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Novel blind full multipath Two Way Relay Network (TWRN), OFDM channel estimation using Machine Learning
  • Parthapratim De
Parthapratim De
Inst for Infocomm Research

Corresponding Author:[email protected]

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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.