Classification Algorithms for Semi-Blind Uplink/Downlink Decoupling in sub-6 GHz/mmWave 5G Networks

TL;DR

This paper proposes a semi-blind uplink/downlink decoupling method in 5G networks using a support vector machine to predict optimal frequency bands and access points, enhancing reliability and latency.

Contribution

It introduces a novel semi-blind decoupling approach utilizing SVMs trained on Rician K-factor and RSRP measurements for 5G UL/DL separation.

Findings

Achieves 95% accuracy with few training samples.

Effectively predicts independent UL/DL access points.

Reduces reliance on extensive measurement data.

Abstract

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.