Abstract
In mobile communication systems with multisensor antennas at base stations, downlink channel estimation plays a key role because accurate channel estimates are needed for transmit beamforming. One efficient approach to this problem is channel probing with feedback. In this method, the base station array transmits probing (training) signals. The channel is then estimated from feedback reports provided by the users. This paper studies the performance of the channel probing method with feedback using a multisensor base station antenna array and single-sensor users. The least squares (LS), linear minimum mean square error (LMMSE), and a new scaled LS (SLS) approaches to the channel estimation are studied. Optimal choice of probing signals is investigated for each of these techniques and their channel estimation performances are analyzed. In the case of multiple LS channel estimates, the best linear unbiased estimation (BLUE) scheme for their linear combining is developed and studied.
Highlights
In recent years, transmit beamforming has been a topic of growing interest [1, 2, 3, 4, 5]
This paper studies the performance of the channel probing method with feedback using a multisensor base station antenna array and single-sensor users
The beamforming performance severely depends on the quality of channel estimates and an accurate downlink channel estimation plays a key role in transmit beamforming [6, 7, 8, 9]
Summary
Transmit beamforming has been a topic of growing interest [1, 2, 3, 4, 5]. The aim of transmit beamforming is to send desired information signals from the base station array to each user and, at the same time, to minimize undesired crosstalks, that is, to satisfy a certain quality of service constraint for each user. This task becomes very complicated if the transmitter does not have precise knowledge of the downlink channel information for each user. The linear minimum mean square error (LMMSE) channel estimator is developed and studied The latter technique is able to outperform both the LS and SLS estimators, but it requires the full a priori knowledge of the channel covariance matrix and the receiver noise powers. The effect of such a combining on the performance of downlink channel estimation is investigated
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