Optimal Beam Training for mmWave Massive MIMO using 802.11ay

TL;DR

This paper introduces a new beam training protocol for mmWave MIMO systems that enhances data-rate performance by combining compressed sensing with optimized beam selection, surpassing existing 802.11ad methods.

Contribution

It proposes a novel beam training protocol utilizing compressed sensing to improve antenna weight vector selection in mmWave MIMO systems.

Findings

Proposed protocol achieves higher data rates than 802.11ad.

Simulation results demonstrate significant performance improvement.

Enhanced beam training reduces complexity and improves efficiency.

Abstract

Beam training of 802.11 ad is a technology that helps accelerate the analog weighting vector (AWV) selection process under the constraint of the existing code-book for AWV. However, 5G milli-meter wave (mmWave) multiple-input-multiple-output (MIMO) system brings challenges to this new technology due to the higher order of complexity of antennae. Hence, the existing codebook of 11ad is unlikely to even include the near-optimal AWV and the data rate will degrade severely. To cope with this situation, this paper proposed a new beam training protocol combined with the state-of-the-art compressed sensing channel estimation in order to find the AWV to maximize the optimal data-rate. Simulation is implemented to show the data-rate of AWV achieved by 11 ad is worse than the proposed protocol.