Multiobjective Beampattern Optimization in Collaborative Beamforming via NSGA-II With Selective Distance

Jayaprakasam, Suhanya; Rahim, S. K. A.; Leow, Chee Yen; Ting, T. O.; Eteng, Akaa Agbaeze

doi:10.1109/tap.2017.2684187

Cited by 68 publications

(36 citation statements)

References 26 publications

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“…According to these studies, mutual coupling will directly affect the beam patterns by, for example, increasing the maximum SLL or changing the directions of the nulls. However, antenna array beam pattern optimization in an ideal environment without considering mutual coupling may provide a general assessment of the effectiveness of beam pattern optimization methods . This implies that if an optimization approach (compared with other methods) achieves better beam pattern performance under ideal conditions, it may be equally effective for beam pattern optimization under realistic conditions.…”

Section: Simulation Resultsmentioning

confidence: 99%

Joint sidelobe suppression and nulls control of large‐scale linear antenna array using particle swarm optimization with global search and population mutation

Zheng

Liu

Sun

et al. 2019

Int J Numerical Modelling

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Large‐scale antenna arrays (LSAAs) are used to achieve satisfactory performance in 5G communications and radar systems. However, sidelobe suppression and nulls control of LSAAs are high‐dimensional nonlinear optimization problems owing to the large number of antenna elements. In this study, we formulate an optimization problem for joint sidelobe suppression and nulls control of large‐scale linear antenna arrays and propose particle swarm optimization with global search and population mutation (PSOGP) to solve this problem. PSOGP introduces global search and population mutation operators into conventional particle swarm optimization to improve performance in terms of convergence rate and accuracy in large solution spaces. Simulation results demonstrate that compared with other methods, the proposed PSOGP has better overall performance in joint sidelobe suppression and nulls control.

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Section: Simulation Resultsmentioning

confidence: 99%

Joint sidelobe suppression and nulls control of large‐scale linear antenna array using particle swarm optimization with global search and population mutation

Zheng

Liu

Sun

et al. 2019

Int J Numerical Modelling

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“…By this way, the search efficiency can be enhanced. The computing method of the standard deviation σ iter in a specific iteration is shown in (8), and the new weeds can be generated by (9).…”

Section: Invasive Weed Optimization Algorithmmentioning

confidence: 99%

“…A directional mainlobe with the low sidelobe level (SLL) of the beam pattern will effectively enhance the communication quality and reduce the interferences [7,8]. Thus, synthesizing the beam pattern of an antenna array is very important.…”

Section: Introductionmentioning

confidence: 99%

An Antenna Array Sidelobe Level Reduction Approach through Invasive Weed Optimization

Sun

Liu

et al. 2018

International Journal of Antennas and Propagation

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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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“…The final objective of these proposals is to save battery power [31]. Finally, it was also proposed that the CB-DCB sends the signal to multiple directions [32]. At the same time, the development of CB-DCB techniques seeks an intelligent choice of emitting nodes to minimize energy consumption [33,34].…”

Section: Beamformingmentioning

confidence: 99%

Feasibility of a Stochastic Collaborative Beamforming for Long Range Communications in Wireless Sensor Networks

et al. 2018

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Wireless Sensor Networks (WSNs) is a group of spatially dispersed autonomous sensor devices, named motes. These motes have a microcontroller, sensors, are powered by AA or AAA batteries, and mainly have the ability to communicate using the IEEE 802.15.4 standard. The motes communicate between them inside the WSN exchanging packets using a multi-hop routing. They use a very low amount of power (below 100 mW). This limits the maximum communication distance between motes within the WSN. Usually, one mote acts as a gateway to other networks and this mote is also called sink or simply Base Station (BS), and the data collected by the sensors of each mote are sent to this mote. The maximum distance between the BS and the nearest mote is below 100 m because of the power limitations of the motes. If the WSN-BS distance is above this boundary, the communication will surely fail. We propose a new technique in order to achieve a long range communication from the WSN, for instance to communicate to a Low Earth Orbit (LEO) satellite. Many proposals in the literature based on Collaborative Beamforming (CB), also known as Distributed or Cooperative Beamforming, for these long range communications are found, however the synchronization of clocks is an almost impossible task given the simplicity and cheapness of the architecture of the motes. To overcome this problem, we propose a new technique, named Stochastic Collaborative Beamforming (SCB), in which we take advantage of the synchronization errors of the clocks. In SCB, it is possible to obtain the adequate time delay that permits the interference or sufficient gain in the direction of the receiver. This gain is obtained from interfering independent signals coming from each mote of the WSN, using a repetition scheme. Although it does not get all the nominal gain that could be obtained in case of a perfect synchronization, it does get a sufficient gain to reach the BS with limited power consumption.Electronics 2018, 7, 417 2 of 19 installed and working, they transmit the information provided by the sensors to the Base Station (BS), also known as sink or gateway, which in turn can supply the information to an external server. WSNs thus allow monitoring of physical or environmental conditions, such as temperature, sound, humidity, pressure, noise, movement or pollutants. WSN were initially developed for military applications, such as battlefield surveillance. They are now used in a multitude of industrial and civil applications [2], in industrial process monitoring and control, machine monitoring, environmental parameter monitoring, traffic control, healthcare, and automation and habitat monitoring in home automation applications. These networks can also be deployed in areas of difficult access or under very adverse conditions, such as mountainous areas, jungles, deserts, mines, skyscrapers, etc. The duration of the batteries determines the duration of its correct operation which can be months or years, depending on the sensors consumption and the energy used by the transmissions ...

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Multiobjective Beampattern Optimization in Collaborative Beamforming via NSGA-II With Selective Distance

Cited by 68 publications

References 26 publications

Joint sidelobe suppression and nulls control of large‐scale linear antenna array using particle swarm optimization with global search and population mutation

Joint sidelobe suppression and nulls control of large‐scale linear antenna array using particle swarm optimization with global search and population mutation

An Antenna Array Sidelobe Level Reduction Approach through Invasive Weed Optimization

Feasibility of a Stochastic Collaborative Beamforming for Long Range Communications in Wireless Sensor Networks

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