Individual channel tracking for one-way relay networks with particle filtering
Hong Mei Hu, Shun Zhang, Hongyan Li · 2014
In this paper, we present a new tracking algorithm based on time-multiplexed-superimposed training (TMST) scheme for the individual channels in amplify-and-forward oneway relay network (OWRN) under time-varying flat fading scenario. Due to the large number of unknowns, we apply the the polynomial basis-expansion-model (P-BEM) to approximate the channel vector of each individual hop by a coefficient-vector with much smaller size, called in-BEM-CV here. Then tracking the individual channel is converted to tracking the corresponding in-BEM-CVs. With the aid of Jakes model, we developed an auto-regressive (AR) process for the in-BEM-CVs and derive the hidden Markov model (HMM) for the in-BEM-CV tracking problem. A particle filtering (PF)-based algorithm to dynamically track the in-BEM-CVs is then designed. Finally, numerical results are presented to evaluate the proposed algorithms.