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

• URL: http://arxiv.org/abs/2111.09416v1
• Date: Thu, 7 Oct 2021 10:05:25 GMT
• Title: Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach
• Authors: Sulaiman Khan, Suleman Khan, Yasir Ali, Muhammad Khalid, Zahid Ullah and Shahid Mumtaz
• Abstract summary: We propose a hybrid deep learning model that consists of a convolution neural network (CNN) and long short term memory (LSTM) The overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability.
• Score: 21.137037568638974
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Learning Load Balancing with GNN in MPTCP-Enabled Heterogeneous Networks [13.178956651532213]
  We propose a graph neural network (GNN)-based model to tackle the LB problem for MP TCP-enabled HetNets. Compared to the conventional deep neural network (DNN), the proposed GNN-based model exhibits two key strengths.
  arXiv  Detail & Related papers  (2024-10-22T15:49:53Z)
• 5G Network Slicing: Analysis of Multiple Machine Learning Classifiers [0.0]
  This paper assesses various machine learning techniques, including the logistic regression model, linear discriminant model, k-nearest neighbor's model, decision tree model, random forest model, SVC BernoulliNB model, and GaussianNB model, to investigate the accuracy and precision of each model on detecting network slices. The report also gives an overview of 5G network slicing.
  arXiv  Detail & Related papers  (2023-10-03T02:16:50Z)
• Adaptive Target-Condition Neural Network: DNN-Aided Load Balancing for Hybrid LiFi and WiFi Networks [19.483289519348315]
  Machine learning has the potential to provide a complexity-friendly load balancing solution. The state-of-the-art (SOTA) learning-aided LB methods need retraining when the network environment changes. A novel deep neural network (DNN) structure named adaptive target-condition neural network (A-TCNN) is proposed.
  arXiv  Detail & Related papers  (2022-08-09T20:46:13Z)
• Optimized Quantum Networks [68.8204255655161]
  Quantum networks offer the possibility to generate different kinds of entanglement prior to network requests. We utilize this to design entanglement-based quantum networks tailored to their desired functionality.
  arXiv  Detail & Related papers  (2021-07-21T18:00:07Z)
• Learnable Expansion-and-Compression Network for Few-shot Class-Incremental Learning [87.94561000910707]
  We propose a learnable expansion-and-compression network (LEC-Net) to solve catastrophic forgetting and model over-fitting problems. LEC-Net enlarges the representation capacity of features, alleviating feature drift of old network from the perspective of model regularization. Experiments on the CUB/CIFAR-100 datasets show that LEC-Net improves the baseline by 57% while outperforms the state-of-the-art by 56%.
  arXiv  Detail & Related papers  (2021-04-06T04:34:21Z)
• Deep-Mobility: A Deep Learning Approach for an Efficient and Reliable 5G Handover [0.0]
  5G cellular networks are being deployed all over the world and this architecture supports ultra-dense network (UDN) deployment. Small cells have a very important role in providing 5G connectivity to the end users. In contrast to any traditional handover improvement scheme, we develop a 'Deep-Mobility' model by implementing a deep learning neural network (DLNN) to manage network mobility.
  arXiv  Detail & Related papers  (2021-01-17T00:31:37Z)
• Channel Planting for Deep Neural Networks using Knowledge Distillation [3.0165431987188245]
  We present a novel incremental training algorithm for deep neural networks called planting. Our planting can search the optimal network architecture with smaller number of parameters for improving the network performance. We evaluate the effectiveness of the proposed method on different datasets such as CIFAR-10/100 and STL-10.
  arXiv  Detail & Related papers  (2020-11-04T16:29:59Z)
• Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming [97.40955121478716]
  We propose a first-order dual SDP algorithm that requires memory only linear in the total number of network activations. We significantly improve L-inf verified robust accuracy from 1% to 88% and 6% to 40% respectively. We also demonstrate tight verification of a quadratic stability specification for the decoder of a variational autoencoder.
  arXiv  Detail & Related papers  (2020-10-22T12:32:29Z)
• Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio [101.84651388520584]
  This paper presents a new framework named network adjustment, which considers network accuracy as a function of FLOPs. Experiments on standard image classification datasets and a wide range of base networks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-06T15:51:00Z)
• Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G [53.23237216769839]
  We develop a deep learning framework to approximate the optimal resource allocation policy for base stations. We find that a fully-connected neural network (NN) cannot fully guarantee the requirements due to the approximation errors and quantization errors of the numbers of subcarriers. Considering that the distribution of wireless channels and the types of services in the wireless networks are non-stationary, we apply deep transfer learning to update NNs in non-stationary wireless networks.
  arXiv  Detail & Related papers  (2020-03-29T04:48:22Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.