Author(s)

Norbert Bayang¹, Samuel Eke^1,2, Abdou Njifenjou^1,2

¹Energy, Materials and Methods Research Laboratory, National High Polytechnic School of Douala, Douala, Cameroon.
²National Advanced School of Engineering, University of Yaounde I, Yaounde, Cameroon.

Ahead of the Internet of Things and the emergence of big data, the interest of research is today focused on radio access and the process of optimizing it or increasing its capacity and capacity flow per user. During the process of determining the arrival directions and beam conformation at the antennas, different types of algorithms can be used, namely deterministic algorithms and heuristics. Genetic algorithms are part of heuristics called meta-heuristics. Although effective, observing a relatively long execution time when applied to spectral estimation methods and the subspace method. This case makes its integration into systems very difficult. The simulation of the same algorithms on the antenna array confirms the results but brings more in terms of signal integrity and throughput because it offers more channels. Several resolutions have been undertaken in this article to reduce the processing time of the genetic algorithm: the definition of a new policy of selection of the initial population and exploitation of the mutation procedure. By applying the genetic algorithm to MUSIC and a process of genetic mutation, we can reduce the latency of the linear antenna by about 70%. The running time of the algorithm leads us to explore neural networks.

Array Antenna, DoA, Beam Steering, Genetic Algorithms, Neural Network

Bayang, N. , Eke, S. and Njifenjou, A. (2025) Genetic Algorithms and Neural Network Approach in Determining the Directions of Linear Patch Antenna Arrays. Open Journal of Antennas and Propagation, 13, 1-16. doi: 10.4236/ojapr.2025.131001.