Machine Learning Based Resource Allocation for Future Wireless Networks

QC 20250131

Abstract

Efficient resource allocation is a critical challenge in future wireless networks, particularly as user demands, network densities and network complexities continue to grow. Traditionally, channel state information (CSI) of the user terminals is utilized for resource allocation. However, with increased network density and taking into account the existence of mobile users, CSI-based resource allocation poses significant performance overhead. This work explores a novel approach to resource allocation by leveraging machine learning models trained on user coordinate information. Specifically, we formulate the resource allocation problem in three ways: (1) modulation and coding scheme (MCS) prediction for transport capacity maximization, (2) resource allocation in noise-limited systems based on user positions, and (3) resource allocation in interference-limited systems to ensure fairness while maximizing capacity.We consider two user placement scenarios for performance evaluation: random drop scenario (RDS), where users are randomly distributed in the propagation environment, and mobility model scenario (MMS), where user positions follow a linear trajectory. 

We perform extensive evaluations to compare the datasets from RDS across key metrics, including the number of training samples, computational complexity, and model performance under varying channel conditions and erroneous position information. Our results demonstrate the viability of coordinates-based resource allocation through machine learning in adapting to complex wireless environments, achieving efficient and scalable resource allocation while maintaining robust performance under dynamic and imperfect conditions. Our proposed coordinates-based resource allocation scheme performs at par with the CSI-based resource allocation scheme, achieving at least 90% performance in an interference-limited system having changing scatterers' density. In addition, the scheme significantly outperforms the geometric-based resource allocation scheme, which intuitively applies the coordinates' information of users for distance-dependent resource allocation. The MMS dataset serves to determine the implementation cost of the proposed scheme, by considering a realistic channel model where the data samples are collected on a continual basis in the system. With this approach, we compare performance in terms of training time, prediction time, and memory footprint of the machine learning models. The results show that the coordinates-based resource allocation scheme can be used reliably for efficient resource allocation while incurring a low to moderate implementation cost for noise-limited and interference-limited system, respectively. This study highlights the potential of machine learning-driven resource management for future wireless networks, paving the way for intelligent, adaptive, and efficient communication systems.

urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-359405