Channel Estimation for Amplify and Forward Relay Networks
The use of intermediate-relaying nodes has been identified as a promising technique for enhancing coverage and combating the impairments of Multiple-Input-Multiple-Output (MIMO) wireless channels. The main idea
is to introduce relays that forward the data to the destination which is
otherwise out of the reach of the source. Accurate channel state information
(CSI) is crucial for optimizing the performance of relay-assisted MIMO
In this thesis, we propose and analyze a training based channel estimator
for amplify-and-forward (AF) relay networks. The method consists of a sequence of least-squares (LS) problems aiming at a computationally efficient solution. It is based on creating different compound channels by varying the gain factors at the relays. Then, the individual links from source to relays and from relay to destination are revealed using LS. For the purpose of performance
evaluation, the Cramer-Rao lower bound (CRB) is computed and
compared with the asymptotic covariance of the proposed estimator. Since the existing estimator does not attain the CRB, we propose and analyze an improved algorithm that is efficient for high Signal-to-Noise Ratio (SNR).
Furthermore, we present a beamforming scheme for MIMO relay networks as an application of the proposed algorithm. We examine the possibility of beamforming at the relaying nodes under receiver SNR maximizing criterion and individual, per relay, transmit power constraints. The relays exploit the knowledge of the channel matrix between the source and the relays as well as the channel matrix between the relays and the destination.
Our numerical results indicate that the choice of the amplification gains is
crucial to achieve an efficient use of the relays.
Cramer-Rao Lower Bound (CRB).
least squares (LS)