Suppressing Sidelobe Level of the Planar Antenna Array in Wireless Power Transmission

Abstract

The power pattern synthesis, as well as the sidelobe level (SLL) suppression of the planar antenna arrays for the wireless power transmission is investigated. An improved chicken swarm optimization (ICSO) algorithm is proposed for reducing the maximum SLL of the power pattern. ICSO introduces the global search, the local search, and the duplicate solution removing operators to enhance the accuracy and the convergence rate of the conventional CSO algorithm, thereby making it suitable for the power pattern optimizations. Simulations are conducted to verify the performance of the proposed algorithm, and the results show that ICSO obtains the lowest maximum SLL compared with other algorithms. Moreover, the power pattern performances of different algorithms are tested by the electromagnetic simulations, and the results show that the proposed ICSO has the best optimization ability. INDEX TERMS Wireless power transmission, power pattern, energy beamforming, sidelobe level, chicken swarm optimization. III. SYSTEM MODEL, ARRAY FACTOR AND PROBLEM DESCRIPTION A. SYSTEM MODEL AND ARRAY FACTOR GUOJUN SHEN received the B.S. degree in computer science from Jilin University, China, in 2015, where he is currently pursuing the M.S. degree in computer science. His research interests include wireless communication and optimizations. YANHENG LIU received the M.Sc. and Ph.D. degrees in computer science from Jilin University, China, where he is currently a Professor. His primary research interests include network security, network management, mobile computing network theory, and applications. GENG SUN (S'17) received the B.S. degree in communication engineering from Dalian Polytechnic University, in 2007, and the Ph.D. degree in computer science and technology from Jilin University, in 2018. He was a Visiting Researcher with the School of Electrical and Computer Engineering, Georgia Institute of Technology, USA. He currently holds a Postdoctoral position with Jilin University. His research interests include wireless sensor networks, antenna arrays, collaborative beamforming, and optimizations. TINGTING ZHENG received the B.S. degree in computer science from Jilin University, China, in 2017, where she is currently pursuing the Ph.D. degree in computer science. Her research interests include wireless communications and optimizations. XU ZHOU received the M.S. degree from Northeast Normal University, in 2013, and the Ph.D. degree from Jilin University, in 2016, where she currently holds a Postdoctoral position. Her research interests include intelligent algorithms, data mining, and complex network analysis. AIMIN WANG received the Ph.D. degree in communication and information system from Jilin University, where he is currently an Associate Professor. His research interests include wireless sensor networks and QoS for multimedia transmission.

Synthesis of Sparse Arrays Based On CIGA (Convex Improved Genetic Algorithm)

“…9 (a) clearly shows the radiation pattern of the planar antenna arrays obtained by the CIGA, for a more intuitive view of the PSLL of the array pattern, power mask is drawn at the PSLL. It is clearly shows that the CIGA has achieved the best PSLL of 13.30 -dB, which is better than that of improved chicken swarm optimization [13]. Fig.…”

“…In Fig. 10 (a), the results show that the PSLL is 14.65 -dB, which is lower than that of the improved chicken swarm optimization proposed in [13]. Fig.…”

“…In the past few years, many synthesis methods of antenna arrays have been developed. The methods of antenna arrays synthesis based on stochastic optimization algorithm are proved effectively, such as genetic algorithm (GA) [6,7], differential evolution (DE) [8], particle swarm optimization (PSO) [9], invasive weed optimization [10] and cuckoo search algorithm [11], dynamic parameters differential evolution algorithm (DPDE) [12], improved chicken swarm optimization [13]. Such methods are effective for optimizing amplitude and phase of the excitation coefficients of the equally spaced arrays or optimizing the element positions with the uniform excitation coefficients, but it cannot achieve the truly minimum PSLL when both the excitation coefficients and the element positions are…”

Energy-efficient collaborative transmission algorithm based on potential game theory for beamforming

“…In wireless power transmission (WPT), beam collection efficiency is considered to improve the performance of the power beam. In Shen et al, 31 an improved chicken swarm optimization (ICSO) algorithm is proposed for SLL suppression in the planar antenna arrays (PAA). The ICSO algorithm reduces the maximum SLL of the power patterns by selecting the appropriate excitation current.…”

“…According to the experimental data, different combinations of cooperative nodes lead to different experimental results, and the spacing of the cooperative nodes affects the beamforming of the virtual node antenna array apparently. The distance between the BS and the source node is 1000 m, and the number of cooperative nodes is 21, which satisfies equation (31). According to the above two node selection algorithms, 21 nodes are selected from the neighbor nodes set to form a beam pattern.…”