Multi-antenna technology has been leveraged in the past decades to meet the continuous demand for increased system capacity. User densification and high data rate provision further necessitates the efficient and fair allocation of resources among the users to reap the full benefit of multi-antenna systems. Conventionally, the resource allocation has been optimized using channel state information of users, which poses a performance trade-off considering large overhead in dense system configuration. In contrast, the position information carries the potential for efficient resource allocation with minimal acquisition cost, and can be enhanced by utilizing the power of machine learning (ML) frameworks. This work focuses on applying ML model for fair resource allocation based on position coordinates of the users in the system. Specifically, we evaluate the performance of our previously proposed coordinates-based resource allocation scheme through ML [1] in an interference-limited scenario, along with a fairness metric applied for optimized resource allocation. Results show that coordinatesbased resource allocation through ML can be successfully used for both noise-limited and interference-limited systems, and performs consistently well under different propagation channel conditions. Surprisingly, the learning frameworks used for coordinates-based resource allocation require similar training time, despite the complexity arising due to interference consideration, and has a moderate storage footprint as an implementation cost in the system.