE. Dynamic RA and spectrum detection capacity are significant issues in resource  
\begin{table}
\begin{tabular}{c c} \hline
**Domain** & **References** \\ \hline Fronthaul & [86,89,90,93–95,99,101,103–107,109–111,113–115,117,118,120,123,124,129,133,134,137–140,142,144–147,149,150,152–156,158] \\ \hline C-RAN & [96,97,109,111,112,118,119,122,126,130,135,143,148,151,157] \\ \hline H-CRAN & [97,132,136,143] \\ \hline Backhaul & [99,110,111,114,133,147] \\ \hline Uplink & [96,97,99,104,105,109–111,113,119,120,123,124,129,140,142,143,145,146,149,150,153,154,156–158] \\ \hline Downlink & [86,89,90,94–11,103,104,106,107,109,111,112,114,115,117,118,120,122,126,129,130,132–139,142–144,147–149,151–155,157] \\ \hline \end{tabular}
\end{table}
Table 6: Uplink Downlink with Domains.  
Figure 10: Articles studied for downlink and uplink resource allocation schemes.  
sharing cognitive networks. Additional new issues for RA in various cellular networks (CNs) may also arise.  
Networks will certainly move towards the development of more powerful functions, higher data rate, better transmission efficiency, and so on from the perspective of network structure. Due to this, how to achieve multiuser diversity optimization and joint antenna selection for multiuser MIMO networks is a challenging issue. Since both ultra-intensive users and multi-antenna systems are a developing trend, the scarce SR and limited bandwidth bring several challenges for the structure and practical application of RA in cellular networks.  
Information security is essential for a communication system from the perspective of information transmission, specifically in cellular networks. Even though CNs can acquire multinetwork integration and fulfill various user requirements, there may be security problems, eavesdropping situations, and information leakage. As a result, RA for consideration of security limitations is essential in multiuser CNs owing to the complex communication scenarios, such as RA for physical layer security in CNs.  
RA has various problems for various application situations. The optimization problem can be a multivariable one. Our focus was to obtain computation offloading and caching optimization in the communication system. In this way, we focused on the complicated and practical application environment. Moreover, from the solution process perspective, self-optimization and more intelligent algorithms should be introduced and developed for upcoming future CNs, such as machine learning for wireless communication applications. The related challenges can be matched adaptively by these machine learning algorithms. The training system can dynamically adjust its parameters of optimization to address the wireless network's requirements. The RA challenges in CNs will attain better solutions in the future by using these intelligent algorithms.