# Resource Allocation Schemes for 5G Network: A Systematic Review
## 5 Discussion
### Q2. What Is the Importance of Resource Allocation in 5G?
In the first phase, numerous VUEs were released from unlicensed frequency bands and the time factor of the duty cycle scheme, while in the second phase, the issue was transformed into a convex optimization problem, which was resolved through the presented Lagrange duality method (LDM). The simulation findings expressed the performance of the presented application scenario having a Wi-Fi or LTE system. Furthermore, the presented scheme performed efficiently related to throughput, along with ensuring QoS of WUEs as compared with the general greedy algorithm. In [151], the authors focused on the resource constraints, and based on 5G enabler concepts and operative bandwidth, a resource allocation scheme was presented that could achieve the requirements of reliability and delay for URLLC traffic. Latency components and end-to-end error were presented and the interchange existing between the error components was used for minimizing data rate. A unified queuing strategy, time frequency, and packet delivery resource allocation for CoMP empowered URLLC in C-RAN architecture were presented. The presented system illustrated the efficient performance regarding UE satisfaction and resource utilization compared with current techniques. In [152], the joint optimization issue was examined along with ambiguous channel rising to achieve maximum energy efficiency and reducing intra-cell interference. The likelihood limitation was converted to the deterministic one based on the fundamental conversion. By using successive convex approximation and a relaxation variable scheme, the novel integer non-convex optimization challenge was shared by two resolvable convex sub-challenges. The power control and user association algorithms were focused to fix the optimal resource allocations. The findings revealed by simulation illustrated the effectiveness of the scheme and were shown to be robust in the dynamic communication environment. In [153], the M/G/1 queuing model was deployed to investigate the inaccurate transmission retrieval interruption of URLLC multiuser amenities, and in applying this model, the lowest essential data rate was designed and implemented on an adaptive control scheme. The presented Pollaczek-Khinchine (P-K) formula-based quadratic optimization (PFQO) method for controlling the maximum retransmission parameter of the hybrid automatic repeated request (HARQ) technique in URLLC enhanced the bandwidth requirement. The findings revealed in a simulation displayed the bandwidth saving effect of the presented PFQO scheme based on various packet length distributions and signal-to-interference-and-noise ratios (SINRs). In [154], the articulated optimization problem was related to the mixed integer nonlinear programming (MINLP) problem, which is NP-hard and which needs a comprehensive search to obtain an ideal result. Nonetheless, the computational complexity of the comprehensive search increased exponentially with the growth in the number of users. Hence, an outer approximation algorithm (OAA), having least complexity, was presented to attain a close-to-optimal solution. Wide-ranging simulation exercises were conducted to assess the proposed system. Outcomes focused on the usefulness of the projected innovative decoupled cell association scheme over the traditional coupled cell association scheme regarding mitigating interference, users attached/associated, offloading traffic to address sum-rate maximization, and traffic imbalances. The researchers in [155] proposed a resource allocation scheme that addressed network slicing by applying the Powell-Hestenes-Rockafellar technique and the branch and bound system, obtaining an ideal result. The outcomes proved that the proposed resource allocation scheme could significantly enhance URLLC spectral efficiency and the system's reliability, in contrast with the equal subcarrier allocation (ESA), the equal power allocation (EPA), and the adaptive particle swarm optimization (APSO) algorithms.