Massive multi-input multi-output (MIMO) technique deploys a number of transmit antennas in base-station (BS) to support large number of users and high data throughput. Since BS needs to acquire channel state information from all transmit antennas, substantial amount of downlink pilot signals is required. In this paper, we suggest a new downlink pilot allocation strategy, inspired by the compressed sensing principle, that reduces the density of the pilot significantly. Key observation in the proposed approach is that the sparse structure of the channel impulse response (CIR) tends to change slower than the OFDM symbol rate. Through computer simulations, we show that the proposed scheme outperforms the conventional compressed sensing methods, achieving the performance bound provided by the Oracle-based Kalman smoother.
|Title of host publication
|2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2015 Oct 27
|Information Theory and Applications Workshop, ITA 2015 - San Diego, United States
Duration: 2015 Feb 1 → 2015 Feb 6
|Information Theory and Applications Workshop, ITA 2015
|15/2/1 → 15/2/6
- Q measurement
- Signal to noise ratio
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems