570 research outputs found
Channel Estimation for mmWave Massive MIMO Based Access and Backhaul in Ultra-Dense Network
Millimeter-wave (mmWave) massive MIMO used for access and backhaul in
ultra-dense network (UDN) has been considered as the promising 5G technique. We
consider such an heterogeneous network (HetNet) that ultra-dense small base
stations (BSs) exploit mmWave massive MIMO for access and backhaul, while
macrocell BS provides the control service with low frequency band. However, the
channel estimation for mmWave massive MIMO can be challenging, since the pilot
overhead to acquire the channels associated with a large number of antennas in
mmWave massive MIMO can be prohibitively high. This paper proposes a structured
compressive sensing (SCS)-based channel estimation scheme, where the angular
sparsity of mmWave channels is exploited to reduce the required pilot overhead.
Specifically, since the path loss for non-line-of-sight paths is much larger
than that for line-of-sight paths, the mmWave massive channels in the angular
domain appear the obvious sparsity. By exploiting such sparsity, the required
pilot overhead only depends on the small number of dominated multipath.
Moreover, the sparsity within the system bandwidth is almost unchanged, which
can be exploited for the further improved performance. Simulation results
demonstrate that the proposed scheme outperforms its counterpart, and it can
approach the performance bound.Comment: 6 pages, 5 figures. Millimeter-wave (mmWave), mmWave massive MIMO,
compressive sensing (CS), hybrid precoding, channel estimation, access,
backhaul, ultra-dense network (UDN), heterogeneous network (HetNet). arXiv
admin note: substantial text overlap with arXiv:1604.03695, IEEE
International Conference on Communications (ICC'16), May 2016, Kuala Lumpur,
Malaysi
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