Optimal Hybrid Quantum-Classical Convolutional Neural Network for Efficient 5G Network Slicing

Newly developed fifth- and sixth-generation (5G and 6G) networks are very secure, reliable, less latent, and versatile. These networks provide various services, like Massive Machine Type Communication (mMTC), Ultra-Reliable Low Latency Communication (URLLC), and Enhance Mobile Broadband (eMBB). Network slicing (NS) is important in both 5G and 6G networks as it enables the provision of the above services in a single physical network. This paper proposes an Optimal Hybrid Quantum-Classical Convolutional Neural Network for efficient 5G Network Slicing (HQCNN-5GNS). Initially, the input data are gathered from Unicauca IP flow version 2 and 5G network slicing dataset. The collected data are fed to the pre-processing phase using a Pseudo-linear Maximum Correntropy Kalman Filter (PMCKF) to remove duplicate data and replace the missing values from the dataset. The pre-processing information is provided to the Hybrid Quantum-Classical Convolutional Neural Network (HQCNN) to classify the network slicing into mMTC, eMBB, and URLLC. In general, HQCNN does not adopt any optimization methods to determine the optimal parameters to guarantee accurate categorization. Therefore, the Quantum Beetle Antennae Search Algorithm (QBASOA) is employed to optimize the weight parameter of HQCNN. The experimental outcomes show that the proposed HQCNN-5GNS attains 17.25%, 15.65%, and 19.55% higher accuracy than the existing approaches: Harris Hawks optimization depends on hybrid deep learning for effectual network slicing on 5G network (HHO-HDL-5GNS), Highly accurate with dependable wireless network slicing in the 5G network: a hybrid deep learning model (WNS-HDL-5GNS), Deep reinforcement learning-basis network slicing for beyond 5G (DRL-5GNS), respectively.