Novel blind full multipath Two Way Relay Network (TWRN), OFDM channel
estimation using Machine Learning
Abstract
Most two way relay network (TWRN) half-duplex channel estimation
algorithms have been developed for single path channels, except for
those in frequency domain OFDM systems. We derive a novel time-domain,
blind Maximum A posteriori Probability (MAP) estimation method for
multipath estimation in TWRN OFDM systems. Since a TWRN half-duplex
system is a cascade of two/more bidirectional transmission systems,
there are multiple forward and reverse, individual, as well as
composite/cascade (of two individual) channels (unlike only one
channel in traditional transmission). The situation is further
complicated in the case of multipath channels. Additionally, TWRN
systems suffer from having noise components at different nodes,
including colored (non-white) at the receiver terminal node. Thus most
recent research works concentrate on the easier task of estimating
single-path (flat frequency) channels, that too by pilot-based, and
sometimes, even by suboptimal least squares (LS) methods. However, in
this paper, forward, composite/individual mulipath channel estimators
developed are semiblind (for enhanced spectral efficiency), and which
turn out to be nonlinear. Moreover, an unique “Factor Analysis
Alternating Maximization” method (used in psychometrics and some
Machine Learning (MLe) applications, but not in signal processing,
communication/TWRN systems), is used, in a novel manner, to overcome the
colored noise problem, allowing one to derive novel, closed-form,
analytical expressions for reverse individual channel h
estimation, (with its convergence provided), which is unavailable in
existing literature. Non-trivial Cramer Rao bounds have also been
derived for these novel multipath channel estimators. Comprehensive
simulation results show the novel forward, reverse, composite and
individual channel estimation methods perform much better than the
existing ones.