Name
Title | ||
|---|---|---|
Comparative Study of Neural Network Architectures Applied to Antenna Array Beamforming |
Abstract | ||
|---|---|---|
A comparison of various neural network (NN) architectures is performed in this paper in order to be used as beamformers applied to a linear antenna array composed of 16 microstrip elements. Two recurrent NNs using respectively gated recurrent units and long short-term memory, a convolutional NN, and a feed-forward NN are used here as adaptive beamformers. Three cases are investigated, each one with a different number of incoming signals received by the antenna array, and the performance of each NN structure is evaluated using various metrics. The simulation results demonstrate the effectiveness of the deep learning-based beamformers in real-time calculation of the optimal antenna array weights, while considering ever-changing environments. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/BlackSeaCom54372.2022.9858214 | 2022 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom) |
Keywords | DocType | ISSN |
Adaptive beamforming,beamforming,convolutional neural network (CNN),feed-forward neural network (FFNN),gated recurrent unit (GRU),long short-term memory (LSTM),neural network,recurrent neural network (RNN) | Conference | 2375-8236 |
ISBN | Citations | PageRank |
978-1-6654-9750-3 | 0 | 0.34 |
References | Authors | |
3 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haya Al Kassir | 1 | 0 | 0.34 |
| Zaharias D. Zaharis | 2 | 1 | 1.45 |
| Pavlos I. Lazaridis | 3 | 0 | 0.34 |
| Nikolaos V. Kantartzis | 4 | 0 | 0.68 |
| Traianos V. Yioultsis | 5 | 1 | 2.07 |
| Ioannis P. Chochliouros | 6 | 0 | 0.34 |
| Thomas D. Xenos | 7 | 1 | 0.77 |