# Resource Allocation Schemes for 5G Network: A Systematic Review
## 5 Discussion
### Q2. What Is the Importance of Resource Allocation in 5G?
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\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & **Algorithm/Scheme/Strategy** & **Problem Addressed** & **Improvements/Achievements** & **Limitations/Weakness** \\ \hline \multirow{4}{*}{[142]} & Deep reinforcement learning-based intelligent & The high dynamic network traffic and unpredicted link-state &  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & **Algorithm/Scheme/Strategy** & **Problem Addressed** & **Improvements/Achievements** & **Limitations/Weakness** \\ \hline \multirow{4}{*}{[150]} & & The optimization problem in mixed-integer nonlinear programming (MINLP). &  
In [89], the authors presented a supporting technique to handle the problem of interference, including co-tier interference and cross-tier interference, that distributed other tiers' macro users that are associated with the users of that tier. The technique implemented an algorithm of online learning for effective allocation of a spectrum having modulation and adaptation of power competence. The simulation results illustrated the outperformance of the online scheme and attained a significant improvement in spectral efficiency, outage ratio, fairness, and throughput. The researchers examined the resource allocation for the downlink of two-tier heterogeneous networks containing macro-cell transmission having utilization of dual band and microwave frequency of small cells having millimeter wave and microwave frequencies [90]. Other authors discussed a novel approach for the base stations of a small cell with a dual band. The area covered by the small cell was categorized among two sections where the outer and inner section users were linked separately by small cells on microwave and millimeter wave frequencies. They designed a theory-based game approach having two layers for enhancing the spectral efficiency and energy efficiency of the system with the best utilization of existing radio resources. In [91], the authors presented GAPSO-PA (Genetic Algorithm Particle Swarm Optimization-Power Allocation), an allocation of power strategy that relies on the GAPSO algorithm. The GAPSO algorithm combines both GA and PSO algorithms like other swarm intelligence algorithms, and it is efficient for resolving the problems of non-linear optimization using cost efficient fast-global search. The resource configuration of the whole heterogeneous ultra-dense network was maintained through an SDN controller. As per [92], the researchers discussed a unique algorithm of resource allocation (RA) for packet Universal Filtered Multi-Carrier (UFMC) BIC-based communications, which was applied in a unique modulation format in 5G wireless systems. The presented RA scheme enhanced the bit loading and coding rate among the overall bandwidth along with carrying per-sub-band power distribution. The researchers [93] produced useful D2D multicast links by sufficiently applying bot social and physical aspects of mobile manipulators, having the objective of enhancing the throughput of the whole social-aware network along with ensuring fair channel allocation among multiple D2D multicast groups. The proposed work primarily consisted of two segments, with the creation cluster having D2D multicast and jointly optimized allocation of both channel and power. Evaluation results proved that, compared with a stochastic and heuristic algorithm, the proposed scheme enhance the entire social-aware network throughput by 50% and 5%, respectively. In [94], they presented a slice-based virtual resource scheduling having NOMA technology to increase the system's quality of service (QoS). The authors formulated subcarrier allocation and power granularity allocation schemes into a Constrained Markov Decision Process (CMDP) problem, targeting improvement of the entire user rate. The above-used scheme further prevented the expectation calculation and the curse of dimensionality in the optimal value function. They developed and designed an adaptive resource allocation strategy that relied on Approximate Dynamic Programming (ADP) to solve the issue.