Abstract
In this paper, an efficient sidelobe levels (SLL) reduction and spatial filtering algorithm is proposed for linear one-dimensional arrays. In this algorithm, the sidelobes are beamspace processed simultaneously based on its orientation symmetry to achieve very deep SLL at much lower processing time compared with recent techniques and is denoted by the sidelobes simultaneous reduction (SSR) algorithm. The beamwidth increase due to SLL reduction is found to be the same as that resulting from the Dolph-Chebyshev window but at considerably lower average SLL at the same interelement spacing distance. The convergence of the proposed SSR algorithm can be controlled to guarantee the achievement of the required SLL with almost steady state behavior. On the other hand, the proposed SSR algorithm has been examined for spatial selective sidelobe filtering and has shown the capability to effectively reduce any angular range of the radiation pattern effectively. In addition, the controlled convergence capability of the proposed SSR algorithm allows it to work at any interelement spacing distance, which ranges from tenths to a few wavelength distances, and still provide very low SLL.
Highlights
Humans need communications services, but everything will demand connection to the Internet using the Internet of Things (IoT), which is the main objective of the current communication systems [1,2]
Evolutionary optimization techniques [21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38] can be utilized to find the suitable power pattern for a certain sidelobe levels (SLL). These techniques are inspired by both natural processes and artificial intelligence such as genetic algorithm (GA) [30], particle swarm optimization (PSO) [22], whale optimization algorithm (WOA) invasive weed optimization (IWO), atom search optimization (ASO) [29,30,31], and many other techniques [32,33,34,35,36,37,38]
Tapering window functions can provide very low sidelobe levels [15,16,17,18,19,20], they suffer from lack of flexibility in the pattern synthesis when the resulted SLL are of constant levels with fixed power pattern
Summary
There is a huge demand for maximizing communication data rates to support users with various Internet and communications services. The main role of adaptive antenna arrays is to boost the required signals from certain directions while reducing the unwanted interfering signals This necessitates the requirement of powerful spatial filtering techniques especially for real time communications scenarios which require rapid and adaptive manipulation of the received signals. Several tapering functions provide efficient SLL reduction such as Dolph-Chebyshev, Blackman-Harris, Hamming, Hanning, Gaussian, Kaiser, and many other amplitude windows [15] Most of these tapering windows have a constant power pattern and could not help in scenarios where adaptive SLL control is required. Among these techniques, the Dolph-Chebyshev window provides the narrowest beamwidth, at a certain required SLL where it is known that all window functions result in beamwidth increase when compared with the uniform constant amplitude feeding case. The SSD is limited to arrays of interelement separation distance that are less than one wavelength which in turn limits its application
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
