Pareto-Optimal Sparse Array Synthesis for Fractal-Inspired UWB-MIMO Antennas: Balancing Hardware Complexity and Radiation Performance in 5G/IoT Systems
Yao-Liang Chung
Signals and Telecommunication Journal, Vol. 15, No. 1, 2026.
DOI: https://doi.org/10.6025/stj/2026/15/1/1-14
Link: https://www.dline.info/stj/fulltext/v15n1/stjv15n1_1.pdf
Abstract:
This study presents a fractal inspired ultra wideband (UWB) multiple input multiple output (MIMO) antenna
design framework integrated with multi objective optimization for next generation 5G, IoT, and wireless
communication systems. Addressing critical challenges of mutual coupling, limited isolation, and excessive
hardware complexity in dense array deployments, we employ Pareto front analysis to systematically
characterize fundamental trade offs in sparse array synthesis. Using the MOPET optimized dataset comprising
3,350 non-dominated solutions derived from aperture sizes spanning 196-2,025 elements, we reconstruct
performance envelopes mapping hardware cost against electromagnetic metrics including sidelobe level (-
27.95 to 3.38 dB), directivity (18.36-34.01 dB), and sparsity ratios (26.7%-62.0%). Our analysis reveals
four key insights: (i) sidelobe suppression exhibits diminishing returns beyond a critical "knee point" in
element count; (ii) beamwidth degradation stems primarily from effective aperture contraction rather than
sparsity alone; (iii) high directivity is preserved in sparse layouts through intelligent peripheral element
placement; and (iv) Pareto optimal configurations achieve performance within a few decibels of dense
arrays while reducing element count by 30-70% [Lorincz, J., 2019, 2022]. These findings validate multiobjective
optimization as a physics grounded methodology for sustainable antenna design, directly addressing
escalating energy consumption in 5G/6G infrastructure [Uluslu, A., 2025; Haxhiraj, E., 2023]. The
reconstructed Pareto fronts serve as quantitative design maps enabling informed selection of array
configurations that optimally balance spectral efficiency, interference suppression, angular resolution, and
implementation cost for resource constrained wireless deployments.