Smart Congestion Control in 5G/6G Networks Using Hybrid Deep Learning Techniques

With the mobility and ease of connection, wireless sensor networks have played a significant role in communication over the last few years, making them a significant data carrier across networks. Additional security, lower latency, and dependable standards and communication capability are required for future-generation systems such as millimeter-wave LANs, broadband wireless access schemes, and 5G/6G networks, among other things. Effectual congestion control is regarded as of the essential aspects of 5G/6G technology. It permits operators to run many network illustrations on a single organization while maintaining higher service quality. A sophisticated decision-making system for arriving network traffic is necessary to confirm load balancing, limit network slice letdown, and supply alternative slices in slice letdown or congestion. Because of the massive amount of data being generated, artificial intelligence (AI) and machine learning (ML) play a vital role in reconfiguring and improving a 5G/6G wireless network. In this research work, a hybrid deep learning method is being applied to forecast optimal congestion improvement in the wireless sensors of 5G/6G IoT networks. This proposed model is applied to a training dataset to govern the congestion in a 5G/6G network. The proposed approach provided promising results, with 0.933 accuracy, and 0.067 miss rate.

[1]  Zihang Gao.  5G Traffic Prediction Based on Deep Learning , 2022, Computational intelligence and neuroscience.

[2]  Okuthe P. Kogeda,et al.  A survey of deep reinforcement learning application in 5G and beyond network slicing and virtualization , 2022, Array.

[3]  Taher M. Ghazal,et al.  Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques , 2022, Egyptian Informatics Journal.

[4]  Sun Mao,et al.  Energy-Efficient Cooperative Communication and Computation for Wireless Powered Mobile-Edge Computing , 2022, IEEE Systems Journal.

[5]  Muhammad Attique Khan,et al.  MapReduce based intelligent model for intrusion detection using machine learning technique , 2021, J. King Saud Univ. Comput. Inf. Sci..

[6]  Shah Nazir,et al.  Efficient and reliable hybrid deep learning-enabled model for congestion control in 5G/6G networks , 2021, Comput. Commun..

[7]  Mohsen Guizani,et al.  A survey on 5G/6G, AI, and Robotics , 2021, Comput. Electr. Eng..

[8]  Linghe Kong,et al.  Spectrum Sharing for 5G/6G URLLC: Research Frontiers and Standards , 2021, IEEE Communications Standards Magazine.

[9]  Jun Zhao,et al.  Smart City Enabled by 5G/6G Networks: An Intelligent Hybrid Random Access Scheme , 2021, 2101.06421.

[10]  Branka Vucetic,et al.  Deep Learning for Radio Resource Allocation With Diverse Quality-of-Service Requirements in 5G , 2020, IEEE Transactions on Wireless Communications.

[11]  Lingyang Song,et al.  Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks , 2020, IEEE Network.

[12]  Jinsong Wu,et al.  Green Massive Traffic Offloading for Cyber-Physical Systems over Heterogeneous Cellular Networks , 2019, Mob. Networks Appl..

[13]  Branka Vucetic,et al.  Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin , 2019, IEEE Transactions on Wireless Communications.

[14]  Ignacio Bosch,et al.  Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment , 2019, Electronics.

[15]  Wajeb Gharibi,et al.  Wireless Sensor Networks in oil and gas industry: Recent advances, taxonomy, requirements, and open challenges , 2018, J. Netw. Comput. Appl..

[16]  Song Guo,et al.  Information and Communications Technologies for Sustainable Development Goals: State-of-the-Art, Needs and Perspectives , 2018, IEEE Communications Surveys & Tutorials.

[17]  Dhananjay Singh,et al.  Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems , 2018, Entropy.

[18]  Giovanni Rigazzi,et al.  Multi-Radio 5G Architecture for Connected and Autonomous Vehicles: Application and Design Insights , 2018, EAI Endorsed Trans. Ind. Networks Intell. Syst..

[19]  Jaime Lloret,et al.  An architecture and protocol for smart continuous eHealth monitoring using 5G , 2017, Comput. Networks.

[20]  Subhashini Krishnasamy,et al.  A generic controller for managing TCP transfers in IEEE 802.11 infrastructure WLANs , 2017, J. Netw. Comput. Appl..

[21]  Jaime Lloret,et al.  An Intelligent handover process algorithm in 5G networks: The use case of mobile cameras for environmental surveillance , 2017, 2017 IEEE International Conference on Communications Workshops (ICC Workshops).

[22]  Carles Antón-Haro,et al.  5G Mobile Cellular Networks: Enabling Distributed State Estimation for Smart Grids , 2017, IEEE Communications Magazine.

[23]  Mahamod Ismail,et al.  Topology sense and graph-based TSG: efficient wireless ad hoc routing protocol for WANET , 2017, Telecommun. Syst..

[24]  Yu Chen,et al.  Distribution Reshaping for Massive Access Control in Cellular Networks , 2016, 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall).

[25]  H. Vincent Poor,et al.  Cluster Content Caching: An Energy-Efficient Approach to Improve Quality of Service in Cloud Radio Access Networks , 2016, IEEE Journal on Selected Areas in Communications.

[26]  Yong Li,et al.  System architecture and key technologies for 5G heterogeneous cloud radio access networks , 2015, IEEE Netw..

[27]  Mahamod Ismail,et al.  Wireless implementation selection in higher institution learning environment , 2014 .

[28]  Djamel Djenouri,et al.  Congestion Control Protocols in Wireless Sensor Networks: A Survey , 2014, IEEE Communications Surveys & Tutorials.

[29]  Sally Floyd,et al.  Congestion Control Principles , 2000, RFC.

[30]  Raj Jain,et al.  Congestion Control in Computer Networks: Trends and Issues , 1990, IEEE Netw..

[31]  Mubarak S. Almutairi,et al.  Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction , 2022, Mathematical Problems in Engineering.

[32]  E. E. García-Guerrero,et al.  Traffic Flow Prediction for Smart Traffic Lights Using Machine Learning Algorithms , 2022 .

[33]  Sagheer Abbas,et al.  Intelligent Model Of Ecosystem For Smart Cities Using Artificial Neural Networks , 2021, Intelligent Automation & Soft Computing.

[34]  Tao Jiang,et al.  Digital Media Application Technology of Mobile Terminals Based on Edge Computing and Virtual Reality , 2021, Mob. Inf. Syst..

[35]  Iván García-Magariño,et al.  PriorityNet App: A Mobile Application for Establishing Priorities in the Context of 5G Ultra-Dense Networks , 2018, IEEE Access.

[36]  P. Thangaraj,et al.  A CLUSTER BASED CONGESTION CONTROL PROTOCOL FOR MOBILE ADHOC NETWORKS , 2010 .