Beyond Complexity Limits: Machine Learning for Sidelink-Assisted mmWave Multicasting in 6G

Abstract

The latest technological developments have fueled revolutionary changes and improvements in wireless communication systems. Among them, mmWave spectrum exploitation stands out for its ability to deliver ultra-high data rates. However, its full adoption beyond fifth generation multicast systems (5G+/6G) remains hampered, mainly due to mobility robustness issues. In this work, we propose a solution to address the problem of efficient sidelink-assisted multicasting in mobile multimode systems, specifically by considering the possibility of jointly utilizing sidelink/device-to-device (D2D), unicast, and multicast transmissions to improve service delivery. To overcome the complexity problem in finding the optimal solution for user-mode binding, we introduce a pre-optimization step called multicast group formation (MGF). Through a clustering technique based on unsupervised machine learning, MGF allows to reduce the complexity of solving the sidelink-assisted multiple modes mmWave (SA3M) problem. A detailed analysis of the impact of various system parameters on performance is conducted, and numerical evidence of the complexity/performance trade-off and its dependence on mobility patterns and user distribution is provided. Particularly, our proposed solution achieves a network throughput improvement of up to 32% over state-of-the-art schemes while ensuring the lowest computational time. Finally, the results demonstrate that an effective balance between power consumption and latency can be achieved through appropriate adjustments of transmit power and bandwidth.