Channel estimation in millimeter wave MIMO systems with one-bit quantization. Mo, J.; Schniter, P.; Prelcic, N.; and Heath, R. In Conference Record - Asilomar Conference on Signals, Systems and Computers, volume 2015-April, 2015.
abstract   bibtex   
? 2014 IEEE.We develop channel estimation agorithms for millimeter wave (mmWave) multiple input multiple output (MIMO) systems with one-bit analog-to-digital converters (ADCs). Since the mmWave MIMO channel is sparse due to the propagation characteristics, the estimation problem is formulated as a one-bit compressed sensing problem. We propose a modified EM algorithm that exploits sparsity and has better performance than the conventional EM algorithm. We also present a second solution using the generalized approximate message passing (GAMP) algorithm to solve this optimization problem. The simulation results show that GAMP can reduce mean squared error in the important low and medium SNR regions.
@inproceedings{
 title = {Channel estimation in millimeter wave MIMO systems with one-bit quantization},
 type = {inproceedings},
 year = {2015},
 identifiers = {[object Object]},
 volume = {2015-April},
 id = {6c83190d-bbbb-3309-b382-d4911597f017},
 created = {2016-09-06T19:47:51.000Z},
 file_attached = {false},
 profile_id = {be62f108-1255-3a2e-9f9e-f751a39b8a03},
 last_modified = {2017-03-24T19:20:02.182Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {? 2014 IEEE.We develop channel estimation agorithms for millimeter wave (mmWave) multiple input multiple output (MIMO) systems with one-bit analog-to-digital converters (ADCs). Since the mmWave MIMO channel is sparse due to the propagation characteristics, the estimation problem is formulated as a one-bit compressed sensing problem. We propose a modified EM algorithm that exploits sparsity and has better performance than the conventional EM algorithm. We also present a second solution using the generalized approximate message passing (GAMP) algorithm to solve this optimization problem. The simulation results show that GAMP can reduce mean squared error in the important low and medium SNR regions.},
 bibtype = {inproceedings},
 author = {Mo, J. and Schniter, P. and Prelcic, N.G. and Heath, R.W.},
 booktitle = {Conference Record - Asilomar Conference on Signals, Systems and Computers}
}
Downloads: 0