Proactive approach for preamble detection in 5G-NR PRACH using supervised machine learning and ensemble model

Abstract

The physical random access channel (PRACH) is used in the uplink of cellular systems for initial access requests from the users. It is very hard to achieve low latency by implementing conventional methods in 5G. The performance of the system degrades when multiple users try to access the PRACH receiver with the same preamble signature, resulting in a collision of request signals and dual peak occurrence. In this paper, we used two machine learning classification technique models with signals samples as big data to obtain the best proactive approach. First, we implemented three supervised learning algorithms, Decision Tree Classification (DTC), naïve bayes (NB), and K-nearest neighbor (KNN) to classify the outcome based on two classes, labeled as ‘peak’ and ‘false peak’. For the second approach, we constructed a Bagged Tree Ensembler, using multiple learners which contributes to the reduction of the variance of DTC and comparing their asymptotes. The comparison shows that Ensembler method proves to be a better proactive approach for the stated problem.

Dynamic Transmission and Delay Optimization Random Access for Reduced Power Consumption

“…[29] introduces a multi-stage detection process to negate false peaks resulting from difficult channel conditions. In particular, [30] integrates machine learning algorithms such as Decision Tree Classification, Naive Bayes and K-Nearest Neighbor to improve system efficiency, especially in scenarios where multiple devices share the same preamble. Finally, [31] explores the use of device-to-device (D2D) communication to facilitate network access, demonstrating its effectiveness in collision reduction and delay minimization, while identifying the need for further improvements in energy efficiency.…”