Human-centric decision-making in cell-less 6G networks

Abstract

In next-generation networks, cells will be replaced by a collection of points-of-access (PoAs), with overlapping coverage areas and/or different technologies. Along with a promise for greater performance and flexibility, this creates further pressure on network management algorithms, which must make joint decisions on (i) PoA-to-user association and (ii) PoA management. We solve this challenging problem through an efficient and effective solution concept called Cluster-then-Match (CtM). While state-of-the-art approaches tend to focus on performance-related metrics, e.g., network throughput, CtM makes human-centric decisions, where pure network performance is balanced against energy consumption and electromagnetic field exposure. Importantly, such human-centric metrics concern all humans in the network area — including those who are not network users. Through our performance evaluation, which leverages detailed models for EMF exposure estimation and standard-specified signal propagation models, we show that CtM outperforms state-of-the-art network management schemes that solely focus on network performance, including those utilizing machine learning, reducing energy consumption by over 80% in indoor scenarios, and over 36% in outdoor ones.