## 2 Related Work  
The retrial phenomena described in the retrial-queue literature bear similarities to the sensing process. If a client entering a retry queue cannot locate an available server, they will be barred and will need to try again later. Clients who are blocked enter the orbit, a line. The M/G/1/1 retrial queue was analyzed by [PERSON] et al. [3] to determine the system's efficiency metrics. [PERSON] [4] established a limiting hypothesis for the waiting period experienced by consumers during heavy traffic, whereas [PERSON] [5] developed the waiting-time distribution of customers in orbit. [PERSON] [6] calculated the average, variance, and probability distribution based on the total number of trials. The models we use in this study feature two classes of users, PUs and SUs, but there is only one class of users in these models. While accessing a server, SUs must perceive, and when a PU arrives, an SU may be interrupted in transmission. Correlated retrial queuing architectures that feature two-way interaction with two customer classes without disruptions were showcased by [PERSON] and [PERSON] [7]. A queuing framework for cognitive radio networks with reactive-decision spectrum handoff was put forward by [PERSON] et al. [8]. The blocking probability, the average number of sensing SUs, and the transmission time are among the outcome metrics that the authors calculated by restricting the number of SUs that can feel simultaneously (i.e., by considering a finite orbit size). According to [PERSON] et al. [8], an SU keeps detecting servers one by one until it locates one that is idle. By removing assumptions and opening up protocols and interfaces among various elements, this multi-vendor, interoperable technology enables the incorporation of intelligence into RAN to support a variety of implementation settings [9; 10]. In keeping with the current trend, this study introduces a federation layer to an O-RAN architecture to facilitate effective admission management, dynamic traffic forecasting, and service monitoring. A weighted-sum-based multi-objective optimization framework for resource allocation in cloud systems was suggested by [PERSON] et al. [11]. This framework utilized the fuzzy-logic method to provide the coefficients for the specified objectives. The adaptive algorithm uses the coefficients above to produce Pareto-optimal alternatives. A combination of techniques that employs a multi-point algorithm in heterogeneous networks to determine the best RAT selection was put forward by [PERSON] et al. [12]. The scheme was implemented by applying biogeography-based optimization (BBO) to RAT selection probabilities generated by a Markov decision process (MDP). The study [13] proposed an M/D/1 model of a PU delay system with both non-preemptive and preemptive priority. The average waiting time for PU was 18% and that for SU was 32% under the non-preemptive scheme in their proposed approach. Further, the waiting times for PUs and SUs were observed to be 22% and 7%, respectively, with the preemptive approach. A comprehensive summary showing the comparison between the above studies is discussed in Table 1 below. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Authors** & **Models Used** & **Contributions** & **Limitations** \\ \hline [PERSON] et al. [3] & M/G/1/1 retrial queue & Analysis of system efficiency metrics & Focus on single-class user models \\ \hline \multirow{2}{*}{Falin [4]} & \multirow{2}{*}{M/G/1/1 retrial queue} & Establishes limiting hypothesis for waiting period during heavy traffic & Single-class user models \\ \hline \multirow{2}{*}{Falin [5]} & \multirow{2}{*}{M/G/1/1 retrial queue} & Develops waiting-time distribution of customers in orbit & Single-class user models \\ \hline \multirow{2}{*}{Falin [6]} & \multirow{2}{*}{M/G/1/1 retrial queue} & Calculates average, variance, and probability distribution based on total number of trials & Single-class user models \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative of relevant studies surveyed.