Abstract
Recently, antenna array radiation pattern synthesis and adaptation has become an essential requirement for most wireless communication systems. Therefore, this paper proposes a new recursive sidelobe level (SLL) reduction algorithm using a sidelobe sequential damping (SSD) approach based on pattern subtraction, where the sidelobes are sequentially reduced to the optimum required levels with near-symmetrical distribution. The proposed SSD algorithm is demonstrated, and its performance is analyzed, including SLL reduction and convergence behavior, mainlobe scanning, processing speed, and performance under mutual coupling effects for uniform linear and planar arrays. In addition, the SSD performance is compared with both conventional tapering windows and optimization techniques, where the simulation results show that the proposed SSD approach has superior maximum and average SLL performances and lower processing speeds. In addition, the SSD is found to have a constant SLL convergence profile that is independent on the array size, working effectively on any uniform array geometry with interelement spacing less than one wavelength, and deep SLL levels of less than −70 dB can be achieved relative to the mainlobe level, especially for symmetrical arrays.
Highlights
Many comparisons have been conducted between these optimization algorithms, such as in [27,28,29], where the atom search optimization (ASO), whale optimization algorithm (WOA), flower pollination algorithm (FPA), and bat algorithm (BA) provided much lower sidelobe level (SLL) for the same array size compared with the biogeography-based optimization (BBO), particle swarm optimization (PSO), invasive weed optimization (IWO), cuckoo search algorithm (CS), and firefly algorithm (FA) techniques where, for a 16 element uniform linear array (ULA), the lowest SLL of −41 dB relative to the mainlobe level was provided by the ASO algorithm [27], while the highest SLL of −17 dB was obtained using PSO [27,29]
To provide a general and array configuration-independent SLL reduction technique with a faster convergence time and adaptive beampattern generation, this paper proposes a recursive, adaptable SLL reduction and control technique by sidelobe sequential damping (SSD), which is performed successively on the local maximum SLL in the radiation pattern
In this paper, spatial filtering has been achieved by a sequential sidelobe damping algorithm (SSD), where the sidelobes are sequentially damped for sidelobe level (SLL) reduction
Summary
Antenna arrays and beamforming systems have become essential parts for most recent wireless communication systems and play important roles in the provision of high capacities and data rates. The work done in [32] to improve the SLL level optimized the element locations, which limited the flexibility of application in different communication scenarios Other optimization techniques, such as the differential search algorithm [33], Taguchi method [34], and backtracking search optimization [35], have been applied to linear array optimization. Tapering window techniques are straightforward for all ULAs, while they can be applied without an optimum solution for planar or concentric ring arrays such as in [13], where the design of the Dolph-Chebyshev window to provide a −80 dB SLL for a linear array provided −40 dB in a uniform concentric ring array These fast conventional tapering windows produce constant SLL profiles for a fixed array size and suffer from the lack of adaptation
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