  Reliability and latency challenges in future mixed sub-6 GHz/millimeter wave
(mmWave) fifth generation (5G) cell-free massive multiple-input multiple-output
(MIMO) networks is to guarantee a fast radio resource management in both uplink
(UL) and downlink (DL), while tackling the corresponding propagation imbalance
that may arise in blockage situations. In this context, we introduce a
semi-blind UL/DL decoupling concept where, after its initial activation, the
central processing unit (CPU) gathers measurements of the Rician
$K$-factor---reflecting the line-of-sight (LOS) condition of the user equipment
(UE)---as well as the DL reference signal receive power (RSRP) for both 2.6 GHz
and 28 GHz frequency bands, and then train a non-linear support vector machine
(SVM) algorithm. The CPU finally stops the measurements of mmWave definitely,
and apply the trained SVM algorithm on the 2.6 GHz data to blindly predict the
target frequencies and access points (APs) that can be independently used for
the UL and DL. The accuracy score of the proposed classifier reaches $95\%$ for
few training samples.
