An Off-Grid Turbo Channel Estimation Algorithm for Millimeter Wave Communications.
The bandwidth shortage has motivated the exploration of the millimeter wave (mmWave) frequency spectrum for future communication networks. To compensate for the severe propagation attenuation in the mmWave band, massive antenna arrays can be adopted at both the transmitter and receiver to provide large array gains via directional beamforming. To achieve such array gains, channel estimation (CE) with high resolution and low latency is of great importance for mmWave communications. However, classic super-resolution subspace CE methods such as multiple signal classification (MUSIC) and estimation of signal parameters via rotation invariant technique (ESPRIT) cannot be applied here due to RF chain constraints. In this paper, an enhanced CE algorithm is developed for the off-grid problem when quantizing the angles of mmWave channel in the spatial domain where off-grid problem refers to the scenario that angles do not lie on the quantization grids with high probability, and it results in power leakage and severe reduction of the CE performance. A new model is first proposed to formulate the off-grid problem. The new model divides the continuously-distributed angle into a quantized discrete grid part, referred to as the integral grid angle, and an offset part, termed fractional off-grid angle. Accordingly, an iterative off-grid turbo CE (IOTCE) algorithm is proposed to renew and upgrade the CE between the integral grid part and the fractional off-grid part under the Turbo principle. By fully exploiting the sparse structure of mmWave channels, the integral grid part is estimated by a soft-decoding based compressed sensing (CS) method called improved turbo compressed channel sensing (ITCCS). It iteratively updates the soft information between the linear minimum mean square error (LMMSE) estimator and the sparsity combiner. Monte Carlo simulations are presented to evaluate the performance of the proposed method, and the results show that it enhances the angle detection resolution greatly.
Wireless Signal Processing and Networks (WSPN) Lab, Key Lab of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Beijing 100876, China. hanlingyi@bupt.edu.cn.
Lingyi
L
Han
Wireless Signal Processing and Networks (WSPN) Lab, Key Lab of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Beijing 100876, China. yxpeng@bupt.edu.cn.
Yuexing
Y
Peng
Huawei Technologies, Sweden AB, Kista 164 40, Sweden. wp_ady@hotmail.com.
Peng
P
Wang
Center of Excellence in Telecommunication, University of Sydney, Sydney NSW 2006, Australia. yonghui.li@sydney.edu.au.
Yonghui
Y
Li
channel estimation; millimeter wave communication; off-grid algorithm
IEEE Trans Signal Process. 2011 Oct 1;59(10):null
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