In modern wireless communications, radar, and software-defined radio (SDR) systems, digital downconverter (DDC) technology serves as a core component for high-speed signal processing. Its primary function is to downconvert high-frequency sampled signals to baseband while eliminating high-frequency noise interference through anti-aliasing filtering. FPGAs, with their parallel processing capabilities and reconfigurable nature, serve as an ideal hardware platform for implementing DDC algorithms. This paper focuses on two critical modules—mixer design and anti-aliasing filtering—exploring optimization strategies for FPGA implementations.

Mixer Design: From Theory to Hardware Implementation
The core function of a mixer is to multiply the input signal with a carrier signal generated by a non-oscillator (NCO), achieving spectrum translation. In FPGAs, NCOs typically employ direct digital frequency synthesis (DDS) technology, generating high-precision carriers through phase accumulators and sine lookup tables (LUTs). For instance, in a radar signal processing system, the NCO employs a 32-bit phase accumulator combined with quarter-cycle symmetric storage technology, reducing LUT capacity to one-fourth of traditional designs while achieving a spurious rejection ratio of -75dBc through phase truncation optimization.
Hardware implementation of mixing operations must balance precision and resource efficiency. For 16-bit fixed-point processing, traditional multiplier arrays consume substantial DSP48E1 hard core resources. By employing time-division multiplexing, a single multiplier can process I/Q signals in time-division mode. Combined with pipeline register insertion, this achieves real-time mixing at 200MHz clock frequency on Xilinx Zynq UltraScale+ MPSoC, reducing resource utilization by 40%. Furthermore, for high-frequency signal processing, employing a multiphase filter structure integrates demodulation with anti-aliasing filtering. In an 8K video processing system, this approach reduced system latency from 12μs to 3μs.

Anti-Aliasing Filtering: From Algorithm Optimization to Hardware Architecture
Anti-aliasing filtering serves as the critical defense in DDCs, requiring design to meet stringent passband flatness and stopband attenuation specifications. In FPGA implementations, CIC filters emerge as the preferred downsampling module due to their multiplier-free nature. For instance, in a satellite communication receiver, a 64x downsampling was achieved by cascading five-stage CIC filters. By pre-positioning the decimation operation using the Noble identity, the comb filter order was reduced from 5th to 1st order, cutting resource consumption by 65%. However, the passband attenuation issue of CIC filters requires correction via a compensating filter (PFIR). One design employed a 31th-order PFIR to suppress passband ripple from 4.5dB to 0.1dB, while simultaneously reducing the number of multipliers by 30% through Canonical Signed Digit (CSD) coding.
For high-precision applications, combining a half-band (HB) filter with an FIR filter offers superior performance. The HB filter's characteristic of 50% zero coefficients enables 128x downsampling on Xilinx Virtex-7 FPGA using only 12 DSP48E1 cores. The final-stage 64-order FIR filter employs a Transposed Direct (TD) architecture. Utilizing parallel multiply-accumulate units and a distributed memory architecture, it achieves 80dB stopband attenuation at a 250MHz clock, meeting 5G NR physical layer protocol requirements.

System-Level Optimization: From Module Design to Performance Validation
In an 8K@120fps video processing system, the DDC module processes 12-bit raw data at 7680×4320 resolution with a sampling rate of 1.5GSPS. A three-stage pipeline architecture is employed: The second stage employs HB filters for further 2x downsampling; the third stage uses a 64-stage FIR filter for final shaping. Implemented on the Xilinx RFSoC platform, this solution achieves an end-to-end latency of 8.2ms with power consumption of only 12W, delivering three times the energy efficiency compared to traditional ASIC solutions.
Performance verification requires combining MATLAB simulation with hardware testing. Using a radar signal processing system as an example, the 26MHz intermediate frequency signal generated by MATLAB was processed by the FPGA. The I/Q data captured via ChipScope Pro showed an error of less than 0.5LSB compared to simulation results, validating the algorithm's correctness. In actual deployment, the system maintained a dynamic range exceeding 55dB across a temperature range of -40°C to 85°C, meeting military standard requirements.

Future Outlook
As 5G-A and 6G technologies evolve, DDC algorithms must support higher sampling rates and more complex modulation schemes. Integrating FPGAs with HBM3 memory will overcome bandwidth limitations, while AI-assisted filter design tools can automatically optimize coefficients, reducing development cycles by 60%. From mixers to anti-aliasing filters, FPGAs are continuously driving DDC technology toward higher performance and lower power consumption, laying the hardware foundation for next-generation communication systems.