# Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach

arXiv: https://arxiv.org/abs/2111.09416 · HTML: https://mlsanity.com/paper/2111.09416/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 36 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Toward Intelligent Vehicular Networks: A Machine Learning Framework](https://mlsanity.com/paper/1804.00338/) (2018)
- [KNN and ANN-based Recognition of Handwritten Pashto Letters using Zoning Features](https://mlsanity.com/paper/1904.03391/) (2019)
- [Towards Secure Slicing: Using Slice Isolation to Mitigate DDoS Attacks on 5G Core Network Slices](https://mlsanity.com/paper/1901.01443/) (2019)

## Built on by (most cited first, 2 of 2)

- [AI-driven Orchestration at Scale: Estimating Service Metrics on National-Wide Testbeds](https://mlsanity.com/paper/2507.16077/) (2025)
- [SliceVision-F2I: A Synthetic Feature-to-Image Dataset for Visual Pattern Representation on Network Slices](https://mlsanity.com/paper/2511.01087/) (2025)

## Abstract (arXiv)

In the current era, the next-generation networks like 5th generation (5G) and 6th generation (6G) networks require high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network slicing is considered as one of the key elements for 5G and 6G networks. A reconfigurable slicing allows the operators to run various instances of the network using a single infrastructure for a better quality of services (QoS). The QoS can be achieved by reconfiguring and optimizing these networks using Artificial intelligence and machine learning algorithms. To develop a smart decision-making mechanism for network management and restricting network slice failures, machine learning-enabled reconfigurable wireless network solutions are required. In this paper, we propose a hybrid deep learning model that consists of a convolution neural network (CNN) and long short term memory (LSTM). The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices. The applicability of the proposed model is validated by using multiple unknown devices, slice failure, and overloading conditions. The overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability.