User-Centric Learning for Multiple Access Se-lections
Sumayyah Dzulkifly1, Wahidah Hashim2, Ahmad Fadzil Ismail3, Mischa Dohler4
1Sumayyah Dzulkifly, Institute of Informatics and Computing Energy, University Tenaga Nasional, Malaysia
2Wahidah Hashim, Institute of Informatics and Computing Energy, University Tenaga Nasional, Malaysia.
3Ahmad Fadzil Ismail, IIUM Strategic Technologies and Engineering Research Unit, Research Management Center, International Islamic University Malaysia.
4Mischa Dohler, Department of Informatics, 30 Aldwych, King’s College London, WC2B 4BG London, United Kingdom.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2338-2344 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2666109119/2019©BEIESP | DOI: 10.35940/ijeat.A2666.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: We are in the age where business growth is based on how user-centric your services or goods is. Current research on wireless system is more focused on ensuring that user could achieve optimal throughput with minimal delay, disregarding what user actually wants from the services. Looking from connectivity point of view, especially in urban areas these days, there are multiple mobile and wireless access that user could choose to get connected to. As people are looking toward machine automation, we understand that the same could be done for allowing users to choose services based on their own requirement. This paper looks into unconventional, non-disruptive approach to provide mobile services based on user requirements. The first stage of this study is to look for user association from three new perspectives. The second stage involved utilizing a reinforcement learning algorithm known as q-learning, to learn from feedbacks to identify optimal decision in reaching user-centric requirement goal. The outcome from the proposed deployment has shown significant improvement in user association with learning aware solution.
Keywords: q-learning, user-centric, heterogeneous wireless networks, user association, access selection