Optimal MMSE Processing for Limited-Capacity Radio Stripes

Cell-Free Massive multiple-input multiple-output (MIMO) has proven to be an essential topology for 5G and beyond 5G (B5G) wireless communications, as it can offer unprecedented levels of spectral efficiency (SE) and energy efficiency (EE). The foremost issue of such systems is the highly centralized architecture that often leads to opposite results. Radio stripes (RS) form a versatile solution which can offer traffic load alleviation, infrastructure cost reduction and installation flexibility. This paper studies the uplink case of a limited-capacity RS system and proposes a novel MMSE algorithm that leads to optimal results, in the sense of local mean square error (MSE) minimization. Performance is further enhanced by the application of the heuristic Compare-and-Forward (CnF) strategy, which, by enabling dynamic clustering, it can optimally choose a subset of antenna processing units (APUs) to serve each user equipment (UE). That user-centric approach, combined with our optimal algorithm, can guarantee superior performance than any other radio stripe processing scheme.