## 3 Proposed Work  
This section discusses the details of the proposed Deep Contextual Bandit-based network slicing (DCB-NS) scheme. It starts with the presentation of the proposed system architecture (Section 3.1), followed by the Network Slice Request Model (Section 3.2), the discussion of the Node Selection and Slice Mapping Model (Section 3.3), and then the specifics of the proposed scheme (Section 3.4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Objective** & **Proposed Solution** & **Performance Metrics** \\ \hline \multirow{4}{*}{[19]} & Integration of Mobile Edge Computing (MEC) for efficient allocation of idle 5G & A MEC-based 5G network resource & \(\bullet\) & Service rate (in terms of served and blocked \\  & mobile network resources in & allocation framework for aggregated & \(\bullet\) & served and blocked \\  & urban settings & idle network resources & \(\bullet\) & requests) \\ \hline \multirow{4}{*}{[20]} & Service-oriented network E2E slice & A complex network theory-based slice & \(\bullet\) & Resource efficiency \\  & mapping and deployment & mapping and creation with slice & \(\bullet\) & Acceptance Ratio \\  & & deployment policy formulation for & \(\bullet\) & \\  & & eMBB, mMTC, and uRLLC use-cases & & \\ \hline \multirow{4}{*}{[21]} & Efficient online service & A multi-armed bandit-based slice & \(\bullet\) & Cumulative agent rewards \\  & request-to-network slice brokering method for budgeted & \(\bullet\) & Tenant selection ratio \\  & while considering network & resource lock-up for 5G & \(\bullet\) & System utilization \\  & resource availability & network tenants & distribution \\ \hline \multirow{4}{*}{[22]} & & A Deep Belief Network and Neural & \(\bullet\) & Learning percentage \\  & & Network-based network slice & \(\bullet\) & Accuracy \\  & learning and AI techniques & classification scheme with Glowworm & \(\bullet\) & Sensitivity \\  & Swarm-based parameter & \(\bullet\) & Specificity \\  & weight optimization & \(\bullet\) & Precision \\ \hline \multirow{4}{*}{[23]} & Implementation of MCDM-based node & A VIKOR algorithm-based & \(\bullet\) & Acceptance ratio \\  & ranking for effective slice provisioning & core-network-slice-provisioning & \(\bullet\) & Revenue-to-cost ratio \\  & & approach to secure network slicing & \(\bullet\) & Node utilization \\ \hline \multirow{4}{*}{[24]} & multi-resource allocation while & A multi-resource allocation framework & \multirow{4}{*}{Resource utilization} \\  & considering resource usage fairness and & based on the Ordered Weighted & \(\bullet\) & Resource utilization \\  & system efficiency & Average (OWA) operator for resource & \(\bullet\) & Fairness \\  & system efficiency & availability and user demand & & \\  & information aggregation & & \\ \hline \multirow{4}{*}{[25]} & Efficient slice deployment through cost & A dynamic slice deployment through a & \(\bullet\) & Energy consumption \\  & and network energy reduction & prediction-assisted adaptive network & \(\bullet\) & Link utilization \\  & and network energy reduction & slice algorithm using Holt–Winters & \(\bullet\) & Total slicing cost \\  & & (HW) prediction & & \\ \hline \multirow{4}{*}{[26]} & Service provisioning in RAN slices & A unified RAN slice provisioning & \(\bullet\) & UE admission rate \\  & while ensuring QoS and optimal & framework for maximization of & \(\bullet\) & Bandwidth consumption \\  & resource utilization & bandwidth utilization with user & \(\bullet\) & Algorithm running \\  & QoS guarantee & & time11 \\ \hline \multirow{4}{*}{[27]} & RAN and CN resource allocation & A proposed Deep Q-Network & \multirow{4}{*}{E2E Access rate} \\  & through E2E NS while considering & algorithm for E2E wireless resource & \(\bullet\) & E2E Access rate \\ \cline{1-1}  & access rate and delay service & allocation and service link mapping on & \\ \cline{1-1}  & requirements & 5G network slices & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._