Optimal Channel Allocation: A Dual Approach with MCDM and Machine Learning
Keywords:
Channel Allocation, Machine Learning, MCDM, Ranking, Decision SystemAbstract
In today's world of fast advancing wireless technologies, effective spectrum use is critical. With the help of machine learning and multiple criteria decision making (MCDM), this research aims to overcome this problem. An organised method for taking into account the many competing elements that affect channel allocation, like signal quality, interference, and resource availability, is offered by the MCDM framework. It enables decision-makers to consider these aspects and come to well-informed conclusions. Machine learning techniques are utilised to improve the MCDM methodology by analysing past data and forecasting future network conditions, which aids in decision-making even more. The combination of machine learning and MCDM allows for a dual strategy. Machine learning adds automation and predictive power, while MCDM offers a transparent, easily understood decision-making process. Combining these approaches allows the suggested method to adjust to changing network conditions, giving it a reliable and flexible solution for wireless communication networks' ideal channel allocation. It is anticipated that this research will have a major impact on the wireless communication sector, improving quality of service, reducing interference, and increasing spectral efficiency. The suggested dual strategy has the ability to completely change how network managers and operators distribute channels, guaranteeing that limited resources are used as efficiently as possible and that network performance is continuously improved in a constantly changing wireless environment.
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