Conventional downlink MU-MIMO techniques require accurate channel state information at the transmitter (CSIT) in order to realize degrees-of-freedom (DoF) gains. In practice, CSIT accuracy is limited by CSI estimation, by the delay between channel estimation and data transmission, and by the time/frequency coherence of the channel. These limitations pose significant challenges in the design and operation of efficient practical MU-MIMO systems. Recently, Maddah-Ali and Tse (MAT) have proposed a scheme achieving non-trivial degrees of freedom (DoF) gains by exploiting only strictly causal CSIT feedback, without relying on channel state prediction. The MAT scheme requires that users are provided also with the past channel states of other users. This requires not only CSI estimation and (causal) CSIT feedback from the receivers to the transmitter, but also CSI dissemination, i.e., providing the channel state of some users to some other users. In this work we focus efficient CSI dissemination for the conventional MAT scheme. We show that efficient CSI dissemination is equivalent to efficient data delivery and therefore it can be accomplished by the same MAT procedure, which delivers the necessary CSI dissemination “degrees of freedom” in the minimum number of channel uses.
[1]
Giuseppe Caire,et al.
On the net DoF comparison between ZF and MAT over time-varying MISO broadcast channels
,
2012,
2012 IEEE International Symposium on Information Theory Proceedings.
[2]
Shlomo Shamai,et al.
Fading Channels: Information-Theoretic and Communication Aspects
,
1998,
IEEE Trans. Inf. Theory.
[3]
Babak Hassibi,et al.
How much training is needed in multiple-antenna wireless links?
,
2003,
IEEE Trans. Inf. Theory.
[4]
Sergio VerdÂ,et al.
Fading Channels: InformationTheoretic and Communications Aspects
,
2000
.
[5]
Mohammad Ali Maddah-Ali.
On the degrees of freedom of the compound MISO broadcast channels with finite states
,
2010,
2010 IEEE International Symposium on Information Theory.
[6]
Shlomo Shamai,et al.
Support recovery with sparsely sampled free random matrices
,
2011,
ISIT.