| Full text | |
| Author(s): |
Mohammadzadeh, Saeed
;
Nascimento, Vitor H.
;
de Lamare, Rodrigo C.
;
Hajarolasvadi, Noushin
Total Authors: 4
|
| Document type: | Journal article |
| Source: | IEEE SIGNAL PROCESSING LETTERS; v. 29, p. 5-pg., 2022-01-01. |
| Abstract | |
Robust adaptive beamforming (RAB) plays a vital role in modern communications by ensuring the reception of high-quality signals. This article proposes a deep learning approach to robust adaptive beamforming. In particular, we propose a novel RAB approach where the sample covariance matrix (SCM) is used as the input of a deep 1D Complex-Valued Convolutional Neural Network (CVCNN). The network employs complex convolutional and pooling layers, as well as a Cartesian Scaled Exponential Linear Unit activation function to directly compute the nearly-optimum weight vector through the training process and without prior knowledge about the direction of arrival of the desired signal. This means that reconstruction of the interference plus noise (IPN) covariance matrix is not required. The trained CVCNN accurately computes the nearly-optimum weight vector for data not used during training. The computed weight vector is employed to estimate the signal-to-interference plus noise ratio. Simulations show that the proposed RAB can provide performance close to that of the optimal beamformer. (AU) | |
| FAPESP's process: | 19/19387-9 - Signal processing techniques for beamforming and coding schemes in IoT communication systems |
| Grantee: | Saeed Mohammadzadeh |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 18/12579-7 - ELIOT: enabling technologies for IoT |
| Grantee: | Vitor Heloiz Nascimento |
| Support Opportunities: | Research Projects - Thematic Grants |