# Memory-assisted Statistically-ranked RF Beam Training Algorithm for Sparse MIMO

## TL;DR

This paper introduces a memory-assisted, statistically-ranked RF beam training algorithm for sparse MIMO channels that reduces training overhead by leveraging past data and probabilistic beam ranking.

## Contribution

It proposes a novel hybrid beam training algorithm that combines statistical knowledge with multi-level search to improve efficiency in sparse MIMO systems.

## Key findings

- Significant reduction in beam testing overhead for low entropy scenarios.
- Hybrid algorithm improves performance in high entropy cases.
- Training savings increase with channel dimension and decrease in beam entropy.

## Abstract

This paper presents a novel radio frequency (RF) beam training algorithm for sparse multiple input multiple output (MIMO) channels using unitary RF beamforming codebooks at transmitter (Tx) and receiver (Rx). The algorithm leverages statistical knowledge from past beam data for expedited beam search with statistically-minimal training overheads. Beams are tested in the order of their ranks based on their probabilities for providing a communication link. For low beam entropy scenarios, statistically-ranked beam search performs excellent in reducing the average number of beam tests per Tx-Rx beam pair identification for a communication link. For high beam entropy cases, a hybrid algorithm involving both memory-assisted statistically-ranked (MarS) beam search and multi-level (ML) beam search is also proposed. Savings in training overheads increase with decrease in beam entropy and increase in MIMO channel dimensions.