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Optimization-Based Behavioral Modeling of Mixers for Frequency Comb OFDM Radar Processing

Umut Utku Erdem, Henning Poensgen, Taewon Jeong, Lucas Giroto, Benjamin Nuss, Ibrahim Kagan Aksoyak, Ahmet Cagri Ulusoy, Thomas Zwick

Abstract

This paper presents an optimization-based behavioral model for mixers driven by multi-tone local oscillator (LO) signals, considered specifically for frequency comb orthogonal frequency-division multiplexing radar applications. Unlike traditional models, the proposed approach is designed and tested for multi-tone LO excitations. The model uses polynomial nonlinearities for both intermediate frequency and LO ports, supported by spectrum-domain fitting that selectively emphasizes strong intermodulation products. In addition, a polynomial block is introduced to capture input power-dependent phase nonlinearity. The approach is validated using circuit-level simulations and supported by measurements. Radar processing results show the model replicates distortive effects in simulations. The proposed model enables rapid system-level performance estimations and waveform optimization, replacing computationally expensive circuit-level simulations.

Optimization-Based Behavioral Modeling of Mixers for Frequency Comb OFDM Radar Processing

Abstract

This paper presents an optimization-based behavioral model for mixers driven by multi-tone local oscillator (LO) signals, considered specifically for frequency comb orthogonal frequency-division multiplexing radar applications. Unlike traditional models, the proposed approach is designed and tested for multi-tone LO excitations. The model uses polynomial nonlinearities for both intermediate frequency and LO ports, supported by spectrum-domain fitting that selectively emphasizes strong intermodulation products. In addition, a polynomial block is introduced to capture input power-dependent phase nonlinearity. The approach is validated using circuit-level simulations and supported by measurements. Radar processing results show the model replicates distortive effects in simulations. The proposed model enables rapid system-level performance estimations and waveform optimization, replacing computationally expensive circuit-level simulations.
Paper Structure (5 sections, 8 equations, 6 figures, 2 tables, 1 algorithm)

This paper contains 5 sections, 8 equations, 6 figures, 2 tables, 1 algorithm.

Figures (6)

  • Figure 1: Mixer schematic including bias circuitry. The resistors $R_{1-6}$ function as termination resistors, creating differential virtual-ground nodes at the connections of $R_{1}$ and $R_2$, $R_{3}$ and $R_4$, and at $V_\mathrm{cc}$.
  • Figure 2: Mixer micrograph, total size (including pads): $\qty{900}{\micro\meter}\times\qty{900}{\micro\meter}$, core area $\qty{100}{\micro\meter}\times\qty{180}{\micro\meter}$ highlighted in red.
  • Figure 3: System model of the considered single-mixer multi-LO architecture.
  • Figure 4: ADS () and measurement () comparison for fundamental mix ($\mathrm{f_{IF,1}+f_{LO}}$) in (a) for IM3 ($\mathrm{2f_{IF,1}-f_{IF,2}+f_{LO}}$) (), () in (b). $\mathrm{P}_{\qty{1}{dB}}$ for each (), () is indicated in (a) respectively.
  • Figure 5: AM-AM curve of fundamental mix (${f_\mathrm{IF1}+f_\mathrm{LO1}}$) for ADS () and multibox model () with ($\mathrm{P}_{\qty{1}{dB}}$) for ADS () and multibox () indicated. IM3 (${2f_\mathrm{IF1}-f_\mathrm{IF2}+f_\mathrm{LO1}}$) AM-AM results for ADS () and multibox () are shown in (b). The spectrums for ADS () and multibox model () for 4--14 are shown in (c).
  • ...and 1 more figures