SIAM Review
On Limits of Wireless Communications in a Fading Environment when UsingMultiple Antennas
Wireless Personal Communications: An International Journal
Periodic sequences with optimal properties for channel estimation and fast start-up equalization
IBM Journal of Research and Development
Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Channel Estimation for Amplify and Forward Relay Based Cooperation Diversity Systems
IEEE Transactions on Wireless Communications
Distributed Space-Frequency Coding for Cooperative Diversity in Broadband Wireless Ad Hoc Networks
IEEE Transactions on Wireless Communications
On channel estimation and optimal training design for amplify and forward relay networks
IEEE Transactions on Wireless Communications - Part 2
Distributed space-time-coded protocols for exploiting cooperative diversity in wireless networks
IEEE Transactions on Information Theory
Cooperative diversity in wireless networks: Efficient protocols and outage behavior
IEEE Transactions on Information Theory
Cooperative Strategies and Capacity Theorems for Relay Networks
IEEE Transactions on Information Theory
Cyclic Prefix Update for OFDM Amplify-and-Forward Relay Systems
Wireless Personal Communications: An International Journal
Combined Time Synchronization and Channel Estimation for OFDM Based Multi-relay Networks
Wireless Personal Communications: An International Journal
Hi-index | 0.00 |
In this paper, we address the preamble-based channel estimation problem for orthogonal frequency-division multiplexing (OFDM) systems using multiple amplify-and-forward (AF) relays. We first establish a linear minimum mean-square-error (LMMSE) channel estimator which directly estimates the overall channels from the source to the destination. Then, based on the assumption that the channel correlations between the relays are unknown at the source terminal, a suboptimal training sequence and precoding matrices in closed form are designed to approach the optimal estimation performance at low-complexity.