Abstract
The efficient and highly accurate channel state information
(CSI) at the base station is essential to achieve the
potential benefits of massive multiple input multiple output
(MIMO) orthogonal frequency division multiplexing (OFDM)
systems, due to limitations of the pilot contamination problem. It
has recently been shown that compressed sensing (CS) techniques
can address the pilot contamination problem, however, the CS based
channel estimation requires prior knowledge of channel
sparsity. To solve this problem, in this paper, an efficient channel
estimation approach based on Bayesian compressed sensing (BCS)
that based on prior knowledge of statistical information about the
channel sparsity is therefore proposed for the uplink of multi-user
massive MIMO systems. Simulation results show that the proposed
method can reconstruct the original channel coefficient effectively
when compared to conventional based channel estimation.
(CSI) at the base station is essential to achieve the
potential benefits of massive multiple input multiple output
(MIMO) orthogonal frequency division multiplexing (OFDM)
systems, due to limitations of the pilot contamination problem. It
has recently been shown that compressed sensing (CS) techniques
can address the pilot contamination problem, however, the CS based
channel estimation requires prior knowledge of channel
sparsity. To solve this problem, in this paper, an efficient channel
estimation approach based on Bayesian compressed sensing (BCS)
that based on prior knowledge of statistical information about the
channel sparsity is therefore proposed for the uplink of multi-user
massive MIMO systems. Simulation results show that the proposed
method can reconstruct the original channel coefficient effectively
when compared to conventional based channel estimation.
| Original language | English |
|---|---|
| Title of host publication | European Conference on Networks and Communications (EUCNC 2016) - EuCNC/EURASIP Student Best Paper Award |
| Number of pages | 5 |
| Publication status | Accepted/In press - 2016 |