Data-Aided Fast Beamforming Selection for 5G

Abstract

Millimeter wave frequencies paired up with MIMO antennas employing beamforming are seen as critical enablers of next generation networks. However, selecting the most beneficial beamforming weights in a codebook-enabled downlink transmitter is a lengthy task, as the existing methods rely on some form of channel measurement. In fact, if the used codebook is too large, the traditional methods might fail to select an appropriate entry within the channel coherence time. In this paper, a new method to assist the beam selection is proposed, based on data obtained from previous connections. Through the continuous update of the set of optimal codebook entries for each position, the search space required for each connection can be greatly reduced if the user position is known. The simulations performed show that retrieving the sets of codebook entries in single user scenarios required less than 51 ns. For multi-user scenarios, results exceeding 10 simultaneous users using a 16 entry codebook were achieved, requiring less than 600 μs. The obtained results show that the proposed method can greatly reduce the beam selection latency and energy requirements, opening the door to powerful millimeter wave networks.