Design and Implementation of Resource Management Optimization Algorithm to improve QoS performance

In wireless communication, the physical layer handles radio propagation and other challenges unique to a wireless channel. Whereas, Media access control layer coordinates the access to the shared medium. This work carried out deliberates on physical as well as medium access control layers respectively. It aims to develop the algorithm to improve Quality of Service (QoS) for recommendation as well as allocation of a good channel in the presence of congestion in a dense environment. The performance of the algorithm is evaluated using network simulator NS2. The investigated algorithm focuses on improving the QoS performance of the network by throughput gain of 22.22 %, reducing packet dropping ratio by 4 %, and recommending a good channel based on the fitness function. The simulation result shows that the algorithm provides a 15% better packet delivery ratio in congestion and dense scenarios. In this paper, required QoS aimed at channel recommendation for communication using machine learning technique is achieved.

[1]  Makrand M Jadhav , Et. al.,et al.  Machine Learning based Autonomous Fire Combat Turret , 2021 .

[2]  Sachin Paranjape,et al.  Bio‐inspired hybrid algorithm to optimize pilot tone positions in polar‐code‐based orthogonal frequency‐division multiplexing–interleave division multiple access system , 2020, Int. J. Commun. Syst..

[3]  Guto Leoni Santos,et al.  When 5G Meets Deep Learning: A Systematic Review , 2020, Algorithms.

[4]  Gheorghita Ghinea,et al.  5MART: A 5G SMART Scheduling Framework for Optimizing QoS Through Reinforcement Learning , 2020, IEEE Transactions on Network and Service Management.

[5]  Moses Ekpenyong,et al.  Optimized channel allocation in emerging mobile cellular networks , 2020, Soft Computing.

[6]  Fadi Al-Turjman,et al.  Applications of Artificial Intelligence and Machine learning in smart cities , 2020, Comput. Commun..

[7]  Ye Xiao,et al.  AI Inspired Intelligent Resource Management in Future Wireless Network , 2020, IEEE Access.

[8]  Ingrid Moerman,et al.  A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer , 2020, Electronics.

[9]  Yu Cheng,et al.  Deep Learning Meets Wireless Network Optimization: Identify Critical Links , 2020, IEEE Transactions on Network Science and Engineering.

[10]  Ashok M. Sapkal,et al.  Seamless Optimized LTE Based Mobile Polar Decoder Configuration for Efficient System Integration, Higher Capacity, and Extended Signal Coverage , 2019, Int. J. Appl. Metaheuristic Comput..

[11]  Yuanming Shi,et al.  LORM: Learning to Optimize for Resource Management in Wireless Networks With Few Training Samples , 2018, IEEE Transactions on Wireless Communications.

[12]  Mugen Peng,et al.  Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues , 2018, IEEE Communications Surveys & Tutorials.

[13]  Wei Cui,et al.  Spatial Deep Learning for Wireless Scheduling , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[14]  Igor G. Olaizola,et al.  Network Resource Allocation System for QoE-Aware Delivery of Media Services in 5G Networks , 2018, IEEE Transactions on Broadcasting.

[15]  Vivek S. Deshpande,et al.  Characterization of Wireless Sensor Networks for Traffic & Delay , 2013, 2013 International Conference on Cloud & Ubiquitous Computing & Emerging Technologies.

[16]  Muneer Khan Mohammed,et al.  Optimal 5G network slicing using machine learning and deep learning concepts , 2021, Comput. Stand. Interfaces.

[17]  Vivek S. Deshpande,et al.  Reducing Delay Data Dissemination Using Mobile Sink in Wireless Sensor Networks , 2013 .