Zero-forcing precoding (or ZF-precoding) is a spatial signal processing that nulls multiuser interference at the multiple antenna transmitter in wireless communications. Regularized zero-forcing precoding is enhanced processing to consider the impact on a background noise and unknown user interference[1], where the background noise and the unknown user interference can be emphasized in the result of (known) interference signal nulling.
Performance of Zero-forcing Precoding
If the transmitter knows the downlink channel status information perfectly, ZF-precoding can achieve almost the system capacity when the number of users is large. On the other hand, wth limited channel status information at the transmitter (CSIT) the performance of ZF-precoding decrease depending on the accuracy of CSIT. ZF-precoding requires the significant feedback overhead with respect to signal-to-noise-ratio (SNR) so as to achieve the full multiplexing gain[2]. Inaccurate CSIT results in the significant throughput loss because of residual multiuser interferences. Multiuser interferences are remained since those can not be nulled with beams generated by imperfect CSIT.
Mathematical Description
In a Precoded MIMO BC system with transmitter antennas at AP and a receiver antenna for each user , the input-output relationship can be described as
where is the vector of transmitted symbols, and are the received symbol and noise respectively, is the vector of channel coefficients and is the linear precoding vector.
For the comparison purpose, we describe the mathematical description of MIMO MAC. In a MIMO MAC system with receiver antennas at AP and a transmit antenna for each user where , the input-output relationship can be described as
where is the transmitted symbol for user , and are the vector of received symbols and noise respectively, is the vector of channel coefficients.
See Also
References
- ^ B. C. B. Peel, B. M. Hochwald, and A. L. Swindlehurst (Jan. 2005). "A vector-perturbation technique for near-capacity multiantenna multiuser communication - Part I: channel inversion and regularization". IEEE Trans. Commun. 53: 195–202. doi:10.1109/TCOMM.2004.840638.
{{cite journal}}
: Check date values in:|date=
(help)CS1 maint: multiple names: authors list (link) - ^ N. Jindal (Nov. 2006). "MIMO Broadcast Channels with Finite Rate Feedback". IEEE Trans. Information Theory. 52 (11): 5045–5059. doi:10.1109/TIT.2006.883550.
{{cite journal}}
: Check date values in:|date=
(help)