Paper Info
Title
Adaptive Bayesian Channel Estimation for Millimeter-Wave MIMO Systems with Hybrid Architecture
Abstract
The hybrid multiple-input-multiple-output (MIMO) transceiver architecture is a promising solution to reducing hardware and power cost at millimeter-wave (mmWave) frequencies. However, channel estimation is a major issue for the deployment such system models. Thus, this work shows that by exploiting the sparse scattering nature of the mmWave channel, an efficent channel estimation algorithm can be achieved by utilizing state-of-the-art compressive sensing (CS) techniques. In general previous CS-based channel estimation methods consider an on-grid sparse signal representation problem, however this is not truly realistic to the scenario of mmWave massive MIMO systems. To achieve a more realistic channel estimation algorithm for the mmWave MIMO system, this work considers an off-grid signal model approach, i.e., the directions of sparse channels are not confined on the angular grid for sparse signal formulation. A new adaptive channel estimation method is proposed by using Bayesian CS (BCS) to accurately and efficiently sense channels in terms of an off-grid signal model. A measurement of recovery uncertainty output by BCS is exploited to adaptively design the sensing matrix, thereby improving its estimation performance.
Year
DOI
Venue
2018
10.1109/ACSSC.2018.8645273
2018 52nd Asilomar Conference on Signals, Systems, and Computers
Keywords
Field
DocType
Channel estimation,MIMO communication,Radio frequency,Bayes methods,Signal representation,Entropy,Sensors
Extremely high frequency,Matrix (mathematics),Computer science,Communication channel,MIMO,Radio frequency,Electronic engineering,Grid,Compressed sensing,Bayesian probability
Conference
ISSN
ISBN
Citations 
1058-6393
978-1-5386-9218-9
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Rongrong Qian14913.30
Mathini Sellathurai248357.03
P. Chambers3803.17
Tharmalingam Ratnarajah41035104.27