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arXiv · 2005.06509

Coordinates-based Resource Allocation Through Supervised Machine Learning

Sahar Imtiaz·Sebastian Schiessl·Georgios P. Koudouridis·James Gross
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Abstract

Appropriate allocation of system resources is essential for meeting the increased user-traffic demands in the next generation wireless technologies. Traditionally, the system relies on channel state information (CSI) of the users for optimizing the resource allocation, which becomes costly for fast-varying channel conditions. Considering that future wireless technologies will be based on dense network deployment, where the mobile terminals are in line-of-sight of the transmitters, the position information of terminals provides an alternative to estimate the channel condition. In this work, we propose a coordinates-based resource allocation scheme using supervised machine learning techniques, and investigate how efficiently this scheme performs in comparison to the traditional approach under various propagation conditions. We consider a simplistic system set up as a first step, where a single transmitter serves a single mobile user. The performance results show that the coordinates-based resource allocation scheme achieves a performance very close to the CSI-based scheme, even when the available coordinates of terminals are erroneous. The proposed scheme performs consistently well with realistic-system simulation, requiring only 4 s of training time, and the appropriate resource allocation is predicted in less than 90 microseconds with a learnt model of size less than 1 kB.

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Sahar Imtiaz, Sebastian Schiessl, Georgios P. Koudouridis, James Gross. 2020-05-13. Coordinates-based Resource Allocation Through Supervised Machine Learning. https://arxiv.org/abs/2005.06509

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