Department of Electronics and Communication Engineering, UCRD, Chandigarh University, Punjab, India.
Skill Faculty of Engineering and Technology, Shri Vishwakarma Skill University, Dudhola, Haryana, India.
Sci Rep. 2023 Jul 29;13(1):12308. doi: 10.1038/s41598-023-39509-4.
Linear antenna arrays (LAAs) play a critical role in smart system communication applications such as the Internet of Things (IoT), mobile communication and beamforming. However, minimizing secondary lobes while maintaining a low beamwidth remains challenging. This study presents an enhanced synthesis methodology for LAAs using the Adaptive Naked Mole Rat Algorithm (ANMRA). ANMRA, inspired by mole-rat mating habits, improves exploration and exploitation capabilities for directive LAA applications. The performance of ANMRA is assessed using the CEC 2019 benchmark test functions, a widely adopted standard for statistical evaluation in optimization algorithms. The proposed methodology results are also benchmarked against state-of-the-art algorithms, including the Salp Swarm Algorithm (SSA), Cuckoo Search (CS), Artificial Hummingbird Algorithm (AHOA), Chimp Optimization Algorithm (ChOA), and Naked Mole Rat Algorithm (NMRA). The results demonstrate that ANMRA achieves superior performance among the benchmarked algorithms by successfully minimizing secondary lobes and obtaining a narrow beamwidth. The ANMRA controlled design achieves the lowest Side Lobe Level (SLL) of - 37.08 dB and the smallest beamwidth of 74.68°. The statistical assessment using the benchmark test functions further confirms the effectiveness of ANMRA. By optimizing antenna element magnitude and placement control, ANMRA enables precise primary lobe placement, grating lobe elimination, and high directivity in LAAs. This research contributes to advancing smart system communication technologies, particularly in the context of IoT and beamforming applications, by providing an enhanced synthesis methodology for LAAs that offers improved performance in terms of secondary lobe reduction and beamwidth optimization.
线性天线阵列 (LAA) 在智能系统通信应用中起着至关重要的作用，例如物联网 (IoT)、移动通信和波束成形。然而，在保持低波束宽度的同时最小化旁瓣仍然具有挑战性。本研究提出了一种使用自适应裸鼹鼠算法 (ANMRA) 的 LAA 增强综合方法。ANMRA 受裸鼹鼠交配习惯的启发，提高了定向 LAA 应用的探索和开发能力。使用 CEC 2019 基准测试函数评估 ANMRA 的性能，这是优化算法中统计评估的广泛采用标准。还将提出的方法结果与最先进的算法进行基准测试，包括沙蚕群算法 (SSA)、布谷鸟搜索 (CS)、人工蜂群算法 (AHOA)、黑猩猩优化算法 (ChOA) 和裸鼹鼠算法 (NMRA)。结果表明，ANMRA 通过成功最小化旁瓣并获得较窄的波束宽度，在基准算法中实现了卓越的性能。ANMRA 控制设计实现了最低的旁瓣电平 (SLL) -37.08dB 和最小的波束宽度 74.68°。使用基准测试函数进行的统计评估进一步证实了 ANMRA 的有效性。通过优化天线元件幅度和位置控制，ANMRA 能够实现 LAA 中的精确主瓣放置、栅瓣消除和高指向性。这项研究通过为 LAA 提供增强的综合方法，为智能系统通信技术的发展做出了贡献，特别是在物联网和波束成形应用方面，该方法在减小旁瓣和优化波束宽度方面提供了更好的性能。
