Coordinates-Based Resource Allocation Through Supervised Machine Learning

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. In such cases, an estimate of the terminals' position information provides an alternative to estimating 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 simple system setup 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 user's coordinates are erroneous. The performance is quite consistent, especially when complex learning frameworks like random forest and neural network are used for resource allocation. In terms of applicability, a training time of about 4 s is required for coordinates-based resource allocation using random forest algorithm, and the appropriate resource allocation is predicted in less than 90 mu s with a learnt model of size <1 kB.