TY - GEN
T1 - Mobile millimeter wave channel tracking: A bayesian beamforming framework against DOA uncertainty
AU - Yang, Yan
AU - Dang, Shuping
AU - Wen, Miaowen
AU - Mumtaz, Shahid
AU - Guizani, Mohsen
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2020/2/28
Y1 - 2020/2/28
N2 - A Bayesian approach for joint beamforming and tracking is presented, which is robust to uncertain direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. The uncertain or completely unknown DOA is modeled as a discrete random variable with a priori distribution defined over a set of candidate DOAs, which describes the level of uncertainty. The estimation problem of DOA is formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. In particular, we present a motion trajectory-based a priori probability approximation method, which implies a high probability to perform a directional estimate within a specific spatial region. We demonstrate that the proposed approach is robust to DOA uncertainty, and the beam tracking problem can be addressed by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate the effectiveness of the proposed solution.
AB - A Bayesian approach for joint beamforming and tracking is presented, which is robust to uncertain direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. The uncertain or completely unknown DOA is modeled as a discrete random variable with a priori distribution defined over a set of candidate DOAs, which describes the level of uncertainty. The estimation problem of DOA is formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. In particular, we present a motion trajectory-based a priori probability approximation method, which implies a high probability to perform a directional estimate within a specific spatial region. We demonstrate that the proposed approach is robust to DOA uncertainty, and the beam tracking problem can be addressed by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate the effectiveness of the proposed solution.
UR - http://hdl.handle.net/10754/662366
UR - https://ieeexplore.ieee.org/document/9013620/
UR - http://www.scopus.com/inward/record.url?scp=85081970567&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM38437.2019.9013620
DO - 10.1109/GLOBECOM38437.2019.9013620
M3 - Conference contribution
SN - 9781728109626
BT - 2019 IEEE Global Communications Conference (GLOBECOM)
PB - IEEE
ER -