Diversified Channel Estimation Method for Reducing Path Loss of MIMO Integrated With Backscatter Networks Using Propagation Learning

ABSTRACT Backscatter networks unite the minor communication entities of a multi‐input–multi‐output (MIMO) system by sharing common radio resources. Such networks estimate the allocation channel capacities to ensure path loss is confined at different intervals. To this extent, a diversified channel estimation using propagation learning (DCE‐PL) method is proposed in this article. This method estimates the channel for its maximum acceptance rate during allocation and resource allocation. The interference is estimated for the first allocation, and in the consecutive allocations, it is forecasted using propagation learning. The divergence in actual allocation and the channel's maximum capacity is used to train the propagation learning. Based on the postinterval channel estimation, the consecutive common sharing between the user equipment (UEs) and the backscatter network is planned. The proposed method improves the capacity allocation rate by 10.83% and reduces the path loss and mean error by 11.98% and 14.2% respectively, for the maximum UE.