2 research outputs found
Learning to Estimate: A Real-Time Online Learning Framework for MIMO-OFDM Channel Estimation
In this paper we introduce StructNet-CE, a novel real-time online learning
framework for MIMO-OFDM channel estimation, which only utilizes over-the-air
(OTA) pilot symbols for online training and converges within one OFDM subframe.
The design of StructNet-CE leverages the structure information in the MIMO-OFDM
system, including the repetitive structure of modulation constellation and the
invariant property of symbol classification to inter-stream interference. The
embedded structure information enables StructNet-CE to conduct channel
estimation with a binary classification task and accurately learn channel
coefficients with as few as two pilot OFDM symbols. Experiments show that the
channel estimation performance is significantly improved with the incorporation
of structure knowledge. StructNet-CE is compatible and readily applicable to
current and future wireless networks, demonstrating the effectiveness and
importance of combining machine learning techniques with domain knowledge for
wireless communication systems