Abstract
Two-way relay networks can have their throughputs improved by adopting physical-layer network coding. To work properly, these systems need to know the channel impulse responses involved. Most previous works on channel estimation for physical-layer network coding systems consider flat fading or frequency-selective fading together with orthogonal frequency division multiplexing modulation. However, for single carrier systems under frequency-selective channels these estimators can not be applied directly. Therefore, a least squares channel estimator for these scenarios is proposed here. Simulations are performed to evaluate the performance of this channel estimator and the results show the effectiveness of the proposed technique.
Highlights
The first work on communication systems that use twoway channels for exchanging information between two nodes was presented by Shannon in 1961 [1]
This protocol is known as Amplify-and-Forward (AF), and the system is said to use Analog Network Coding (ANC), since the relay does not need to perform any detection, and the mapping occurs in the analog domain
Most works on Physical-layer Network Coding (PNC) systems consider that the channel impulse responses (CIR) of each channel are perfectly known at the relay and at the end nodes
Summary
The first work on communication systems that use twoway channels for exchanging information between two nodes was presented by Shannon in 1961 [1]. The nodes must, be able to extract the information of interest from this signal This protocol is known as Amplify-and-Forward (AF), and the system is said to use Analog Network Coding (ANC), since the relay does not need to perform any detection, and the mapping occurs in the analog domain. Most works on PNC systems consider that the channel impulse responses (CIR) of each channel are perfectly known at the relay and at the end nodes. The work in [6] derives the maximum likelihood (ML) estimator and the Linear Maximum Signal-to-Noise Ratio (LMSNR) estimator, considering flat fading channels It proposes the optimal training sequence that reduces the mean squared error (MSE) associated with those estimators.
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