Momentum Survey Propagation: A Statistical Physics Approach to Resource Allocation in mMTC

Abstract

The importance of Massive Machine-Type Communications (mMTC) in Beyond 5G and 6G networks is supported by the ever-increasing number of connected devices in what are known as massive Internet of Things (IoT) networks. These networks bring unprecedented challenges for the distribution of the available communication resources because the allocation problems often lead to combinatorial optimization formulations which are known to be NP-hard. A fact that limits the performance of state-of-the-art techniques when the network size increases. To address this challenge, we take a new direction and propose a method based on statistical physics to address resource allocation problems in large networks. To this aim, we first show that resource allocation problems have the same structure as the problem of finding specific configurations in spin glasses, a type of disordered physical systems. Based on this parallel, we propose Momentum Survey Propagation, a resource allocation method to minimize the interference in mMTC networks. Our proposed approach extends the Survey Propagation method of statistical physics. Specifically, it exploits the so-called momentum technique, widely used in the context of neural networks, to improve the convergence properties of Survey Propagation. Our implementation is the first application of Survey Propagation to a wireless communication network. Through numerical simulations we show that Momentum Survey Propagation is a promising tool for the efficient allocation of communication resources in mMTC.