Adaptive algorithm for smart antenna system

Boustani, Btissam; Baghdad, Abdennaceur; Sahel, Aïcha; Badri, Abdelmajid; Ballouk, Abdelhakim

doi:10.1109/icmcs.2018.8525905

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Finally, the combined amplitude-and-phase synthesis incorporates both amplitude and phase adjustments. Deterministic methods such as Linearly Constrained Minimum Variance (LCMV) and Minimum Variance Distortionless Response (MVDR) [21] fall into this category, along with hybrid approaches that blend deterministic and non-deterministic methods [22].…”

Section: Introductionmentioning

confidence: 99%

Multibeam Beamforming for Direct Radiating Arrays in Satellite Communications Using Genetic Algorithm

Andrés Vásquez-Peralvo,

Querol,

Ortíz

et al. 2024

IEEE Open J. Commun. Soc.

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Recent advancements in onboard satellite communication have enhanced the capability of dynamically modifying the radiation pattern of a Direct Radiating Array (DRA). This is crucial not only for conventional communication satellites like Geostationary Orbit (GEO) but also for those in lower orbits such as Low Earth Orbit (LEO). Key design factors include the number of beams, beamwidth, Effective Isotropic Radiated Power (EIRP), and Side Lobe Level (SLL) for each beam. However, a challenge arises in multibeam scenarios when trying to simultaneously meet requirements for the aforementioned design factors which are reflected as uneven power distribution. This leads to over-saturation, especially in centrally located antenna elements due to the activation times per beam, commonly referred to as activation instances. In response to this challenge, this paper presents a method to balance the activation instances across antenna elements for each required beam. Our focus is on beams operating at 19 GHz on a CubeSat positioned 500 km above the Earth's surface. We introduce a Genetic Algorithm (GA)-based algorithm to optimize the beamforming coefficients by modulating the amplitude component of the weight matrix for each antenna element. A key constraint of this algorithm is a limit on activation instances per element, which avoids over-saturation in the Radio Frequency (RF) chain. Additionally, the algorithm accommodates beam requirements such as beamwidth, SLL, pointing direction, and total available power. With the previous key design factors, the algorithm will optimize the required genes to address the desired beam characteristics and constraints. We tested the algorithm's effectiveness in three scenarios using an 8×8 DRA patch antenna with circular polarization, arranged in a triangular lattice. The results demonstrate that our algorithm not only meets the required beam pattern specifications but also ensures a uniform activation distribution across the antenna array.INDEX TERMS Antennas, beamforming, direct radiating array, satellite communications.

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Section: Introductionmentioning

confidence: 99%

Multibeam Beamforming for Direct Radiating Arrays in Satellite Communications Using Genetic Algorithm

Andrés Vásquez-Peralvo,

Querol,

Ortíz

et al. 2024

IEEE Open J. Commun. Soc.

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“…Many other algorithms are applied for beamforming in SAS, e.g. those presented in [10], [11]. The minimum variance distortion less response (MVDR) approach to beamforming, which is capable of estimating the weight vectors for adaptive beam steering, is used in [10] to improve cellular network capacity.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…those presented in [10], [11]. The minimum variance distortion less response (MVDR) approach to beamforming, which is capable of estimating the weight vectors for adaptive beam steering, is used in [10] to improve cellular network capacity. The constant modulus algorithm (CMA) is used for blind adaptive beam formation and for SLL reduction in paper [11].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Designing Smart Antennas Using Machine Learning Algorithms

Samantaray,

Das,

Roy

2023

JTIT

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Smart antenna technologies improve spectral efficiency, security, energy efficiency, and overall service quality in cellular networks by utilizing signal processing algorithms that provide radiation beams to users while producing nulls for interferers. In this paper, the performance of such ML solutions as the support vector machine (SVM) algorithm, the artificial neural network (ANN), the ensemble algorithm (EA), and the decision tree (DT) algorithm used for forming the beam of smart antennas are compared. A smart antenna array made up of 10 half-wave dipoles is considered. The ANN method is better than the remaining approaches when it comes to achieving beam and null directions, whereas EA offers better performance in terms of reducing the side lobe level (SLL). The maximum SLL is achieved using EA for all the user directions. The performance of the ANN algorithm in terms of forming the beam of a smart antenna is also compared with that of the variable-step size adaptive algorithm.

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Comparative Study of DOA Estimation and Smart Antenna Beam Forming for Next-Generation Core Networks

Thadikamalla

Amara

2023

Lecture Notes in Networks and Systems

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Adaptive algorithm for smart antenna system

Cited by 5 publications

References 4 publications

Multibeam Beamforming for Direct Radiating Arrays in Satellite Communications Using Genetic Algorithm

Multibeam Beamforming for Direct Radiating Arrays in Satellite Communications Using Genetic Algorithm

Designing Smart Antennas Using Machine Learning Algorithms

Comparative Study of DOA Estimation and Smart Antenna Beam Forming for Next-Generation Core Networks

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