MFTTS: A Mean-Field Transfer Thompson Sampling Approach for Distributed Power Allocation in Unsourced Multiple Access

Unsourced multiple access (UMA) is a novel approach to support a large number of devices in a massive Machine-Type Communication (mMTC) system. UMA enables devices to concurrently encode their data using the same codebook to transmit without being individually identified, resulting in reduced signaling and computational overhead at the base station. Hybrid-domain non-orthogonal multiple access (NOMA), which combines power-domain NOMA with code-domain NOMA, is another technique that enhances the spectral efficiency of mMTC. While the study of hybrid-domain NOMA has been conducted, its integration with UMA has not been thoroughly investigated. Considering that mMTC traffic primarily consists of sporadic short packets in the uplink direction, employing a fully distributed mMTC multiple access protocol can substantially decrease signaling overhead and latency. In this work, a multi-armed bandits (MAB) paradigm is adopted to create a distributed power selection policy for devices that using UMA. Particularly, an MAB algorithm called Thompson Sampling (TS) is used to allow mMTC devices to minimize the transmission power without violating the minimum receiving signal-to-noise constraint needed to correctly decode the UMA codewords back to the original messages. A mean-field modeling technique is used to approximate the learned policies. The knowledge gained from the approximated policies can be transferred to new devices by initializing their prior distribution, which is called Mean-field Transfer Thompson Sampling (MFTTS). Simulations show that the mean-field approximation is indeed accurate and effective. Interestingly, MFTTS performs better than TS without knowledge transfer as well as other distributed power allocation methods.