Predicting Channel Transition for MU-MIMO Beamforming

Abstract

Legacy beamforming procedure for multiuser MIMO (MU-MIMO) requires periodic explicit feedback mechanism for channel estimation resulting in high control overhead with increasing number of users. Often, these procedures are redundant, especially if the channel remains static between two successive measurements. Projected increase in number of users in emerging 5G network, necessitates us to rethink the channel sounding technique to reduce overhead as well as increase aggregate throughput of the network. In this paper, we propose to use changes in channel to trigger the channel assessment procedure at Access Point (AP). This technique is generic enough to be used either in the AP of IEEE 802.11ac or in the eNodeB of LTE systems. The experimental results using Software Defined Radios (SDRs) on the ORBIT testbed show more than 96% classification success of channel transition and a significant difference in error vector values for different types of channel variations. This leads to a need-based CSI collection, which will improve aggregate throughput of the network.