Sidelobe level reduction in linear array pattern synthesis using Taylor-MUSIC algorithm for reliable IEEE 802.11 MIMO applications

The problems of synthesizing the beam patterns of the linear antenna array (LAA) and the circular antenna array (CAA) are addressed. First, an optimization problem is formulated for reducing the maximum sidelobe level (SLL) of the beam patterns. Then, the formulated problem is solved by using the invasive weed optimization (IWO) algorithm. Various simulations are performed to evaluate the effectiveness of the IWO algorithm for the synthesis of the beam patterns of the LAA and the CAA. The results show that IWO has a better performance in terms of the accuracy, the convergence rate, and the stability compared with other algorithms for the SLL reductions. Moreover, the electromagnetic simulation results also show that IWO achieves the best performance for the beam pattern synthesis of the antenna arrays in practical conditions.

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An Improved Peak Sidelobe Reduction Method for Subarrayed Beam Scanning

International Journal of Antennas and Propagation ◽

10.1155/2015/464521 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Hang Hu ◽

Xuepeng Luan ◽

Guanglei Zhang ◽

Ke Song ◽

Weihui Liu ◽

...

Keyword(s):

Phased Array ◽

Reduction Method ◽

Ideal Space ◽

Sidelobe Level ◽

Beam Scanning ◽

Array Pattern ◽

Element Number ◽

Sidelobe Level Reduction ◽

Remarkable Reduction ◽

Level Reduction

This paper focused on PSL (peak sidelobe level) reduction for subarrayed beam scanning in phased array radars. The desired GSP (Gaussian Subarray Patterns) are achieved by creating a subarray weighting network. The GSP-based method could reduce PSL of array pattern; compared with the method based on the desired subarray pattern which is defined by ideal space-domain filter, the PSL reduction performance is improved remarkably. Further, based on the concept adopting superelement patterns to approximately express original subarray patterns, the simplified GSP-based method is proposed. So the dimension of each matrix required for creating the weighting network, which was originally the same as the element number, could be reduced to the same as the subarray number. Consequently, we achieve remarkable reduction of the computation burden; simultaneously, the PSL mitigation performance is degraded slightly. Simulation results demonstrate the validity of the introduced methods.

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Sidelobe Level Reduction in Symmetric Dual-Reflector Antennas Using a Small Lens Antenna

Journal of Electromagnetic Waves and Applications ◽

10.1163/156939306779292237 ◽

2006 ◽

Vol 20 (13) ◽

pp. 1807-1816 ◽

Cited By ~ 5

Author(s):

S. Karimkashi ◽

J. Rashed-Mohassel

Keyword(s):

Sidelobe Level ◽

Reflector Antennas ◽

Lens Antenna ◽

Sidelobe Level Reduction ◽

Level Reduction

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Application of the Unit Circle Representation to Pattern Synthesis of Nonuniformly Spaced and Nonuniformly Excited Arrays based on the Teaching Learning Based Optimization

Algerian Journal of Signals and Systems ◽

10.51485/ajss.v4i1.77 ◽

2019 ◽

Vol 4 (1) ◽

pp. 8-17

Author(s):

Abdelmadjid RECIOUI

Keyword(s):

Unit Circle ◽

Antenna Array ◽

Communication Systems ◽

Sidelobe Level ◽

Pattern Synthesis ◽

Linear Arrays ◽

Array Pattern ◽

Teaching Learning Based Optimization ◽

Null Placement ◽

Teaching Learning

Pattern synthesis of Antenna array has gained much attention over the last years as they constitute an important role in the modern communication systems. Unit circle-based techniques such as Schelkunoff null placement method have proved their effectiveness to synthesize uniformly spaced linear arrays. Nonuniformly spaced antenna array pattern synthesis has been investigated and interesting results have been obtained. In this work, the unit circle representation approach is applied to synthesize nonuniformly spaced and nonuniformly excited linear arrays. The objective is to accurately place nulls in the desired directions while achieving the least possible sidelobe level. The problem is cast as an optimization problem that is solved using the Teaching Learning Based Optimization (TLBO). Examples are dealt with to prove the design approach effectiveness and flexibility for modern communication system applications.

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